# papers

title: Zero-shot RGB-D Point Cloud Registration with Pre-trained Large Vision Model
authors: Haobo Jiang, Jin Xie, Jian Yang, Liang Yu, Jianmin Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16943-16952
affiliations: ["Nanyang Technological University", "Alibaba Group", "Nankai University", "Nanjing University"]
cities: ["Singapore", "Hangzhou", "Tianjin", "Nanjing"]
countries: ["Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Zero-shot_RGB-D_Point_Cloud_Registration_with_Pre-trained_Large_Vision_Model_CVPR_2025_paper.html

abstract:
This paper introduces ZeroMatch, a novel zero-shot RGB-D point cloud registration framework, aimed at achieving robust 3D matching on unseen data without any task-specific training. Our core idea is to utilize the powerful zero-shot image representation of Stable Diffusion, achieved through extensive pre-training on large-scale data, to enhance point-cloud geometric descriptors for robust matching. Specifically, we combine the handcrafted geometric descriptor FPFH with Stable-Diffusion features to create point descriptors that are both locally and contextually aware, enabling reliable RGB-D registration with zero-shot capability. This approach is based on our observation that Stable-Diffusion features effectively encode discriminative global contextual cues, naturally alleviating the feature ambiguity that FPFH often encounters in scenes with repetitive patterns or low overlap. To further enhance cross-view consistency of Stable-Diffusion features for improved matching, we propose a coupled-image input mode that concatenates the source and target images into a single input, replacing the original single-image mode. This design achieves both inter-image and prompt-to-image consistency attentions, facilitating robust cross-view feature interaction and alignment. Finally, we leverage feature nearest neighbors to construct putative correspondences for hypothesize-and-verify transformation estimation.  Extensive experiments on 3DMatch, ScanNet, and ScanLoNet verify the excellent zero-shot matching ability of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Zero-shot_RGB-D_Point_Cloud_Registration_with_Pre-trained_Large_Vision_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Zero-shot_RGB-D_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Haobo and Xie, Jin and Yang, Jian and Yu, Liang and Zheng, Jianmin},
    title     = {Zero-shot RGB-D Point Cloud Registration with Pre-trained Large Vision Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16943-16952}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]


title: Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration
authors: Jiani Ni, He Zhao, Jintong Gao, Dandan Guo, Hongyuan Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30712-30721
cities: ["Changchun", "Sydney", "Shenzhen"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_Balancing_Two_Classifiers_via_A_Simplex_ETF_Structure_for_Model_CVPR_2025_paper.html

abstract:
In recent years, deep neural networks (DNNs) have demonstrated state-of-the-art performance across various domains. However, despite their success, they often face calibration issues, particularly in safety-critical applications such as autonomous driving and healthcare, where unreliable predictions can have serious consequences. Recent research starts to improve model calibration from the view of classifier. However, the explore about designing the classifier to solve the model calibration problem is insufficient.  Let alone most of existing methods ignore the calibration errors arising from underconfidence. In this work, we propose a novel method by Balancing learnable and ETF classifiers to solve the overconfidence or underconfidence problem for model Calibration named BalCAL. By introducing a confidence-tunable module and a dynamic adjustment method, we ensure better alignment between model confidence and its true accuracy. Extensive experimental validation shows that ours significantly improves model calibration performance while maintaining high predictive accuracy, outperforming existing techniques. This provides a novel solution to the calibration challenges commonly encountered in deep learning. Our code is available at https://github.com/JianiNi/BalCAL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_Balancing_Two_Classifiers_via_A_Simplex_ETF_Structure_for_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ni_Balancing_Two_Classifiers_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Jiani and Zhao, He and Gao, Jintong and Guo, Dandan and Zha, Hongyuan},
    title     = {Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30712-30721}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "CSIRO\u2019s Data61", "city": "Sydney", "country": "Australia"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Jilin University", "CSIRO\u2019s Data61", "The Chinese University of Hong Kong, Shenzhen"]


title: DistinctAD: Distinctive Audio Description Generation in Contexts
authors: Bo Fang, Wenhao Wu, Qiangqiang Wu, Yuxin Song, Antoni B. Chan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13571-13581
cities: ["Hong Kong", "Beijing", "Sydney"]
countries: ["Hong Kong", "China", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_DistinctAD_Distinctive_Audio_Description_Generation_in_Contexts_CVPR_2025_paper.html

abstract:
Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automatic generation of ADs remains challenging due to: i) the domain gap between movie-AD data and existing data used to train vision-language models, and ii) the issue of contextual redundancy arising from highly similar neighboring visual clips in a long movie. In this work, we propose **DistinctAD**, a novel two-stage framework for generating ADs that emphasize distinctiveness to produce better narratives. To address the domain gap, we introduce a CLIP-AD adaptation strategy that does not require additional AD corpora, enabling more effective alignment between movie and AD modalities at both global and fine-grained levels. In Stage-II, DistinctAD incorporates two key innovations: (i) a Contextual Expectation-Maximization Attention (EMA) module that reduces redundancy by extracting common bases from consecutive video clips, and (ii) an explicit distinctive word prediction loss that filters out repeated words in the context, ensuring the prediction of unique terms specific to the current AD. Comprehensive evaluations on MAD-Eval, CMD-AD, and TV-AD benchmarks demonstrate the superiority of DistinctAD, with the model consistently outperforming baselines, particularly in Recall@k/N, highlighting its effectiveness in producing high-quality, distinctive ADs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_DistinctAD_Distinctive_Audio_Description_Generation_in_Contexts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_DistinctAD_Distinctive_Audio_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Bo and Wu, Wenhao and Wu, Qiangqiang and Song, Yuxin and Chan, Antoni B.},
    title     = {DistinctAD: Distinctive Audio Description Generation in Contexts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13571-13581}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}]

affiliations:
["Department of Computer Science, City University of Hong Kong", "Baidu Inc.", "The University of Sydney"]


authors: Junjie Zhou, Shouju Wang, Yuxia Tang, Qi Zhu, Daoqiang Zhang, Wei Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30886-30895
cities: ["Nanjing", "Nanjing", "Nanjing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_DAMM-Diffusion_Learning_Divergence-Aware_Multi-Modal_Diffusion_Model_for_Nanoparticles_Distribution_Prediction_CVPR_2025_paper.html

title:
DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction

abstract:
The prediction of nanoparticles (NPs) distribution is crucial for the diagnosis and treatment of tumors. Recent studies indicate that the heterogeneity of tumor microenvironment (TME) highly affects the distribution of NPs across tumors. Hence, it has become a research hotspot to generate the NPs distribution by the aid of multi-modal TME components. However, the distribution divergence among multi-modal TME components may cause side effects i.e., the best uni-modal model may outperform the joint generative model. To address the above issues, we propose a \Divergence-Aware Multi-Modal Diffusion model (i.e., DAMM-Diffusion) to adaptively generate the prediction results from uni-modal and multi-modal branches in a unified network. In detail, the uni-modal branch is composed of the U-Net architecture while the multi-modal branch extends it by introducing two novel fusion modules i.e., Multi-Modal Fusion Module (MMFM) and Uncertainty-Aware Fusion Module (UAFM). Specifically, the MMFM is proposed to fuse features from multiple modalities, while the UAFM module is introduced to learn the uncertainty map for cross-attention computation. Following the individual prediction results from each branch, the Divergence-Aware Multi-Modal Predictor (DAMMP) module is proposed to assess the consistency of multi-modal data with the uncertainty map, which determines whether the final prediction results come from  multi-modal or uni-modal predictions. We predict the NPs distribution given the TME components of tumor vessels and cell nuclei, and the experimental results show that DAMM-Diffusion can generate the distribution of NPs with higher accuracy than the comparing methods. Additional results on the multi-modal brain image synthesis task further validate the effectiveness of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_DAMM-Diffusion_Learning_Divergence-Aware_Multi-Modal_Diffusion_Model_for_Nanoparticles_Distribution_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_DAMM-Diffusion_Learning_Divergence-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Junjie and Wang, Shouju and Tang, Yuxia and Zhu, Qi and Zhang, Daoqiang and Shao, Wei},
    title     = {DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30886-30895}
}

raw_affiliation:
[{"affiliation": "The College of Arti\ufb01cial Intelligence, Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education", "city": "Nanjing", "country": "China"}, {"affiliation": "Department of Radiology, Nanjing Medical University", "city": "Nanjing", "country": "China"}]

affiliations:
["The College of Arti\ufb01cial Intelligence, Nanjing University of Aeronautics and Astronautics", "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education", "Department of Radiology, Nanjing Medical University"]


title: Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
authors: Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li, Farzan Farnia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8269-8278
raw_affiliation: [{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]
affiliations: ["The Chinese University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Unveiling_Differences_in_Generative_Models_A_Scalable_Differential_Clustering_Approach_CVPR_2025_paper.html

abstract:
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been proposed in the literature to rank different generative models, score-based evaluation and ranking do not reveal the nuanced differences between the generative models in producing different sample types. In this work, we propose solving a differential clustering problem to detect sample types generated differently by two generative models. To solve the differential clustering problem, we develop a spectral method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to large-scale computer vision datasets and generative modeling frameworks. Our numerical results suggest the scalability of the developed Fourier-based method in highlighting the sample types produced with different frequencies by generative models. The project code is available at https://github.com/buyeah1109/FINC

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Unveiling_Differences_in_Generative_Models_A_Scalable_Differential_Clustering_Approach_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Unveiling_Differences_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jingwei and Jalali, Mohammad and Li, Cheuk Ting and Farnia, Farzan},
    title     = {Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8269-8278}
}


authors: Tianyu Huai, Jie Zhou, Xingjiao Wu, Qin Chen, Qingchun Bai, Ze Zhou, Liang He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19608-19617
cities: ["Shanghai", "Shanghai", "Zhejiang"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huai_CL-MoE_Enhancing_Multimodal_Large_Language_Model_with_Dual_Momentum_Mixture-of-Experts_CVPR_2025_paper.html

title:
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering

abstract:
Multimodal large language models (MLLMs) have garnered widespread attention from researchers due to their remarkable understanding and generation capabilities in visual language tasks (e.g., visual question answering). However, the rapid pace of knowledge updates in the real world makes offline training of MLLMs costly, and when faced with non-stationary data streams, MLLMs suffer from catastrophic forgetting during learning. In this paper, we propose an MLLMs-based dual momentum Mixture-of-Experts (\texttt CL-MoE ) framework for continual visual question answering. We integrate MLLMs with continual learning to utilize the rich commonsense knowledge in LLMs.We introduce a Dual-Router MoE (RMoE) to select the global and local experts using task-level and instance-level routers, to robustly assign weights to the experts most appropriate for the task. Then, we design a dynamic Momentum MoE (MMoE) to update the parameters of experts dynamically based on the relationships between the experts and tasks, so that the model can absorb new knowledge while maintaining existing knowledge. The extensive experimental results indicate that our method achieves state-of-the-art performance on 10 VQA tasks, proving the effectiveness of our approach. The codes and weights will be released on GitHub.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huai_CL-MoE_Enhancing_Multimodal_Large_Language_Model_with_Dual_Momentum_Mixture-of-Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huai_CL-MoE_Enhancing_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huai_2025_CVPR,
    author    = {Huai, Tianyu and Zhou, Jie and Wu, Xingjiao and Chen, Qin and Bai, Qingchun and Zhou, Ze and He, Liang},
    title     = {CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19608-19617}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Open University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "ZhuQingTing Data Technology (Zhejiang) Co., Ltd.", "city": "Zhejiang", "country": "China"}]

affiliations:
["School of Computer Science and Technology, East China Normal University", "Shanghai Open University, Shanghai, China", "ZhuQingTing Data Technology (Zhejiang) Co., Ltd."]


title: Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9804-9815
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qorbani_Semantic_Library_Adaptation_LoRA_Retrieval_and_Fusion_for_Open-Vocabulary_Semantic_CVPR_2025_paper.html

authors:
Reza Qorbani, Gianluca Villani, Theodoros Panagiotakopoulos, Marc Botet Colomer, Linus Härenstam-Nielsen, Mattia Segu, Pier Luigi Dovesi, Jussi Karlgren, Daniel Cremers, Federico Tombari, Matteo Poggi

abstract:
Open-vocabulary semantic segmentation models associate vision and text to label pixels from an undefined set of classes using textual queries, providing versatile performance on novel datasets. However, large shifts between training and test domains degrade their performance, requiring fine-tuning for effective real-world applications. We introduce Semantic Library Adaptation (SemLA), a novel framework for training-free, test-time domain adaptation. SemLA leverages a library of LoRA-based adapters indexed with CLIP embeddings, dynamically merging the most relevant adapters based on proximity to the target domain in the embedding space. This approach constructs an ad-hoc model tailored to each specific input without additional training. Our method scales efficiently, enhances explainability by tracking adapter contributions, and inherently protects data privacy, making it ideal for sensitive applications. Comprehensive experiments on a 20-domain benchmark built over 10 standard datasets demonstrate SemLA's superior adaptability and performance across diverse settings, establishing a new standard in domain adaptation for open-vocabulary semantic segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qorbani_Semantic_Library_Adaptation_LoRA_Retrieval_and_Fusion_for_Open-Vocabulary_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qorbani_Semantic_Library_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qorbani_2025_CVPR,
    author    = {Qorbani, Reza and Villani, Gianluca and Panagiotakopoulos, Theodoros and Colomer, Marc Botet and H\"arenstam-Nielsen, Linus and Segu, Mattia and Dovesi, Pier Luigi and Karlgren, Jussi and Cremers, Daniel and Tombari, Federico and Poggi, Matteo},
    title     = {Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9804-9815}
}

raw_affiliation:
[{"affiliation": "The Good AI Lab", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "KTH", "city": "Stockholm", "country": "Sweden"}, {"affiliation": "AMD Silo AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "King", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}, {"affiliation": "Google", "city": "Unknown", "country": "Unknown"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of Bologna", "city": "Bologna", "country": "Italy"}]

affiliations:
["The Good AI Lab", "University of Toronto", "KTH", "AMD Silo AI", "King", "Technical University of Munich", "Munich Center for Machine Learning", "Google", "ETH Zurich", "University of Bologna"]

cities:
["Unknown", "Toronto", "Stockholm", "Unknown", "Unknown", "Munich", "Munich", "Unknown", "Zurich", "Bologna"]

countries:
["Unknown", "Canada", "Sweden", "Unknown", "Unknown", "Germany", "Germany", "Unknown", "Switzerland", "Italy"]


title: PhyS-EdiT: Physics-aware Semantic Image Editing with Text Description
authors: Ziqi Cai, Shuchen Weng, Yifei Xia, Boxin Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7867-7876
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_PhyS-EdiT_Physics-aware_Semantic_Image_Editing_with_Text_Description_CVPR_2025_paper.html

abstract:
Achieving joint control over material properties, lighting, and high-level semantics in images is essential for applications in digital media, advertising, and interactive design. Existing methods often isolate these properties, lacking a cohesive approach to manipulating materials, lighting, and semantics simultaneously. We introduce PhyS-EdiT, a novel diffusion-based model that enables precise control over four critical material properties: roughness, metallicity, albedo, and transparency while integrating lighting and semantic adjustments within a single framework. To facilitate this disentangled control, we present PR-TIPS, a large and diverse synthetic dataset designed to improve the disentanglement of material and lighting effects. PhyS-EdiT incorporates a dual-network architecture and robust training strategies to balance low-level physical realism with high-level semantic coherence, supporting localized and continuous property adjustments. Extensive experiments demonstrate the superiority of PhyS-EdiT in editing both synthetic and real-world images, achieving state-of-the-art performance on material, lighting, and semantic editing tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_PhyS-EdiT_Physics-aware_Semantic_Image_Editing_with_Text_Description_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_PhyS-EdiT_Physics-aware_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Ziqi and Weng, Shuchen and Xia, Yifei and Shi, Boxin},
    title     = {PhyS-EdiT: Physics-aware Semantic Image Editing with Text Description},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7867-7876}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Academy of Artificial Intelligence", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "Beijing Academy of Artificial Intelligence"]


authors: Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, Munchurl Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23069-23079
affiliations: ["KAIST", "KNU"]
cities: ["Daejeon", "Daegu"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_U-Know-DiffPAN_An_Uncertainty-aware_Knowledge_Distillation_Diffusion_Framework_with_Details_Enhancement_CVPR_2025_paper.html

title:
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening

abstract:
Conventional methods for PAN-sharpening often struggle to restore fine details due to limitations in leveraging high-frequency information. Moreover, diffusion-based approaches lack sufficient conditioning to fully utilize Panchromatic (PAN) images and low-resolution multispectral (LRMS) inputs effectively. To address these challenges, we propose an uncertainty-aware knowledge distillation diffusion framework with details enhancement for PAN-sharpening, called U-Know-DiffPAN. The U-Know-DiffPAN incorporates uncertainty-aware knowledge distillation for effective transfer of feature details from our teacher model to a student one. The teacher model in our U-Know-DiffPAN captures frequency details through freqeuncy selective attention, facilitating accurate reverse process learning. By conditioning the encoder on compact vector representations of PAN and LRMS and the decoder on Wavelet transforms, we enable rich frequency utilization. So, the high-capacity teacher model distills frequency-rich features into a lightweight student model aided by an uncertainty map. From this, the teacher model can guide the student model to focus on difficult image regions for PAN-sharpening via the usage of the uncertainty map. Extensive experiments on diverse datasets demonstrate the robustness and superior performance of our U-Know-DiffPAN over very recent state-of-the-art PAN-sharpening methods. The project page is available at https://kaist-viclab.github.io/U-Know-DiffPAN-site/  https://kaist-viclab.github.io/U-Know-DiffPAN-site

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_U-Know-DiffPAN_An_Uncertainty-aware_Knowledge_Distillation_Diffusion_Framework_with_Details_Enhancement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_U-Know-DiffPAN_An_Uncertainty-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sungpyo and Do, Jeonghyeok and Lee, Jaehyup and Kim, Munchurl},
    title     = {U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23069-23079}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "KNU", "city": "Daegu", "country": "South Korea"}]


title: SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1570-1580
affiliations: ["Waymo LLC", "UT Austin"]
cities: ["Mountain View", "Austin"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_SceneDiffuser_City-Scale_Traffic_Simulation_via_a_Generative_World_Model_CVPR_2025_paper.html

authors:
Shuhan Tan, John Lambert, Hong Jeon, Sakshum Kulshrestha, Yijing Bai, Jing Luo, Dragomir Anguelov, Mingxing Tan, Chiyu Max Jiang

abstract:
The goal of traffic simulation is to augment a potentially limited amount of manually-driven miles that is available for testing and validation, with a much larger amount of simulated synthetic miles. The culmination of this vision would be a generative simulated city, where given a map of the city and an autonomous vehicle (AV) software stack, the simulator can seamlessly simulate the trip from point A to point B by populating the city around the AV and controlling all aspects of the scene, from animating the dynamic agents (e.g., vehicles, pedestrians) to controlling the traffic light states. We refer to this vision as CitySim, which requires an agglomeration of simulation technologies: scene generation to populate the initial scene, agent behavior modeling to animate the scene, occlusion reasoning, dynamic scene generation to seamlessly spawn and remove agents, and environment simulation for factors such as traffic lights. While some key technologies have been separately studied in various works, others such as dynamic scene generation and environment simulation have received less attention in the research community. We propose SceneDiffuser++, the first end-to-end generative world model trained on a single loss function capable of point A-to-B simulation on a city scale integrating all the requirements above. We demonstrate the city-scale traffic simulation capability of SceneDiffuser++ and study its superior realism under long simulation conditions.We evaluate the simulation quality on an augmented version of the Waymo Open Motion Dataset (WOMD) with larger map regions to support trip-level simulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_SceneDiffuser_City-Scale_Traffic_Simulation_via_a_Generative_World_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_SceneDiffuser_City-Scale_Traffic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Shuhan and Lambert, John and Jeon, Hong and Kulshrestha, Sakshum and Bai, Yijing and Luo, Jing and Anguelov, Dragomir and Tan, Mingxing and Jiang, Chiyu Max},
    title     = {SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1570-1580}
}

raw_affiliation:
[{"affiliation": "Waymo LLC", "city": "Mountain View", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}]


title: Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization
authors: Maxime Pietrantoni, Gabriela Csurka, Torsten Sattler
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1082-1092
cities: ["Prague", "Prague", "Unknown"]
countries: ["Czech Republic", "Czech Republic", "Europe"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pietrantoni_Gaussian_Splatting_Feature_Fields_for_Privacy-Preserving_Visual_Localization_CVPR_2025_paper.html

abstract:
Visual localization is the task of estimating a camera pose in a known environment. In this paper, we utilize 3D Gaussian Splatting (3DGS)-based representations for accurate and privacy-preserving visual localization. We propose Gaussian Splatting Feature Fields (GSFFs), a scene representation for visual localization that combines an explicit geometry model (3DGS) with an implicit feature field. We leverage the dense geometric information and differentiable rasterization algorithm from 3DGS to learn robust feature representations grounded in 3D. In particular, we align a 3D scale-aware feature field and a 2D feature encoder in a common embedding space through a contrastive framework. Using a 3D structure-informed clustering procedure, we further regularize the representation learning and seamlessly convert the features to segmentations, which can be used for privacy-preserving visual localization. Pose refinement, which involves aligning either feature maps or segmentations from a query image with those rendered from the GSFFs scene representation, is used to achieve localization. The resulting privacy- and non-privacy-preserving localization pipelines, evaluated on multiple real-world datasets, show state-of-the-art  performances.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pietrantoni_Gaussian_Splatting_Feature_Fields_for_Privacy-Preserving_Visual_Localization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pietrantoni_Gaussian_Splatting_Feature_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Pietrantoni_2025_CVPR,
    author    = {Pietrantoni, Maxime and Csurka, Gabriela and Sattler, Torsten},
    title     = {Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1082-1092}
}

raw_affiliation:
[{"affiliation": "Faculty of Electrical Engineering, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "NA VER LABS Europe", "city": "Unknown", "country": "Europe"}]

affiliations:
["Faculty of Electrical Engineering, Czech Technical University in Prague", "Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague", "NA VER LABS Europe"]


title: Point Cloud Upsampling Using Conditional Diffusion Module with Adaptive Noise Suppression
authors: Boqian Zhang, Shen Yang, Hao Chen, Chao Yang, Jing Jia, Guang Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16987-16996
raw_affiliation: [{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}]
affiliations: ["Xidian University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Point_Cloud_Upsampling_Using_Conditional_Diffusion_Module_with_Adaptive_Noise_CVPR_2025_paper.html

abstract:
Point cloud upsampling can improve the quality of the initial point cloud, significantly enhancing the performance of downstream tasks such as classification and segmentation. Existing methods mostly focus on generating the geometric details of point clouds, neglecting noise suppression. To address this, we propose a novel network based on a conditional diffusion model, incorporating the Adaptive Noise Suppression (ANS) module, which we refer to as PDANS. The ANS module assigns weights to each point and determines the removal strategy based on these weights, reducing the impact of noisy points on the sampling process. The module first selects the neighborhood set for each point in the point cloud and performs a weighted sum between the point and its neighbors. It then adjusts the removal points based on the weighted sum, effectively mitigating the bias caused by outliers. We introduce the TreeTrans (TT) module to capture more correlated feature information. This module learns the interaction between high-level and low-level features, resulting in a more comprehensive and refined feature representation. Our results on several widely used benchmark datasets demonstrate that PDANS exhibits exceptional robustness in noisy point cloud processing and outperforms current state-of-the-art(SOTA) methods in terms of performance. Code is available at https://github.com/Baty2023/PDANS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Point_Cloud_Upsampling_Using_Conditional_Diffusion_Module_with_Adaptive_Noise_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Boqian and Yang, Shen and Chen, Hao and Yang, Chao and Jia, Jing and Jiang, Guang},
    title     = {Point Cloud Upsampling Using Conditional Diffusion Module with Adaptive Noise Suppression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16987-16996}
}


authors: Michele Mazzamuto, Antonino Furnari, Yoichi Sato, Giovanni Maria Farinella
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8310-8320
affiliations: ["University of Catania", "The University of Tokyo"]
cities: ["Catania", "Tokyo"]
countries: ["Italy", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mazzamuto_Gazing_Into_Missteps_Leveraging_Eye-Gaze_for_Unsupervised_Mistake_Detection_in_CVPR_2025_paper.html

title:
Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities

abstract:
We address the challenge of unsupervised mistake detection in egocentric video of skilled human activities through the analysis of gaze signals. While traditional methods rely on manually labeled mistakes, our approach does not require mistake annotations, hence overcoming the need of domain-specific labeled data. Based on the observation that eye movements closely follow object manipulation activities, we assess to what extent eye-gaze signals can support mistake detection, proposing to identify deviations in attention patterns measured through a gaze tracker with respect to those estimated by a gaze prediction model. Since predicting gaze in video is characterized by high uncertainty, we propose a novel gaze completion task, where eye fixations are predicted from visual observations and partial gaze trajectories, and contribute a novel gaze completion approach which explicitly models correlations between gaze information and local visual tokens. Inconsistencies between predicted and observed gaze trajectories act as an indicator to identify mistakes. Experiments highlight the effectiveness of the proposed approach in different settings, with relative gains up to  +14%, +11%, and +5% in EPIC-Tent, HoloAssist and IndustReal respectively, remarkably matching results of supervised approaches without seeing any labels. We further show that gaze-based analysis is particularly useful in the presence of skilled actions, low action execution confidence, and actions requiring hand-eye coordination and object manipulation skills. Our method is ranked first on the HoloAssist Mistake Detection challenge.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mazzamuto_Gazing_Into_Missteps_Leveraging_Eye-Gaze_for_Unsupervised_Mistake_Detection_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mazzamuto_Gazing_Into_Missteps_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mazzamuto_2025_CVPR,
    author    = {Mazzamuto, Michele and Furnari, Antonino and Sato, Yoichi and Farinella, Giovanni Maria},
    title     = {Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8310-8320}
}

raw_affiliation:
[{"affiliation": "University of Catania", "city": "Catania", "country": "Italy"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}]


title: Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM
authors: Yizhou Huang, Yihua Cheng, Kezhi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12058-12067
affiliations: ["Brunel University of London", "University of Birmingham"]
cities: ["London", "Birmingham"]
countries: ["United Kingdom", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Trajectory_Mamba_Efficient_Attention-Mamba_Forecasting_Model_Based_on_Selective_SSM_CVPR_2025_paper.html

abstract:
Motion prediction is crucial for autonomous driving, as it enables accurate forecasting of future vehicle trajectories based on historical inputs. This paper introduces Trajectory Mamba, a novel efficient trajectory prediction framework based on the selective state-space model (SSM). Conventional attention-based models face the challenge of computational costs that grow quadratically with the number of targets, hindering their application in highly dynamic environments. In response, we leverage the SSM to redesign the self-attention mechanism in the encoder-decoder architecture, thereby achieving linear time complexity. To address the potential reduction in prediction accuracy resulting from modifications to the attention mechanism, we propose a joint polyline encoding strategy to better capture the associations between static and dynamic contexts, ultimately enhancing prediction accuracy. Additionally, to balance prediction accuracy and inference speed, we adopted the decoder that differs entirely from the encoder. Through cross-state space attention, all target agents share the scene context, allowing the SSM to interact with the shared scene representation during decoding, thus inferring different trajectories over the next prediction steps. Our model achieves state-of-the-art results in terms of inference speed and parameter efficiency on both the Argoverse 1 and Argoverse 2 datasets. It demonstrates a four-fold reduction in FLOPs compared to existing methods and reduces parameter count by over 40% while surpassing the performance of the vast majority of previous methods. These findings validate the effectiveness of Trajectory Mamba in trajectory prediction tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Trajectory_Mamba_Efficient_Attention-Mamba_Forecasting_Model_Based_on_Selective_SSM_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yizhou and Cheng, Yihua and Wang, Kezhi},
    title     = {Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12058-12067}
}

raw_affiliation:
[{"affiliation": "Brunel University of London", "city": "London", "country": "United Kingdom"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "United Kingdom"}]


authors: Hao Tan, Zichang Tan, Jun Li, Ajian Liu, Jun Wan, Zhen Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4650-4660
cities: ["Beijing", "Beijing", "Shenzhen", "Shenzhen", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_Recover_and_Match_Open-Vocabulary_Multi-Label_Recognition_through_Knowledge-Constrained_Optimal_Transport_CVPR_2025_paper.html

title:
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport

abstract:
Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of powerful vision-language models such as CLIP. However, these approaches face two critical challenges: (1) The local semantics of CLIP are disrupted due to its global pre-training objectives, resulting in unreliable regional predictions. (2) The matching property between image regions and candidate labels has been neglected, relying instead on naive feature aggregation such as average pooling, which leads to spurious predictions from irrelevant regions. In this paper, we present RAM (Recover And Match), a novel framework that effectively addresses the above issues. To tackle the first problem, we propose Ladder Local Adapter (LLA) to enforce the model to refocus on its surrounding local regions, recovering local semantics in a memory-friendly way. For the second issue, we propose Knowledge-Constrained Optimal Transport (KCOT) to suppress meaningless matching to non-GT labels by formulating the task as an optimal transport problem. As a result, RAM achieves state-of-the-art performance on various datasets from three distinct domains, and shows great potential to boost the existing methods. Code: https://github.com/EricTan7/RAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_Recover_and_Match_Open-Vocabulary_Multi-Label_Recognition_through_Knowledge-Constrained_Optimal_Transport_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_Recover_and_Match_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Hao and Tan, Zichang and Li, Jun and Liu, Ajian and Wan, Jun and Lei, Zhen},
    title     = {Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4650-4660}
}

raw_affiliation:
[{"affiliation": "SAIS, UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "SIAT, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "Sangfor Technologies Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "SAI, UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "CAIR, HKISI, Chinese Academy of Sciences", "city": "Hong Kong", "country": "China"}]

affiliations:
["SAIS, UCAS", "MAIS, Institute of Automation, Chinese Academy of Sciences", "SIAT, Chinese Academy of Sciences", "Sangfor Technologies Inc.", "SAI, UCAS", "CAIR, HKISI, Chinese Academy of Sciences"]


title: RelationField: Relate Anything in Radiance Fields
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21706-21716
affiliations: ["University Ulm", "Bosch Center for AI", "Google", "TU Vienna", "TU Munich"]
cities: ["Ulm", "Stuttgart", "Mountain View", "Vienna", "Munich"]
countries: ["Germany", "Germany", "USA", "Austria", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Koch_RelationField_Relate_Anything_in_Radiance_Fields_CVPR_2025_paper.html

authors:
Sebastian Koch, Johanna Wald, Mirco Colosi, Narunas Vaskevicius, Pedro Hermosilla, Federico Tombari, Timo Ropinski

abstract:
Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from vision-language models. However, current method primarily focus on object-centric representations, supporting object segmentation or detection, while understanding semantic relationships between objects remains largely unexplored. To address this gap, we propose RelationField, the first method to extract inter-object relationships directly from neural radiance fields. RelationField represents relationships between objects as pairs of rays within a neural radiance field, effectively extending its formulation to include implicit relationship queries. To teach RelationField complex, open-vocabulary relationships, relationship knowledge is distilled from multi-modal LLMs. To evaluate RelationField, we solve open-vocabulary 3D scene graph generation tasks and relationship-guided instance segmentation, achieving state-of-the-art performance in both tasks. See the project website at relationfield.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Koch_RelationField_Relate_Anything_in_Radiance_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Koch_RelationField_Relate_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Koch_2025_CVPR,
    author    = {Koch, Sebastian and Wald, Johanna and Colosi, Mirco and Vaskevicius, Narunas and Hermosilla, Pedro and Tombari, Federico and Ropinski, Timo},
    title     = {RelationField: Relate Anything in Radiance Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21706-21716}
}

raw_affiliation:
[{"affiliation": "University Ulm", "city": "Ulm", "country": "Germany"}, {"affiliation": "Bosch Center for AI", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "TU Vienna", "city": "Vienna", "country": "Austria"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}]


authors: Geng Li, Jinglin Xu, Yunzhen Zhao, Yuxin Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9098-9108
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DyFo_A_Training-Free_Dynamic_Focus_Visual_Search_for_Enhancing_LMMs_CVPR_2025_paper.html

title:
DyFo: A Training-Free Dynamic Focus Visual Search for Enhancing LMMs in Fine-Grained Visual Understanding

abstract:
Humans can effortlessly locate desired objects in cluttered environments, relying on a cognitive mechanism known as visual search to efficiently filter out irrelevant information and focus on task related regions. Inspired by this process, we propose DyFo (Dynamic Focus), a training-free dynamic focusing visual search method that enhances fine-grained visual understanding in large multi-modal models (LMMs). Unlike existing approaches which require additional modules or data collection, DyFo leverages a bidirectional interaction between LMMs and visual experts, using a Monte Carlo Tree Search (MCTS) algorithm to simulate human-like focus adjustments. This enables LMMs to focus on key visual regions while filtering out irrelevant content, without introducing additional training caused by vocabulary expansion or the integration of specialized localization modules. Experimental results demonstrate that DyFo significantly improves fine-grained visual understanding and reduces hallucination issues in LMMs, achieving superior performance across both fixed and dynamic resolution models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DyFo_A_Training-Free_Dynamic_Focus_Visual_Search_for_Enhancing_LMMs_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Geng and Xu, Jinglin and Zhao, Yunzhen and Peng, Yuxin},
    title     = {DyFo: A Training-Free Dynamic Focus Visual Search for Enhancing LMMs in Fine-Grained Visual Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9098-9108}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent Beijing Research", "city": "Beijing", "country": "China"}]

affiliations:
["Wangxuan Institute of Computer Technology, Peking University", "School of Intelligence Science and Technology, University of Science and Technology Beijing", "Tencent Beijing Research"]


authors: Mingyang Song, Xiaoye Qu, Jiawei Zhou, Yu Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9434-9444
cities: ["Shanghai", "Shanghai", "Stony Brook", "Hong Kong"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_From_Head_to_Tail_Towards_Balanced_Representation_in_LargeVision-Language_Models_CVPR_2025_paper.html

title:
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration

abstract:
Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation.Despite this success, the training data of LVLMs still suffers from Long-Tail (LT) problems, where the data distribution is highly imbalanced.Previous works have mainly focused on traditional VLM architectures, i.e., CLIP or ViT, and specific tasks such as recognition and classification. Nevertheless, the exploration of LVLM (e.g. LLaVA) and more general tasks (e.g. Visual Question Answering and Visual Reasoning) remains under-explored.In this paper, we first conduct an in-depth analysis of the LT issues in LVLMs and identify two core causes: the overrepresentation of head concepts and the underrepresentation of tail concepts.Based on the above observation, we propose an Adaptive Data Refinement Framework (ADR), which consists of two stages: Data Rebalancing (DR) and Data Synthesis (DS).In the DR stage, we adaptively rebalance the redundant data based on entity distributions, while in the DS stage, we leverage Denoising Diffusion Probabilistic Models (DDPMs) and scarce images to supplement underrepresented portions.Through comprehensive evaluations across eleven benchmarks, our proposed ADR effectively mitigates the long-tail problem in the training data, improving the average performance of LLaVA 1.5 relatively by 4.36%, without increasing the training data volume. Our code and data will be publicly released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_From_Head_to_Tail_Towards_Balanced_Representation_in_LargeVision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_From_Head_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Mingyang and Qu, Xiaoye and Zhou, Jiawei and Cheng, Yu},
    title     = {From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9434-9444}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Artificial Intelligence Laboratory", "Stony Brook University", "The Chinese University of Hong Kong"]


title: Let Humanoids Hike! Integrative Skill Development on Complex Trails
authors: Kwan-Yee Lin, Stella X. Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22498-22507
raw_affiliation: [{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}]
affiliations: ["University of Michigan"]
cities: ["Ann Arbor"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Let_Humanoids_Hike_Integrative_Skill_Development_on_Complex_Trails_CVPR_2025_paper.html

abstract:
Hiking on complex trails demands balance, agility, and adaptive decision-making over unpredictable terrain. Current humanoid research remains fragmented and inadequate for hiking: locomotion focuses on motor skills without long-term goals or situational awareness, while semantic navigation overlooks real-world embodiment and local terrain variability.  We propose training humanoids to hike on complex trails, fostering integrative skill development across visual perception, decision making, and motor execution. We develop LEGO-H, a learning framework that enables a humanoid with vision to hike complex trails independently. It has two key innovations. (1) A Temporal Vision Transformer anticipates future steps to guide locomotion, unifying local movement and goal-directed navigation. (2) Latent representations of joint movement patterns combined with hierarchical metric learning allow smooth policy transfer from privileged training to real-world training.  These techniques enable LEGO-H to handle diverse physical and environmental challenges without relying on predefined motion patterns. Experiments on diverse simulated hiking trails and humanoids with different morphologies demonstrate LEGO-H's robustness and versatility, establishing a strong foundation for future humanoid development.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Let_Humanoids_Hike_Integrative_Skill_Development_on_Complex_Trails_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Let_Humanoids_Hike_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Kwan-Yee and Yu, Stella X.},
    title     = {Let Humanoids Hike! Integrative Skill Development on Complex Trails},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22498-22507}
}


title: VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15896-15908
raw_affiliation: [{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]
affiliations: ["Google DeepMind"]
cities: ["Mountain View"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Corona_VLOGGER_Multimodal_Diffusion_for_Embodied_Avatar_Synthesis_CVPR_2025_paper.html

authors:
Enric Corona, Andrei Zanfir, Eduard Gabriel Bazavan, Nikos Kolotouros, Thiemo Alldieck, Cristian Sminchisescu

abstract:
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models. Our method consists of 1) a stochastic human-to-3d-motion diffusion model, and 2) a novel diffusion-based architecture that augments text-to-image models with both spatial and temporal controls. This supports the generation of high quality video of variable length, easily controllable through text or speech via high-level representations of human faces and bodies. In contrast to previous work, our method does not require training for each person, does not rely on face detection and cropping, generates the complete image (not just the face or the lips), and considers a broad spectrum of scenarios (e.g. visible torso or diverse subject identities) that are critical to correctly synthesize humans who communicate. We also curate MENTOR, a new and diverse dataset with 3d pose and expression anno- tations, one order of magnitude larger than previous ones (800,000 identities) and with dynamic gestures, where we train and ablate our main technical contributions. VLOGGER outperforms state-of-the-art methods in three public benchmarks, considering image quality, identity preservation and temporal consistency while also generating upperbody gestures. We analyze the performance of VLOGGER with respect to multiple diversity metrics, showing that our architectural choices and the use of MENTOR benefit training a fair and unbiased model at scale. Finally we show applications in video editing and personalization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Corona_VLOGGER_Multimodal_Diffusion_for_Embodied_Avatar_Synthesis_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Corona_2025_CVPR,
    author    = {Corona, Enric and Zanfir, Andrei and Bazavan, Eduard Gabriel and Kolotouros, Nikos and Alldieck, Thiemo and Sminchisescu, Cristian},
    title     = {VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15896-15908}
}


title: DEIM: DETR with Improved Matching for Fast Convergence
authors: Shihua Huang, Zhichao Lu, Xiaodong Cun, Yongjun Yu, Xiao Zhou, Xi Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15162-15171
cities: ["Unknown", "Hong Kong", "Unknown", "Hefei"]
countries: ["Unknown", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_DEIM_DETR_with_Improved_Matching_for_Fast_Convergence_CVPR_2025_paper.html

abstract:
We introduce DEIM, an innovative and efficient training framework designed to accelerate convergence in real-time object detection with Transformer-based architectures (DETR). To mitigate the sparse supervision inherent in one-to-one (O2O) matching in DETR models, DEIM employs a Dense O2O matching strategy. This approach increases the number of positive samples per image by incorporating additional targets, using standard data augmentation techniques. While Dense O2O matching speeds up convergence, it also introduces numerous low-quality matches that could affect performance. To address this, we propose the Matchability-Aware Loss (MAL), a novel loss function that optimizes matches across various quality levels, enhancing the effectiveness of Dense O2O.    Extensive experiments on the COCO dataset validate the efficacy of DEIM. When integrated with RT-DETR and D-FINE, it consistently boosts performance while reducing training time by 50%. Notably, paired with RT-DETRv2, DEIM achieves 53.2% AP in a single day of training on an NVIDIA 4090 GPU. Additionally, DEIM-trained real-time models outperform leading real-time object detectors, with DEIM-D-FINE-L and DEIM-D-FINE-X achieving 54.7% and 56.4% AP at 124 and 78 FPS on an NVIDIA T4 GPU, respectively, without the need for additional data. We believe DEIM sets a new baseline for advancements in real-time object detection. Our code and pre-trained models are available at https://www.shihuahuang.cn/DEIM/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_DEIM_DETR_with_Improved_Matching_for_Fast_Convergence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_DEIM_DETR_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Shihua and Lu, Zhichao and Cun, Xiaodong and Yu, Yongjun and Zhou, Xiao and Shen, Xi},
    title     = {DEIM: DETR with Improved Matching for Fast Convergence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15162-15171}
}

raw_affiliation:
[{"affiliation": "Intellindust AI Lab", "city": "Unknown", "country": "Unknown"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Great Bay University", "city": "Unknown", "country": "China"}, {"affiliation": "Hefei Normal University", "city": "Hefei", "country": "China"}]

affiliations:
["Intellindust AI Lab", "City University of Hong Kong", "Great Bay University", "Hefei Normal University"]


authors: Eunjin Kim, Hyeonjin Kim, Kyong Hwan Jin, Jaejun Yoo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28009-28018
affiliations: ["Ulsan National Institute of Science and Technology (UNIST)", "Korea University"]
cities: ["Ulsan", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_BF-STVSR_B-Splines_and_Fourier---Best_Friends_for_High_Fidelity_Spatial-Temporal_Video_CVPR_2025_paper.html

title:
BF-STVSR: B-Splines and Fourier---Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution

abstract:
While prior methods in Continuous Spatial-Temporal Video Super-Resolution (C-STVSR) employ Implicit Neural Representation (INR) for continuous encoding, they often struggle to capture the complexity of video data, relying on simple coordinate concatenation and pre-trained optical flow networks for motion representation.  Interestingly, we find that adding position encoding, contrary to common observations, does not improve---and even degrades---performance. This issue becomes particularly pronounced when combined with pre-trained optical flow networks, which can limit the model's flexibility. To address these issues, we propose BF-STVSR, a C-STVSR framework with two key modules tailored to better represent spatial and temporal characteristics of video: 1) B-spline Mapper for smooth temporal interpolation, and 2) Fourier Mapper for capturing dominant spatial frequencies. Our approach achieves state-of-the-art in various metrics, including PSNR and SSIM, showing enhanced spatial details and natural temporal consistency. Our code is available https://github.com/Eunjnnn/bfstvsr.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_BF-STVSR_B-Splines_and_Fourier---Best_Friends_for_High_Fidelity_Spatial-Temporal_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_BF-STVSR_B-Splines_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Eunjin and Kim, Hyeonjin and Jin, Kyong Hwan and Yoo, Jaejun},
    title     = {BF-STVSR: B-Splines and Fourier---Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28009-28018}
}

raw_affiliation:
[{"affiliation": "Ulsan National Institute of Science and Technology (UNIST)", "city": "Ulsan", "country": "South Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}]


title: DIO: Decomposable Implicit 4D Occupancy-Flow World Model
authors: Christopher Diehl, Quinlan Sykora, Ben Agro, Thomas Gilles, Sergio Casas, Raquel Urtasun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27456-27466
affiliations: ["TU Dortmund University", "Waabi", "University of Toronto"]
cities: ["Dortmund", "Toronto", "Toronto"]
countries: ["Germany", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Diehl_DIO_Decomposable_Implicit_4D_Occupancy-Flow_World_Model_CVPR_2025_paper.html

abstract:
We present DIO, a flexible world model that can estimate the scene occupancy-flow from a sparse set of LiDAR observations, and decompose it into individual instances. DIO can not only complete instance shapes at the present time, but also forecast their occupancy-flow evolution over a future horizon. Thanks to its flexible prompt representation, DIO can take instance prompts from off-the-shelf models like 3D detectors, achieving state-of-the-art performance in the task of 4D semantic occupancy completion and forecasting on the Argoverse 2 dataset. Moreover, our world model can easily and effectively be transferred to downstream tasks like LiDAR point cloud forecasting, ranking first compared to all baselines in the Argoverse 4D occupancy forecasting challenge.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Diehl_DIO_Decomposable_Implicit_4D_Occupancy-Flow_World_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Diehl_DIO_Decomposable_Implicit_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Diehl_2025_CVPR,
    author    = {Diehl, Christopher and Sykora, Quinlan and Agro, Ben and Gilles, Thomas and Casas, Sergio and Urtasun, Raquel},
    title     = {DIO: Decomposable Implicit 4D Occupancy-Flow World Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27456-27466}
}

raw_affiliation:
[{"affiliation": "TU Dortmund University", "city": "Dortmund", "country": "Germany"}, {"affiliation": "Waabi", "city": "Toronto", "country": "Canada"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}]


title: SLADE: Shielding against Dual Exploits in Large Vision-Language Models
authors: Md Zarif Hossain, Ahmed Imteaj
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24244-24254
affiliations: ["School of Computing, Southern Illinois University Carbondale, IL 62901, USA"]
cities: ["Carbondale"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hossain_SLADE_Shielding_against_Dual_Exploits_in_Large_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Large Vision-Language Models (LVLMs) have emerged as transformative tools in multimodal tasks, seamlessly integrating pretrained vision encoders to align visual and textual modalities. Prior works have highlighted the susceptibility of LVLMs to dual exploits (gradient-based and optimization-based jailbreak attacks), which leverage the expanded attack surface introduced by the image modality. Despite advancements in enhancing robustness, existing methods fall short in their ability to defend against dual exploits while preserving fine-grained semantic details and overall semantic coherence under intense adversarial perturbations. To bridge this gap, we introduce SheiLding against Dual Exploits (SLADE), a novel unsupervised adversarial fine-tuning scheme that enhances the resilience of CLIP-based vision encoders. SLADE's dual-level contrastive learning approach balances the granular and the holistic, capturing fine-grained image details without losing sight of high-level semantic coherence. Extensive experiments demonstrate that SLADE-equipped LVLMs set a new benchmark for robustness against dual exploits while preserving fine-grained semantic details of perturbed images. Notably, SLADE achieves these results without compromising the core functionalities of LVLMs, such as instruction following, or requiring the computational overhead (e.g., large batch sizes, momentum encoders) commonly associated with traditional contrastive learning methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hossain_SLADE_Shielding_against_Dual_Exploits_in_Large_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hossain_SLADE_Shielding_against_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hossain_2025_CVPR,
    author    = {Hossain, Md Zarif and Imteaj, Ahmed},
    title     = {SLADE: Shielding against Dual Exploits in Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24244-24254}
}

raw_affiliation:
[{"affiliation": "School of Computing, Southern Illinois University Carbondale, IL 62901, USA", "city": "Carbondale", "country": "USA"}]


title: Human Motion Instruction Tuning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17582-17591
affiliations: ["1", "2", "3", "4", "5", "6"]
cities: ["Seattle", "Seattle", "unknown", "unknown", "unknown", "unknown"]
countries: ["USA", "USA", "unknown", "unknown", "unknown", "unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Human_Motion_Instruction_Tuning_CVPR_2025_paper.html

authors:
Lei Li, Sen Jia, Jianhao Wang, Zhongyu Jiang, Feng Zhou, Ju Dai, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang

abstract:
This paper presents LLaMo (Large Language and Human Motion Assistant), a multimodal framework for human motion instruction tuning. In contrast to conventional instruction-tuning approaches that convert non-linguistic inputs, such as video or motion sequences, into language tokens, LLaMo retains motion in its native form for instruction tuning. This method preserves motion-specific details that are often diminished in tokenization, thereby improving the model's ability to interpret complex human behaviors. By processing both video and motion data alongside textual inputs, LLaMo enables a flexible, human-centric analysis. Experimental evaluations across high-complexity domains, including human behaviors and professional activities, indicate that LLaMo effectively captures domain-specific knowledge, enhancing comprehension and prediction in motion-intensive scenarios. We hope LLaMo offers a foundation for future multimodal AI systems with broad applications, from sports analytics to behavioral prediction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Human_Motion_Instruction_Tuning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Lei and Jia, Sen and Wang, Jianhao and Jiang, Zhongyu and Zhou, Feng and Dai, Ju and Zhang, Tianfang and Wu, Zongkai and Hwang, Jenq-Neng},
    title     = {Human Motion Instruction Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17582-17591}
}

raw_affiliation:
[{"affiliation": "1", "city": "Seattle", "country": "USA"}, {"affiliation": "2", "city": "Seattle", "country": "USA"}, {"affiliation": "3", "city": "unknown", "country": "unknown"}, {"affiliation": "4", "city": "unknown", "country": "unknown"}, {"affiliation": "5", "city": "unknown", "country": "unknown"}, {"affiliation": "6", "city": "unknown", "country": "unknown"}]


title: A Flag Decomposition for Hierarchical Datasets
authors: Nathan Mankovich, Ignacio Santamaria, Gustau Camps-Valls, Tolga Birdal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18738-18748
cities: ["Valencia", "Santander", "Valencia", "London"]
countries: ["Spain", "Spain", "Spain", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mankovich_A_Flag_Decomposition_for_Hierarchical_Datasets_CVPR_2025_paper.html

abstract:
Flag manifolds encode nested sequences of subspaces and serve as powerful structures for various computer vision and machine learning applications. Despite their utility in tasks such as dimensionality reduction, motion averaging, and subspace clustering, current applications are often restricted to extracting flags using common matrix decomposition methods like the singular value decomposition. Here, we address the need for a general algorithm to factorize and work with hierarchical datasets. In particular, we propose a novel, flag-based method that decomposes arbitrary hierarchical real-valued data into a hierarchy-preserving flag representation in Stiefel coordinates. Our work harnesses the potential of flag manifolds in applications including denoising, clustering, and few-shot learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mankovich_A_Flag_Decomposition_for_Hierarchical_Datasets_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mankovich_A_Flag_Decomposition_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mankovich_2025_CVPR,
    author    = {Mankovich, Nathan and Santamaria, Ignacio and Camps-Valls, Gustau and Birdal, Tolga},
    title     = {A Flag Decomposition for Hierarchical Datasets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18738-18748}
}

raw_affiliation:
[{"affiliation": "Universitat de Val`encia", "city": "Valencia", "country": "Spain"}, {"affiliation": "Universidad de Cantabria", "city": "Santander", "country": "Spain"}, {"affiliation": "Universitat de Val`encia", "city": "Valencia", "country": "Spain"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}]

affiliations:
["Universitat de Val`encia", "Universidad de Cantabria", "Universitat de Val`encia", "Imperial College London"]


title: RCP-Bench: Benchmarking Robustness for Collaborative Perception Under Diverse Corruptions
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11908-11918
affiliations: ["Tongji University", "EACON Technology Co., ltd."]
cities: ["Shanghai", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_RCP-Bench_Benchmarking_Robustness_for_Collaborative_Perception_Under_Diverse_Corruptions_CVPR_2025_paper.html

authors:
Shihang Du, Sanqing Qu, Tianhang Wang, Xudong Zhang, Yunwei Zhu, Jian Mao, Fan Lu, Qiao Lin, Guang Chen

abstract:
Collaborative perception enhances single-vehicle perception by integrating sensory data from multiple connected vehicles. However, existing studies often assume ideal conditions, overlooking resilience to real-world challenges such as adverse weather and sensor malfunctions, which is critical for safe deployment. To address this gap, we introduce RCP-Bench, the first comprehensive benchmark designed to evaluate the robustness of collaborative detection models under a wide range of real-world corruptions. RCP-Bench includes three new datasets (i.e., OPV2V-C, V2XSet-C, and DAIR-V2X-C) that simulate six collaborative cases and 14 types of camera corruption resulting from external environmental factors, sensor failures, and temporal misalignments. Extensive experiments on 10 leading collaborative perception models reveal that, while these models perform well under ideal conditions, they are significantly affected by corruptions. To improve robustness, we propose two simple yet effective strategies, RCP-Drop and RCP-Mix, based on training regularization and feature augmentation. Additionally, we identify several critical factors influencing robustness, such as backbone architecture, camera number, feature fusion methods, and the number of connected vehicles. We hope that RCP-Bench, along with these strategies and insights, will stimulate future research toward developing more robust collaborative perception models. Our benchmark toolkit is available at https://github.com/LuckyDush/RCP-Bench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_RCP-Bench_Benchmarking_Robustness_for_Collaborative_Perception_Under_Diverse_Corruptions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_RCP-Bench_Benchmarking_Robustness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Shihang and Qu, Sanqing and Wang, Tianhang and Zhang, Xudong and Zhu, Yunwei and Mao, Jian and Lu, Fan and Lin, Qiao and Chen, Guang},
    title     = {RCP-Bench: Benchmarking Robustness for Collaborative Perception Under Diverse Corruptions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11908-11918}
}

raw_affiliation:
[{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "EACON Technology Co., ltd.", "city": "Unknown", "country": "Unknown"}]


title: Olympus: A Universal Task Router for Computer Vision Tasks
authors: Yuanze Lin, Yunsheng Li, Dongdong Chen, Weijian Xu, Ronald Clark, Philip Torr
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14235-14246
affiliations: ["University of Oxford", "Microsoft"]
cities: ["Oxford", "Redmond"]
countries: ["United Kingdom", "United States"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Olympus_A_Universal_Task_Router_for_Computer_Vision_Tasks_CVPR_2025_paper.html

abstract:
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Utilizing a controller MLLM, Olympus delegates over 20 specialized tasks across images, videos, and 3D objects to dedicated modules. This instruction-based routing enables complex workflows through chained actions without the need for training heavy generative models. Olympus easily integrates with existing MLLMs, expanding their capabilities with comparable performance. Experimental results demonstrate that Olympus achieves an average routing accuracy of 94.75% across 20 tasks and precision of 91.82% in chained action scenarios, showcasing its effectiveness as a universal task router that can solve a diverse range of computer vision tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Olympus_A_Universal_Task_Router_for_Computer_Vision_Tasks_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Yuanze and Li, Yunsheng and Chen, Dongdong and Xu, Weijian and Clark, Ronald and Torr, Philip},
    title     = {Olympus: A Universal Task Router for Computer Vision Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14235-14246}
}

raw_affiliation:
[{"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "United States"}]


title: HERA: Hybrid Explicit Representation for Ultra-Realistic Head Avatars
authors: Hongrui Cai, Yuting Xiao, Xuan Wang, Jiafei Li, Yudong Guo, Yanbo Fan, Shenghua Gao, Juyong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 260-270
cities: ["Hefei", "Shanghai", "Xi'an", "Nanjing", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_HERA_Hybrid_Explicit_Representation_for_Ultra-Realistic_Head_Avatars_CVPR_2025_paper.html

abstract:
We introduce a novel approach to creating ultra-realistic head avatars and rendering them in real time (\geq 30 fps at 2048 x1334 resolution). First, we propose a hybrid explicit representation that combines the advantages of two primitive based efficient rendering techniques. UV-mapped 3D mesh is utilized to capture sharp and rich textures on smooth surfaces, while 3D Gaussian Splatting is employed to represent complex geometric structures. In the pipeline of modeling an avatar, after tracking parametric models based on captured multi-view RGB videos, our goal is to simultaneously optimize the texture and opacity map of mesh, as well as a set of 3D Gaussian splats localized and rigged onto the mesh facets. Specifically, we perform \alpha-blending on the color and opacity values based on the merged and re-ordered z-buffer from the rasterization results of mesh and 3DGS. This process involves the mesh and 3DGS adaptively fitting the captured visual information to outline a high-fidelity digital avatar. To avoid artifacts caused by Gaussian splats crossing the mesh facets, we design a stable hybrid depth sorting strategy. Experiments illustrate that our modeled results exceed those of state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_HERA_Hybrid_Explicit_Representation_for_Ultra-Realistic_Head_Avatars_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_HERA_Hybrid_Explicit_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Hongrui and Xiao, Yuting and Wang, Xuan and Li, Jiafei and Guo, Yudong and Fan, Yanbo and Gao, Shenghua and Zhang, Juyong},
    title     = {HERA: Hybrid Explicit Representation for Ultra-Realistic Head Avatars},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {260-270}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["University of Science and Technology of China", "ShanghaiTech University", "Xi\u2019an Jiaotong University", "Nanjing University", "University of Hong Kong"]


title: Circumventing Shortcuts in Audio-visual Deepfake Detection Datasets with Unsupervised Learning
authors: Stefan Smeu, Dragos-Alexandru Boldisor, Dan Oneata, Elisabeta Oneata
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18815-18825
affiliations: ["Bitdefender", "POLITEHNICA Bucharest"]
cities: ["Bucharest", "Bucharest"]
countries: ["Romania", "Romania"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Smeu_Circumventing_Shortcuts_in_Audio-visual_Deepfake_Detection_Datasets_with_Unsupervised_Learning_CVPR_2025_paper.html

abstract:
Good datasets are essential for developing and benchmarking any machine learning system. Their importance is even more extreme for safety critical applications such as deepfake detection - the focus of this paper. Here we reveal that two of the most widely used audio-video deepfake datasets suffer from a previously unidentified spurious feature: the leading silence. Fake videos start with a very brief moment of silence and, on the basis of this feature alone, we can separate the real and fake samples almost perfectly. As such, previous audio-only and audio-video models exploit the presence of silence in the fake videos and consequently perform worse when the leading silence is removed. To circumvent latching on such an unwanted artifact and possibly other unrevealed ones, we propose a shift from supervised to unsupervised learning by training models exclusively on real data. We show that by aligning self-supervised audio-video representations we remove the risk of relying on dataset-specific biases and improve robustness in deepfake detection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Smeu_Circumventing_Shortcuts_in_Audio-visual_Deepfake_Detection_Datasets_with_Unsupervised_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Smeu_2025_CVPR,
    author    = {Smeu, Stefan and Boldisor, Dragos-Alexandru and Oneata, Dan and Oneata, Elisabeta},
    title     = {Circumventing Shortcuts in Audio-visual Deepfake Detection Datasets with Unsupervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18815-18825}
}

raw_affiliation:
[{"affiliation": "Bitdefender", "city": "Bucharest", "country": "Romania"}, {"affiliation": "POLITEHNICA Bucharest", "city": "Bucharest", "country": "Romania"}]


authors: Wenlong Yu, Qilong Wang, Chuang Liu, Dong Li, Qinghua Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4364-4374
affiliations: ["Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_CoE_Chain-of-Explanation_via_Automatic_Visual_Concept_Circuit_Description_and_Polysemanticity_CVPR_2025_paper.html

title:
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification

abstract:
Explainability is a critical factor influencing the wide deployment of deep vision models (DVMs). Concept-based post-hoc explanation methods can provide both global and local insights into model decisions. However, current methods in this field face challenges in that they are inflexible to automatically construct accurate and sufficient linguistic explanations for global concepts and local circuits. Particularly, the intrinsic polysemanticity in semantic Visual Concepts (VCs) impedes the interpretability of concepts and DVMs, which is underestimated severely. In this paper, we propose a Chain-of-Explanation (CoE) approach to address these issues. Specifically, CoE automates the decoding and description of VCs to construct global concept explanation datasets. Further, to alleviate the effect of polysemanticity on model explainability, we design a concept polysemanticity disentanglement and filtering mechanism to distinguish the most contextually relevant concept atoms. Besides, a Concept Polysemanticity Entropy (CPE), as a measure of model interpretability, is formulated to quantify the degree of concept uncertainty. The modeling of deterministic concepts is upgraded to uncertain concept atom distributions. Ultimately, CoE automatically enables linguistic local explanations of the decision-making process of DVMs by tracing the concept circuit. GPT-4o and human-based experiments demonstrate the effectiveness of CPE and the superiority of CoE, achieving an average absolute improvement of 36% in terms of explainability scores.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_CoE_Chain-of-Explanation_via_Automatic_Visual_Concept_Circuit_Description_and_Polysemanticity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_CoE_Chain-of-Explanation_via_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Wenlong and Wang, Qilong and Liu, Chuang and Li, Dong and Hu, Qinghua},
    title     = {CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4364-4374}
}

raw_affiliation:
[{"affiliation": "Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}]


title: Ego4o: Egocentric Human Motion Capture and Understanding from Multi-Modal Input
authors: Jian Wang, Rishabh Dabral, Diogo Luvizon, Zhe Cao, Lingjie Liu, Thabo Beeler, Christian Theobalt
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22668-22679
cities: ["Saarbrucken", "Mountain View", "Philadelphia", "Saarbrucken"]
countries: ["Germany", "USA", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Ego4o_Egocentric_Human_Motion_Capture_and_Understanding_from_Multi-Modal_Input_CVPR_2025_paper.html

abstract:
This work focuses on tracking and understanding human motion using consumer wearable devices, such as VR/AR headsets, smart glasses, cellphones, and smartwatches. These devices provide diverse, multi-modal sensor inputs, including egocentric images, and 1-3 sparse IMU sensors in varied combinations. Motion descriptions can also accompany these signals. The diverse input modalities and their intermittent availability pose challenges for consistent motion capture and understanding. In this work, we present Ego4o (o for omni), a new framework for simultaneous human motion capture and understanding from multi-modal egocentric inputs. This method maintains performance with partial inputs while achieving better results when multiple modalities are combined. First, the IMU sensor inputs, the optional egocentric image, and text description of human motion are encoded into the latent space of a motion VQ-VAE. Next, the latent vectors are sent to the VQ-VAE decoder and optimized to track human motion. When motion descriptions are unavailable, the latent vectors can be input into a multi-modal LLM to generate human motion descriptions, which can further enhance motion capture accuracy. Quantitative and qualitative evaluations demonstrate the effectiveness of our method in predicting accurate human motion and high-quality motion descriptions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Ego4o_Egocentric_Human_Motion_Capture_and_Understanding_from_Multi-Modal_Input_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Ego4o_Egocentric_Human_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jian and Dabral, Rishabh and Luvizon, Diogo and Cao, Zhe and Liu, Lingjie and Beeler, Thabo and Theobalt, Christian},
    title     = {Ego4o: Egocentric Human Motion Capture and Understanding from Multi-Modal Input},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22668-22679}
}

raw_affiliation:
[{"affiliation": "MPI Informatics & Saarland Informatics Campus", "city": "Saarbrucken", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "Saarbr \u00a8ucken Research Center for V isual Computing, Interaction and Arti\ufb01cial Intelligence", "city": "Saarbrucken", "country": "Germany"}]

affiliations:
["MPI Informatics & Saarland Informatics Campus", "Google", "University of Pennsylvania", "Saarbr \u00a8ucken Research Center for V isual Computing, Interaction and Arti\ufb01cial Intelligence"]


title: Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19681-19690
affiliations: ["Shanghai Arti\ufb01cial Intelligence Laboratory", "Shanghai Jiao Tong University"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Image_Over_Text_Transforming_Formula_Recognition_Evaluation_with_Character_Detection_CVPR_2025_paper.html

authors:
Bin Wang, Fan Wu, Linke Ouyang, Zhuangcheng Gu, Rui Zhang, Renqiu Xia, Botian Shi, Bo Zhang, Conghui He

abstract:
Formula recognition presents significant challenges due to the complicated structure and varied notation of mathematical expressions. Despite continuous advancements in formula recognition models, the evaluation metrics employed by these models, such as BLEU and Edit Distance, still exhibit notable limitations. They overlook the fact that the same formula has diverse representations and is highly sensitive to the distribution of training data, thereby causing unfairness in formula recognition evaluation. To this end, we propose a Character Detection Matching (CDM) metric, ensuring the evaluation objectivity by designing an image-level rather than a LaTeX-level metric score. Specifically, CDM renders both the model-predicted LaTeX and the ground-truth LaTeX formulas into image-formatted formulas, then employs visual feature extraction and localization techniques for precise character-level matching, incorporating spatial position information. Such a spatially-aware and character-matching method offers a more accurate and equitable evaluation compared with previous BLEU and Edit Distance metrics that rely solely on text-based character matching. Experimentally, we evaluated various formula recognition models using CDM, BLEU, and ExpRate metrics. Their results demonstrate that the CDM aligns more closely with human evaluation standards and provides a fairer comparison across different models by eliminating discrepancies caused by diverse formula representations. Code is available at https://github.com/opendatalab/UniMERNet/tree/main/cdm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Image_Over_Text_Transforming_Formula_Recognition_Evaluation_with_Character_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Image_Over_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Bin and Wu, Fan and Ouyang, Linke and Gu, Zhuangcheng and Zhang, Rui and Xia, Renqiu and Shi, Botian and Zhang, Bo and He, Conghui},
    title     = {Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19681-19690}
}

raw_affiliation:
[{"affiliation": "Shanghai Arti\ufb01cial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


authors: Jiangtong Tan, Hu Yu, Jie Huang, Jie Xiao, Feng Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27979-27988
cities: ["Hefei"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FreePCA_Integrating_Consistency_Information_across_Long-short_Frames_in_Training-free_Long_CVPR_2025_paper.html

title:
FreePCA: Integrating Consistency Information across Long-short Frames in Training-free Long Video Generation via Principal Component Analysis

abstract:
Long video generation involves generating extended videos using models trained on short videos, suffering from distribution shifts due to varying frame counts. It necessitates the use of local information from the original short frames to enhance visual and motion quality, and global information from the entire long frames to ensure appearance consistency. Existing training-free methods struggle to effectively integrate the benefits of both, as appearance and motion in videos are closely coupled, leading to inconsistency and poor quality. In this paper, we reveal that global and local information can be precisely decoupled into consistent appearance and motion intensity information by applying Principal Component Analysis (PCA), allowing for refined complementary integration of global consistency and local quality. With this insight, we propose FreePCA, a training-free long video generation paradigm based on PCA that simultaneously achieves high consistency and quality. Concretely, we decouple consistent appearance and motion intensity features by measuring cosine similarity in the principal component space. Critically, we progressively integrate these features to preserve original quality and ensure smooth transitions, while further enhancing consistency by reusing the mean statistics of the initial noise. Experiments demonstrate that FreePCA can be applied to various video diffusion models without requiring training, leading to substantial improvements. Code is available at https://github.com/JosephTiTan/FreePCA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_FreePCA_Integrating_Consistency_Information_across_Long-short_Frames_in_Training-free_Long_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_FreePCA_Integrating_Consistency_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Jiangtong and Yu, Hu and Huang, Jie and Xiao, Jie and Zhao, Feng},
    title     = {FreePCA: Integrating Consistency Information across Long-short Frames in Training-free Long Video Generation via Principal Component Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27979-27988}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China"]


title: Hierarchical Adaptive Filtering Network for Text Image Specular Highlight Removal
authors: Zhi Jiang, Jingbo Hu, Ling Zhang, Gang Fu, Chunxia Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2408-2417
cities: ["Wuhan", "Wuhan", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Hierarchical_Adaptive_Filtering_Network_for_Text_Image_Specular_Highlight_Removal_CVPR_2025_paper.html

abstract:
Despite significant advances in the field of specular highlight removal in recent years, existing methods predominantly focus on natural images, where highlights typically appear on raised or edged surfaces of objects. These highlights are often small and sparsely distributed. However, for text images such as cards and posters, the flat surfaces reflect light uniformly, resulting in large areas of highlights. Current methods struggle with these large-area highlights in text images, often producing severe visual artifacts or noticeable discrepancies between filled pixels and the original image in the central high-intensity highlight areas. To address these challenges, we propose the Hierarchical Adaptive Filtering Network (HAFNet). Our approach performs filtering at both the downsampled deep feature layer and the upsampled image reconstruction layer.  By designing and applying the Adaptive Comprehensive Filtering Module (ACFM) and Adaptive Dilated Filtering Module (ADFM) at different layers, our method effectively restores semantic information in large-area specular highlight regions and recovers detail loss at various scales. The required filtering kernels are pre-generated by a prediction network, allowing them to adaptively adjust according to different images and their semantic content, enabling robust performance across diverse scenarios.  Additionally, we utilize Unity3D to construct a comprehensive large-area highlight dataset featuring images with rich texts and complex textures. Experimental results on various datasets demonstrate that our method outperforms state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Hierarchical_Adaptive_Filtering_Network_for_Text_Image_Specular_Highlight_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Hierarchical_Adaptive_Filtering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Zhi and Hu, Jingbo and Zhang, Ling and Fu, Gang and Xiao, Chunxia},
    title     = {Hierarchical Adaptive Filtering Network for Text Image Specular Highlight Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2408-2417}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University, China", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Wuhan University of Science and Technology, China", "city": "Wuhan", "country": "China"}, {"affiliation": "Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University, China", "School of Computer Science and Technology, Wuhan University of Science and Technology, China", "Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China"]


authors: Chen-Yi Lu, Kasra Derakhshandeh, Somali Chaterji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20233-20243
raw_affiliation: [{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}]
affiliations: ["Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Improving_Semi-Supervised_Semantic_Segmentation_with_Sliced-Wasserstein_Feature_Alignment_and_Uniformity_CVPR_2025_paper.html

title:
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity

abstract:
Semi-supervised semantic segmentation with consistencyregularization capitalizes on unlabeled images to enhancethe accuracy of pixel-level segmentation. Current consistencylearning methods primarily rely on the consistency loss be-tween pseudo-labels and unlabeled images, neglecting the in-formation within the feature representations of the backboneencoder. Preserving maximum information in feature embed-dings requires achieving the alignment and uniformity objec-tives, as widely studied. To address this, we present SWSEG,a semi-supervised semantic segmentation algorithm that opti-mizes alignment and uniformity using the Sliced-WassersteinDistance (SWD), and rigorously and empirically proves thisconnection. We further resolve the computational issues as-sociated with conventional Monte Carlo-based SWD by im-plementing a Gaussian-approximated variant, which not onlymaintains the alignment and uniformity objectives but alsoimproves training efficiency. We evaluate SWSEG on thePASCAL VOC 2012, Cityscapes, and ADE20K datasets, out-shining supervised baselines in mIoU by up to 11.8%, 8.9%,and 8.2%, respectively, given an equivalent number of labeledsamples. Further, SWSEG surpasses state-of-the-art methodsin multiple settings across these three datasets. Our extensiveablation studies confirm the optimization of the uniformityand alignment objectives of the feature representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Improving_Semi-Supervised_Semantic_Segmentation_with_Sliced-Wasserstein_Feature_Alignment_and_Uniformity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Improving_Semi-Supervised_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Chen-Yi and Derakhshandeh, Kasra and Chaterji, Somali},
    title     = {Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20233-20243}
}


title: Mind the Time: Temporally-Controlled Multi-Event Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23989-24000
affiliations: ["Snap Research", "University of Toronto", "Vector Institute"]
cities: ["Menlo Park", "Toronto", "Toronto"]
countries: ["USA", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Mind_the_Time_Temporally-Controlled_Multi-Event_Video_Generation_CVPR_2025_paper.html

authors:
Ziyi Wu, Aliaksandr Siarohin, Willi Menapace, Ivan Skorokhodov, Yuwei Fang, Varnith Chordia, Igor Gilitschenski, Sergey Tulyakov

abstract:
Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input. When tasked with generating multiple events described using a single prompt, such methods often ignore some of the events or fail to arrange them in the correct order. To address this limitation, we present MinT, a multi-event video generator with temporal control. Our key insight is to bind each event to a specific period in the generated video, which allows the model to focus on one event at a time. To enable time-aware interactions between event captions and video tokens, we design a time-based positional encoding method, dubbed ReRoPE. This encoding helps to guide the cross-attention operation. By fine-tuning a pre-trained video diffusion transformer on temporally grounded data, our approach produces coherent videos with smoothly connected events. For the first time in the literature, our model offers control over the timing of events in generated videos. Extensive experiments demonstrate that MinT outperforms existing commercial and open-source models by a large margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Mind_the_Time_Temporally-Controlled_Multi-Event_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Mind_the_Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Ziyi and Siarohin, Aliaksandr and Menapace, Willi and Skorokhodov, Ivan and Fang, Yuwei and Chordia, Varnith and Gilitschenski, Igor and Tulyakov, Sergey},
    title     = {Mind the Time: Temporally-Controlled Multi-Event Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23989-24000}
}

raw_affiliation:
[{"affiliation": "Snap Research", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}]


title: Learning Extremely High Density Crowds as Active Matters
authors: Feixiang He, Jiangbei Yue, Jialin Zhu, Armin Seyfried, Dan Casas, Julien Pettré, He Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 540-550
cities: ["London", "London", "Leeds", "J\u00fclich", "Madrid", "Rennes"]
countries: ["UK", "UK", "UK", "Germany", "Spain", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_Learning_Extremely_High_Density_Crowds_as_Active_Matters_CVPR_2025_paper.html

abstract:
Video-based high-density crowd analysis and prediction has been a long-standing topic in computer vision. It is notoriously difficult due to, but not limited to, the lack of high-quality data and complex crowd dynamics. Consequently, it has been relatively under studied. In this paper, we propose a new approach that aims to learn from in-the-wild videos, often with low quality where it is difficult to track individuals or count heads. The key novelty is a new physics prior to model crowd dynamics. We model high-density crowds as active matter, a continumm with active particles subject to stochastic forces, named `crowd material'. Our physics model is combined with neural networks, resulting in a neural stochastic differential equation system which can mimic the complex crowd dynamics. Due to the lack of similar research, we adapt a range of existing methods which are close to ours, and other possible alternatives for comparison. Through exhaustive evaluation, we show our model outperforms existing methods in analyzing and forecasting extremely high-density crowds. Furthermore, since our model is a continuous-time physics model, it can be used for simulation and analysis, providing strong interpretability. This is categorically different from most deep learning methods, which are discrete-time models and black-boxes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_Learning_Extremely_High_Density_Crowds_as_Active_Matters_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_Learning_Extremely_High_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Feixiang and Yue, Jiangbei and Zhu, Jialin and Seyfried, Armin and Casas, Dan and Pettr\'e, Julien and Wang, He},
    title     = {Learning Extremely High Density Crowds as Active Matters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {540-550}
}

raw_affiliation:
[{"affiliation": "University College London", "city": "London", "country": "UK"}, {"affiliation": "University College London", "city": "London", "country": "UK"}, {"affiliation": "University of Leeds", "city": "Leeds", "country": "UK"}, {"affiliation": "Forschungszentrum J\u00a8ulich", "city": "J\u00fclich", "country": "Germany"}, {"affiliation": "Universidad Rey Juan Carlos", "city": "Madrid", "country": "Spain"}, {"affiliation": "INRIA Rennes", "city": "Rennes", "country": "France"}]

affiliations:
["University College London", "University College London", "University of Leeds", "Forschungszentrum J\u00a8ulich", "Universidad Rey Juan Carlos", "INRIA Rennes"]


title: Audio-Visual Semantic Graph Network for Audio-Visual Event Localization
authors: Liang Liu, Shuaiyong Li, Yongqiang Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23957-23966
affiliations: ["School of Computer Science and Technology", "School of Automation"]
cities: ["Chongqing", "Chongqing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Audio-Visual_Semantic_Graph_Network_for_Audio-Visual_Event_Localization_CVPR_2025_paper.html

abstract:
Audio-visual event localization (AVEL) aims to identify both the category and temporal boundaries of events that are both audible and visible in unconstrained videos. However, the inherent semantic gap between heterogeneous modalities often leads to semantic inconsistency. In this paper, we propose a novel Audio-Visual Semantic Graph Network (AVSGN) to facilitate cross-modal alignment and cross-temporal interaction. Unlike previous approaches (e.g., audio-guided, visual-guided, or both), we introduce shared semantic textual labels to bridge the semantic gap between audio and visual modalities. Specifically, we present a cross-modal semantic alignment (CMSA) module to explore the complementary relationships across heterogeneous modalities (i.e., visual, audio, and text), promoting the convergence of multimodal distributions into a unified semantic space. Additionally, in order to capture cross-temporal dependencies sufficiently, we devise a cross-modal graph interaction (CMGI) module which disentangles complicated interactions across modalities into three complementary subgraphs. Extensive experiments on the AVE dataset comprehensively demonstrate the superiority and effectiveness of the proposed model in both fully- and weakly-supervised AVE settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Audio-Visual_Semantic_Graph_Network_for_Audio-Visual_Event_Localization_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Liang and Li, Shuaiyong and Zhu, Yongqiang},
    title     = {Audio-Visual Semantic Graph Network for Audio-Visual Event Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23957-23966}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology", "city": "Chongqing", "country": "China"}, {"affiliation": "School of Automation", "city": "Chongqing", "country": "China"}]


title: 3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning
authors: Yuncong Yang, Han Yang, Jiachen Zhou, Peihao Chen, Hongxin Zhang, Yilun Du, Chuang Gan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17294-17303
affiliations: ["UMass Amherst", "CUHK", "Columbia University", "MIT", "MIT-IBM Watson AI Lab"]
cities: ["Amherst", "Hong Kong", "New York", "Cambridge", "Cambridge"]
countries: ["USA", "China", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_3D-Mem_3D_Scene_Memory_for_Embodied_Exploration_and_Reasoning_CVPR_2025_paper.html

abstract:
Constructing compact and informative 3D scene representations is essential for effective embodied exploration and reasoning, especially in complex environments over extended periods. Existing representations, such as object-centric 3D scene graphs, oversimplify spatial relationships by modeling scenes as isolated objects with restrictive textual relationships, making it difficult to address queries requiring nuanced spatial understanding. Moreover, these representations lack natural mechanisms for active exploration and memory management, hindering their application to lifelong autonomy. In this work, we propose 3D-Mem, a novel 3D scene memory framework for embodied agents. 3D-Mem employs informative multi-view images, termed Memory Snapshots, to capture rich visual information of explored regions. It further integrates frontier-based exploration by introducing Frontier Snapshots--glimpses of unexplored areas--enabling agents to make decisions by considering both known and potential new information. To support lifelong memory in active exploration settings, we present an incremental construction pipeline for 3D-Mem, as well as a memory retrieval technique for memory management. Experimental results on three benchmarks demonstrate that 3D-Mem significantly enhances agents' exploration and reasoning capabilities in 3D environments, highlighting its potential for advancing applications in embodied AI.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_3D-Mem_3D_Scene_Memory_for_Embodied_Exploration_and_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_3D-Mem_3D_Scene_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yuncong and Yang, Han and Zhou, Jiachen and Chen, Peihao and Zhang, Hongxin and Du, Yilun and Gan, Chuang},
    title     = {3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17294-17303}
}

raw_affiliation:
[{"affiliation": "UMass Amherst", "city": "Amherst", "country": "USA"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}]


title: EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
authors: Rang Meng, Xingyu Zhang, Yuming Li, Chenguang Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5489-5498
affiliations: ["Terminal Technology Department, Alipay, Ant Group"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_EchoMimicV2_Towards_Striking_Simplified_and_Semi-Body_Human_Animation_CVPR_2025_paper.html

abstract:
Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is possible to achieve striking half-body human animation while simplifying unnecessary conditions. To this end, we propose a half-body human animation method, dubbed EchoMimicV2, that leverages a novel Audio-Pose Dynamic Harmonization strategy, including Pose Sampling and Audio Diffusion, to enhance half-body details, facial and gestural expressiveness, and meanwhile reduce conditions redundancy. To compensate for the scarcity of half-body data, we utilize Head Partial Attention to seamlessly accommodate headshot data into our training framework, which can be omitted during inference, providing a free lunch for animation. Furthermore, we design the Phase-specific Denoising Loss to guide motion, detail, and low-level quality for animation in specific phases, respectively. Besides, we also present a novel benchmark for evaluating the effectiveness of half-body human animation. Extensive experiments and analyses demonstrate that EchoMimicV2 surpasses existing methods in both quantitative and qualitative evaluations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_EchoMimicV2_Towards_Striking_Simplified_and_Semi-Body_Human_Animation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Rang and Zhang, Xingyu and Li, Yuming and Ma, Chenguang},
    title     = {EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5489-5498}
}

raw_affiliation:
[{"affiliation": "Terminal Technology Department, Alipay, Ant Group", "city": "Hangzhou", "country": "China"}]


title: Navigation World Models
authors: Amir Bar, Gaoyue Zhou, Danny Tran, Trevor Darrell, Yann LeCun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15791-15801
affiliations: ["FAIR at Meta", "New York University", "Berkeley AI Research"]
cities: ["New York", "New York", "Berkeley"]
countries: ["USA", "USA", "USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bar_Navigation_World_Models_CVPR_2025_paper.html

abstract:
Navigation is a fundamental skill of agents with visual-motor capabilities. We introduce a Navigation World Model (NWM), a controllable video generation model that predicts future visual observations based on past observations and navigation actions. To capture complex environment dynamics, NWM employs a Conditional Diffusion Transformer (CDiT), trained on a diverse collection of egocentric videos of both human and robotic agents, and scaled up to 1 billion parameters. In familiar environments, NWM can plan navigation trajectories by simulating them and evaluating whether they achieve the desired goal. Unlike supervised navigation policies with fixed behavior, NWM can dynamically incorporate constraints during planning. Experiments demonstrate its effectiveness in planning trajectories from scratch or by ranking trajectories sampled from an external policy. Furthermore, NWM leverages its learned visual priors to imagine trajectories in unfamiliar environments from a single input image, making it a flexible and powerful tool for next-generation navigation systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bar_Navigation_World_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bar_Navigation_World_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bar_2025_CVPR,
    author    = {Bar, Amir and Zhou, Gaoyue and Tran, Danny and Darrell, Trevor and LeCun, Yann},
    title     = {Navigation World Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15791-15801}
}

raw_affiliation:
[{"affiliation": "FAIR at Meta", "city": "New York", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Berkeley AI Research", "city": "Berkeley", "country": "USA"}]


title: Video Motion Transfer with Diffusion Transformers
authors: Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov, Philip Torr, Fabio Pizzati
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22911-22921
affiliations: ["University of Oxford", "Snap Inc.", "MBZUAI"]
cities: ["Oxford", "Santa Monica", "Abu Dhabi"]
countries: ["United Kingdom", "United States", "United Arab Emirates"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pondaven_Video_Motion_Transfer_with_Diffusion_Transformers_CVPR_2025_paper.html

abstract:
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process the reference video with a pre-trained DiT to analyze cross-frame attention maps and extract a patch-wise motion signal called the Attention Motion Flow (AMF). We guide the latent denoising process in an optimization-based, training-free, manner by optimizing latents with our AMF loss to generate videos reproducing the motion of the reference one. We also apply our optimization strategy to transformer positional embeddings, granting us a boost in zero-shot motion transfer capabilities. We evaluate DiTFlow against recently published methods, outperforming all across multiple metrics and human evaluation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pondaven_Video_Motion_Transfer_with_Diffusion_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pondaven_Video_Motion_Transfer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pondaven_2025_CVPR,
    author    = {Pondaven, Alexander and Siarohin, Aliaksandr and Tulyakov, Sergey and Torr, Philip and Pizzati, Fabio},
    title     = {Video Motion Transfer with Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22911-22921}
}

raw_affiliation:
[{"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "United States"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "United Arab Emirates"}]


title: Gaussian Splatting for Efficient Satellite Image Photogrammetry
authors: Luca Savant Aira, Gabriele Facciolo, Thibaud Ehret
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5959-5969
affiliations: ["Politecnico di T orino", "Universite Paris-Saclay", "AMIAD, P \u02c6ole Recherche"]
cities: ["Turin", "Gif-sur-Yvette", "France"]
countries: ["Italy", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Aira_Gaussian_Splatting_for_Efficient_Satellite_Image_Photogrammetry_CVPR_2025_paper.html

abstract:
Recently, Gaussian splatting has emerged as a strong alternative to NeRF, demonstrating impressive 3D modeling capabilities while requiring only a fraction of the training and rendering time. In this paper, we show how the standard Gaussian splatting framework can be adapted for remote sensing, retaining its high efficiency. This enables us to achieve state-of-the-art performance in just a few minutes, compared to the day-long optimization required by the best-performing NeRF-based Earth observation methods. The proposed framework incorporates remote-sensing improvements from EO-NeRF, such as radiometric correction and shadow modeling, while introducing novel components, including sparsity, view consistency, and opacity regularizations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Aira_Gaussian_Splatting_for_Efficient_Satellite_Image_Photogrammetry_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Aira_Gaussian_Splatting_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Aira_2025_CVPR,
    author    = {Aira, Luca Savant and Facciolo, Gabriele and Ehret, Thibaud},
    title     = {Gaussian Splatting for Efficient Satellite Image Photogrammetry},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5959-5969}
}

raw_affiliation:
[{"affiliation": "Politecnico di T orino", "city": "Turin", "country": "Italy"}, {"affiliation": "Universite Paris-Saclay", "city": "Gif-sur-Yvette", "country": "France"}, {"affiliation": "AMIAD, P \u02c6ole Recherche", "city": "France", "country": "France"}]


title: Unified Reconstruction of Static and Dynamic Scenes from Events
authors: Qiyao Gao, Peiqi Duan, Hanyue Lou, Minggui Teng, Ziqi Cai, Xu Chen, Boxin Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27914-27923
cities: ["Beijing", "Beijing", "Seattle"]
countries: ["China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Unified_Reconstruction_of_Static_and_Dynamic_Scenes_from_Events_CVPR_2025_paper.html

abstract:
This paper addresses the challenge that current event-based video reconstruction methods cannot produce static background information. Recent research has uncovered the potential of event cameras in capturing static scenes. Nonetheless, image quality deteriorates due to noise interference and detail loss, failing to provide reliable background information. We propose a two-stage reconstruction strategy to address these challenges and reconstruct static scene images comparable to frame cameras. Building on this, we introduce the URSEE framework, the first unified framework designed for reconstructing motion videos with static backgrounds. This framework includes a parallel channel that can simultaneously process static and dynamic events, and a network module designed to reconstruct videos encompassing both static and dynamic scenes in an end-to-end manner. We also collect a real-captured dataset for static reconstruction, containing both indoor and outdoor scenes. Comparison results indicate that the proposed approach achieves state-of-the-art reconstruction results on both synthetic and real data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Unified_Reconstruction_of_Static_and_Dynamic_Scenes_from_Events_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Unified_Reconstruction_of_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Qiyao and Duan, Peiqi and Lou, Hanyue and Teng, Minggui and Cai, Ziqi and Chen, Xu and Shi, Boxin},
    title     = {Unified Reconstruction of Static and Dynamic Scenes from Events},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27914-27923}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Mechatronics, Automation, and Control Systems Laboratory, University of Washington", "city": "Seattle", "country": "USA"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "Mechatronics, Automation, and Control Systems Laboratory, University of Washington"]


title: Automatic Spectral Calibration of Hyperspectral Images: Method, Dataset and Benchmark
authors: Zhuoran Du, Shaodi You, Cheng Cheng, Shikui Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28081-28090
cities: ["Amsterdam", "Beijing"]
countries: ["Netherlands", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_Automatic_Spectral_Calibration_of_Hyperspectral_Images_Method_Dataset_and_Benchmark_CVPR_2025_paper.html

abstract:
Hyperspectral images (HSI) densely sample the world in both the space and frequency domains and, therefore, are more distinctive than RGB images. Usually, HSI needs to be calibrated to minimize the impact of various illumination conditions. The traditional way to calibrate HSI utilizes a physical reference, which involves manual operations, occlusions, and/or limits camera mobility. These limitations inspire this paper to automatically calibrate HSIs using a learning-based method. Towards this goal, a large-scale HSI calibration dataset, which has 765 high-quality HSI pairs covering diversified natural scenes and illuminations, is created. The dataset is further expanded to 7650 pairs by combining with 10 different physically measured illuminations. A spectral illumination transformer (SIT) together with an illumination attention module is proposed. Extensive benchmarks demonstrate the SoTA performance of the proposed SIT. The benchmarks also indicate that low-light conditions are more challenging than normal conditions. The dataset and codes are available online: https://github.com/duranze/Automatic-spectral-calibration-of-HSI.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_Automatic_Spectral_Calibration_of_Hyperspectral_Images_Method_Dataset_and_Benchmark_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_Automatic_Spectral_Calibration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Zhuoran and You, Shaodi and Cheng, Cheng and Wei, Shikui},
    title     = {Automatic Spectral Calibration of Hyperspectral Images: Method, Dataset and Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28081-28090}
}

raw_affiliation:
[{"affiliation": "University of Amsterdam, Netherlands", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "Visual Intelligence +X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University", "city": "Beijing", "country": "China"}]

affiliations:
["University of Amsterdam, Netherlands", "Visual Intelligence +X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University"]


title: Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation
authors: Sayak Nag, Udita Ghosh, Calvin-Khang Ta, Sarosij Bose, Jiachen Li, Amit K. Roy-Chowdhury
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11676-11686
affiliations: ["University of California, Riverside, USA", "Dolby Laboratories, USA"]
cities: ["Riverside", "Unknown"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nag_Conformal_Prediction_and_MLLM_aided_Uncertainty_Quantification_in_Scene_Graph_CVPR_2025_paper.html

abstract:
Scene Graph Generation (SGG) aims to represent visual scenes by identifying objects and their pairwise relationships, providing a structured understanding of image content. However, inherent challenges like long-tailed class distributions and prediction variability necessitate uncertainty quantification in SGG for its practical viability. In this paper, we introduce a novel Conformal Prediction based framework, adaptive to any existing SGG method, for quantifying their predictive uncertainty by constructing well-calibrated prediction sets over their generated scene graphs. These scene graph prediction sets are designed to achieve statistically rigorous coverage guarantees under exchangeability assumptions. Additionally, to ensure the prediction sets contain the most practically interpretable scene graphs, we propose an effective MLLM-based post-processing strategy for selecting the most visually and semantically plausible scene graphs within each set. We show that our proposed approach can produce diverse possible scene graphs from an image, assess the reliability of SGG methods, and improve overall SGG performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nag_Conformal_Prediction_and_MLLM_aided_Uncertainty_Quantification_in_Scene_Graph_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nag_Conformal_Prediction_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nag_2025_CVPR,
    author    = {Nag, Sayak and Ghosh, Udita and Ta, Calvin-Khang and Bose, Sarosij and Li, Jiachen and Roy-Chowdhury, Amit K.},
    title     = {Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11676-11686}
}

raw_affiliation:
[{"affiliation": "University of California, Riverside, USA", "city": "Riverside", "country": "USA"}, {"affiliation": "Dolby Laboratories, USA", "city": "Unknown", "country": "USA"}]


title: Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting
authors: Wei Lin, Chenyang Zhao, Antoni B. Chan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29363-29373
affiliations: ["Department of Computer Science, City University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Point-to-Region_Loss_for_Semi-Supervised_Point-Based_Crowd_Counting_CVPR_2025_paper.html

abstract:
Point detection has been developed to locate pedestrians in crowded scenes by training a counter through a point-to-point (P2P) supervision scheme. Despite its excellent localization and counting performance, training a point-based counter still faces challenges concerning annotation labor: hundreds to thousands of points are required to annotate a single sample capturing a dense crowd. In this paper, we integrate point-based methods into a semi-supervised counting framework based on pseudo-labeling, enabling the training of a counter with only a few annotated samples supplemented by a large volume of pseudo-labeled data. However, during implementation, the training encounters issues as the confidence for pseudo-labels fails to be propagated to background pixels via the P2P. To tackle this challenge, we devise a point-specific activation map (PSAM) to visually interpret the phenomena occurring during the ill-posed training. Observations from the PSAM suggest that the feature map is excessively activated by the loss for unlabeled data, causing the decoder to misinterpret these over-activations as pedestrians. To mitigate this issue, we propose a point-to-region (P2R) scheme to substitute P2P, which segments out local regions rather than detects a point corresponding to a pedestrian for supervision. Consequently, pixels in the local region can share the same confidence with the corresponding pseudo points. Experimental results in both semi-supervised counting and unsupervised domain adaptation highlight the advantages of our method, illustrating P2R can resolve issues identified in PSAM. The code is available at  https://github.com/Elin24/P2RLoss.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Point-to-Region_Loss_for_Semi-Supervised_Point-Based_Crowd_Counting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Point-to-Region_Loss_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Wei and Zhao, Chenyang and Chan, Antoni B.},
    title     = {Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29363-29373}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning
authors: Buzhen Huang, Chen Li, Chongyang Xu, Dongyue Lu, Jinnan Chen, Yangang Wang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17475-17485
cities: ["Nanjing", "Singapore", "Chengdu", "Singapore", "Singapore"]
countries: ["China", "Singapore", "China", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Reconstructing_Close_Human_Interaction_with_Appearance_and_Proxemics_Reasoning_CVPR_2025_paper.html

abstract:
Due to visual ambiguities and inter-person occlusions, existing human pose estimation methods cannot recover plausible close interactions from in-the-wild videos. Even state-of-the-art large foundation models (e.g., SAM) cannot accurately distinguish human semantics in such challenging scenarios. In this work, we find that human appearance can provide a straightforward cue to address these obstacles. Based on this observation, we propose a dual-branch optimization framework to reconstruct accurate interactive motions with plausible body contacts constrained by human appearances, social proxemics, and physical laws. Specifically, we first train a diffusion model to learn the human proxemic behavior and pose prior knowledge. The trained network and two optimizable tensors are then incorporated into a dual-branch optimization framework to reconstruct human motions and appearances. Several constraints based on 3D Gaussians, 2D keypoints, and mesh penetrations are also designed to assist the optimization. With the proxemics prior and diverse constraints, our method is capable of estimating accurate interactions from in-the-wild videos captured in complex environments. We further build a dataset with pseudo ground-truth interaction annotations, which may promote future research on pose estimation and human behavior understanding. Experimental results on several benchmarks demonstrate that our method outperforms existing approaches. The code and data are available at https://www.buzhenhuang.com/works/CloseApp.html.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Reconstructing_Close_Human_Interaction_with_Appearance_and_Proxemics_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Reconstructing_Close_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Buzhen and Li, Chen and Xu, Chongyang and Lu, Dongyue and Chen, Jinnan and Wang, Yangang and Lee, Gim Hee},
    title     = {Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17475-17485}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "IHPC, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CFAR, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Southeast University", "National University of Singapore", "Sichuan University", "IHPC, Agency for Science, Technology and Research, Singapore", "CFAR, Agency for Science, Technology and Research, Singapore"]


authors: Guotao Liang, Baoquan Zhang, Zhiyuan Wen, Junteng Zhao, Yunming Ye, Kola Ye, Yao He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4060-4069
affiliations: ["Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory", "SiFar Company"]
cities: ["Shenzhen", "Shenzhen", "Unknown"]
countries: ["China", "China", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Towards_Improved_Text-Aligned_Codebook_Learning_Multi-Hierarchical_Codebook-Text_Alignment_with_Long_CVPR_2025_paper.html

title:
Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text

abstract:
Image quantization is a crucial technique in image generation, aimed at learning a codebook that encodes an image into a discrete token sequence. Recent advancements have seen researchers exploring learning multi-modal codebook (i.e., text-aligned codebook) by utilizing image caption semantics, aiming to enhance codebook performance in cross-modal tasks. However, existing image-text paired datasets exhibit a notable flaw in that the text descriptions tend to be overly concise, failing to adequately describe the images and provide sufficient semantic knowledge, resulting in limited alignment of text and codebook at a fine-grained level.In this paper, we propose a novel Text-Augmented Codebook Learning framework, named TA-VQ, which generates longer text for each image using the visual-language model for improved text-aligned codebook learning.However, the long text presents two key challenges: how to encode text and how to align codebook and text. To tackle two challenges, we propose to split the long text into multiple granularities for encoding, i.e., word, phrase, and sentence, so that the long text can be fully encoded without losing any key semantic knowledge. Following this, a hierarchical encoder and novel sampling-based alignment strategy are designed to achieve fine-grained codebook-text alignment. Additionally, our method can be seamlessly integrated into existing VQ models. Extensive experiments in reconstruction and various downstream tasks demonstrate its effectiveness compared to previous state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Towards_Improved_Text-Aligned_Codebook_Learning_Multi-Hierarchical_Codebook-Text_Alignment_with_Long_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Towards_Improved_Text-Aligned_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Guotao and Zhang, Baoquan and Wen, Zhiyuan and Zhao, Junteng and Ye, Yunming and Ye, Kola and He, Yao},
    title     = {Towards Improved Text-Aligned Codebook Learning: Multi-Hierarchical Codebook-Text Alignment with Long Text},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4060-4069}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "SiFar Company", "city": "Unknown", "country": "Unknown"}]


title: Parallel Sequence Modeling via Generalized Spatial Propagation Network
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4473-4483
affiliations: ["NVIDIA", "The University of Hong Kong", "University of California, San Diego"]
cities: ["Santa Clara", "Hong Kong", "San Diego"]
countries: ["USA", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Parallel_Sequence_Modeling_via_Generalized_Spatial_Propagation_Network_CVPR_2025_paper.html

authors:
Hongjun Wang, Wonmin Byeon, Jiarui Xu, Jinwei Gu, Ka Chun Cheung, Xiaolong Wang, Kai Han, Jan Kautz, Sifei Liu

abstract:
We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attention models, including transformers, linear attention, and state-space models like Mamba, process multi-dimensional data as 1D sequences, compromising spatial coherence and efficiency. GSPN overcomes these limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a unique line-scan approach. Central to GSPN is the Stability-Context Condition, which ensures stable, context-aware propagation across 2D sequences and reduces the effective sequence length to \sqrt N , significantly enhancing computational efficiency. With learnable, input-dependent weights and no reliance on positional embeddings, GSPN achieves superior spatial fidelity and state-of-the-art performance in vision tasks, including ImageNet classification, class-guided image generation, and text-to-image generation. Notably, GSPN accelerates SD-XL with softmax-attention by over 84xwhen generating 16K images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Parallel_Sequence_Modeling_via_Generalized_Spatial_Propagation_Network_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Parallel_Sequence_Modeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hongjun and Byeon, Wonmin and Xu, Jiarui and Gu, Jinwei and Cheung, Ka Chun and Wang, Xiaolong and Han, Kai and Kautz, Jan and Liu, Sifei},
    title     = {Parallel Sequence Modeling via Generalized Spatial Propagation Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4473-4483}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of California, San Diego", "city": "San Diego", "country": "USA"}]


title: Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
authors: Luke Rowe, Roger Girgis, Anthony Gosselin, Liam Paull, Christopher Pal, Felix Heide
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17207-17218
cities: ["Montreal", "Montreal", "Montreal", "Princeton", "Toronto", "Blacksburg"]
countries: ["Canada", "Canada", "Canada", "USA", "Canada", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rowe_Scenario_Dreamer_Vectorized_Latent_Diffusion_for_Generating_Driving_Simulation_Environments_CVPR_2025_paper.html

abstract:
We introduce Scenario Dreamer, a fully data-driven generative simulator for autonomous vehicle planning that generates both the initial traffic scene--comprising a lane graph and agent bounding boxes--and closed-loop agent behaviours. Existing methods for generating driving simulation environments encode the initial traffic scene as a rasterized image and, as such, require parameter-heavy networks that perform unnecessary computation due to many empty pixels in the rasterized scene. Moreover, we find that existing methods that employ rule-based agent behaviours lack diversity and realism. Scenario Dreamer instead employs a novel vectorized latent diffusion model for initial scene generation that directly operates on the vectorized scene elements and an autoregressive Transformer for data-driven agent behaviour simulation. Scenario Dreamer additionally supports scene extrapolation via diffusion inpainting, enabling the generation of unbounded simulation environments. Extensive experiments show that Scenario Dreamer outperforms existing generative simulators in realism and efficiency: the vectorized scene-generation base model achieves superior generation quality with around 2x fewer parameters, 6x lower generation latency, and 10x fewer GPU training hours compared to the strongest baseline. We confirm its practical utility by showing that reinforcement learning planning agents are more challenged in Scenario Dreamer environments than traditional non-generative simulation environments, especially on long and adversarial driving environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rowe_Scenario_Dreamer_Vectorized_Latent_Diffusion_for_Generating_Driving_Simulation_Environments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rowe_Scenario_Dreamer_Vectorized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rowe_2025_CVPR,
    author    = {Rowe, Luke and Girgis, Roger and Gosselin, Anthony and Paull, Liam and Pal, Christopher and Heide, Felix},
    title     = {Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17207-17218}
}

raw_affiliation:
[{"affiliation": "Mila", "city": "Montreal", "country": "Canada"}, {"affiliation": "Universit\u00b4e de Montr\u00b4eal", "city": "Montreal", "country": "Canada"}, {"affiliation": "Polytechnique Montr\u00b4eal", "city": "Montreal", "country": "Canada"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "CIFAR AI Chair", "city": "Toronto", "country": "Canada"}, {"affiliation": "Torc Robotics", "city": "Blacksburg", "country": "USA"}]

affiliations:
["Mila", "Universit\u00b4e de Montr\u00b4eal", "Polytechnique Montr\u00b4eal", "Princeton University", "CIFAR AI Chair", "Torc Robotics"]


title: Poly-Autoregressive Prediction for Modeling Interactions
authors: Neerja Thakkar, Tara Sadjadpour, Jathushan Rajasegeran, Shiry Ginosar, Jitendra Malik
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12402-12412
affiliations: ["UC Berkeley", "Toyota Technical Institute at Chicago", "Google DeepMind"]
cities: ["Berkeley", "Chicago", "Mountain View"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Thakkar_Poly-Autoregressive_Prediction_for_Modeling_Interactions_CVPR_2025_paper.html

abstract:
We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is on scenarios with multiple agents whose interactions are shaped by physical constraints and internal motivations. To this end, we propose Poly-Autoregressive (PAR) modeling, which forecasts an ego agent's future behavior by reasoning about the ego agent's state history and the past and current states of other interacting agents. At its core, PAR represents the behavior of all agents as a sequence of tokens, each representing an agent's state at a specific timestep. With minimal data pre-processing changes, we show that PAR can be applied to three different problems: human action forecasting in social situations, trajectory prediction for autonomous vehicles, and object pose forecasting during hand-object interaction. Using a small proof-of-concept transformer backbone, PAR outperforms AR across these three scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Thakkar_Poly-Autoregressive_Prediction_for_Modeling_Interactions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Thakkar_Poly-Autoregressive_Prediction_for_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Thakkar_2025_CVPR,
    author    = {Thakkar, Neerja and Sadjadpour, Tara and Rajasegeran, Jathushan and Ginosar, Shiry and Malik, Jitendra},
    title     = {Poly-Autoregressive Prediction for Modeling Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12402-12412}
}

raw_affiliation:
[{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Toyota Technical Institute at Chicago", "city": "Chicago", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


title: NADER: Neural Architecture Design via Multi-Agent Collaboration
authors: Zekang Yang, Wang Zeng, Sheng Jin, Chen Qian, Ping Luo, Wentao Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4452-4461
cities: ["Hong Kong", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_NADER_Neural_Architecture_Design_via_Multi-Agent_Collaboration_CVPR_2025_paper.html

abstract:
Designing effective neural architectures poses a significant challenge in deep learning. While Neural Architecture Search (NAS) automates the search for optimal architectures, existing methods are often constrained by predetermined search spaces and may miss critical neural architectures. In this paper, we introduce NADER (Neural Architecture Design via multi-agEnt collaboRation), a novel framework that formulates neural architecture design (NAD) as a LLM-based multi-agent collaboration problem. NADER employs a team of specialized agents to enhance a base architecture through iterative modification. Current LLM-based NAD methods typically operate independently, lacking the ability to learn from past experiences, which results in repeated mistakes and inefficient exploration. To address this issue, we propose the Reflector, which effectively learns from immediate feedback and long-term experiences. Additionally, unlike previous LLM-based methods that use code to represent neural architectures, we utilize a graph-based representation. This approach allows agents to focus on design aspects without being distracted by coding. We demonstrate the effectiveness of NADER in discovering high-performing architectures beyond predetermined search spaces through extensive experiments on benchmark tasks, showcasing its advantages over state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_NADER_Neural_Architecture_Design_via_Multi-Agent_Collaboration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_NADER_Neural_Architecture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zekang and Zeng, Wang and Jin, Sheng and Qian, Chen and Luo, Ping and Liu, Wentao},
    title     = {NADER: Neural Architecture Design via Multi-Agent Collaboration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4452-4461}
}

raw_affiliation:
[{"affiliation": "SenseTime Research and Tetras.AI", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]

affiliations:
["SenseTime Research and Tetras.AI", "The University of Hong Kong", "HKU Shanghai Intelligent Computing Research Center"]


title: Move-in-2D: 2D-Conditioned Human Motion Generation
authors: Hsin-Ping Huang, Yang Zhou, Jui-Hsien Wang, Difan Liu, Feng Liu, Ming-Hsuan Yang, Zhan Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22766-22775
affiliations: ["Adobe Research", "University of California, Merced"]
cities: ["San Jose", "Merced"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Move-in-2D_2D-Conditioned_Human_Motion_Generation_CVPR_2025_paper.html

abstract:
Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which restricts applications to specific motion types and global scene matching. We propose Move-in-2D, a novel approach to generate human motion sequences conditioned on a scene image, allowing for diverse motion that adapts to different scenes. Our approach utilizes a diffusion model that accepts both a scene image and text prompt as inputs, producing a motion sequence tailored to the scene. To train this model, we collect a large-scale video dataset featuring single-human activities, annotating each video with the corresponding human motion as the target output. Experiments demonstrate that our method effectively predicts human motion that aligns with the scene image after projection. Furthermore, we show that the generated motion sequence improves human motion quality in video synthesis tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Move-in-2D_2D-Conditioned_Human_Motion_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Hsin-Ping and Zhou, Yang and Wang, Jui-Hsien and Liu, Difan and Liu, Feng and Yang, Ming-Hsuan and Xu, Zhan},
    title     = {Move-in-2D: 2D-Conditioned Human Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22766-22775}
}

raw_affiliation:
[{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}]


title: PoseBH: Prototypical Multi-Dataset Training Beyond Human Pose Estimation
authors: Uyoung Jeong, Jonathan Freer, Seungryul Baek, Hyung Jin Chang, Kwang In Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12278-12288
affiliations: ["UNIST", "University of Birmingham", "POSTECH"]
cities: ["Ulsan", "Birmingham", "Pohang"]
countries: ["South Korea", "UK", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_PoseBH_Prototypical_Multi-Dataset_Training_Beyond_Human_Pose_Estimation_CVPR_2025_paper.html

abstract:
We study multi-dataset training (MDT) for pose estimation, where skeletal heterogeneity presents a unique challenge that existing methods have yet to address. In traditional domains, e.g. regression and classification, MDT typically relies on dataset merging or multi-head supervision. However, the diversity of skeleton types and limited cross-dataset supervision complicate integration in pose estimation. To address these challenges, we introduce PoseBH, a new MDT framework that tackles keypoint heterogeneity and limited supervision through two key techniques. First, we propose nonparametric keypoint prototypes that learn within a unified embedding space, enabling seamless integration across skeleton types. Second, we develop a cross-type self-supervision mechanism that aligns keypoint predictions with keypoint embedding prototypes, providing supervision without relying on teacher-student models or additional augmentations. PoseBH substantially improves generalization across whole-body and animal pose datasets, including COCO-WholeBody, AP-10K, and APT-36K, while preserving performance on standard human pose benchmarks (COCO, MPII, and AIC). Furthermore, our learned keypoint embeddings transfer effectively to hand shape estimation (InterHand2.6M) and human body shape estimation (3DPW). The code for PoseBH is available at: `https://github.com/uyoung-jeong/PoseBH`.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_PoseBH_Prototypical_Multi-Dataset_Training_Beyond_Human_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_PoseBH_Prototypical_Multi-Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Uyoung and Freer, Jonathan and Baek, Seungryul and Chang, Hyung Jin and Kim, Kwang In},
    title     = {PoseBH: Prototypical Multi-Dataset Training Beyond Human Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12278-12288}
}

raw_affiliation:
[{"affiliation": "UNIST", "city": "Ulsan", "country": "South Korea"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "UK"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]


title: MATCHA: Towards Matching Anything
authors: Fei Xue, Sven Elflein, Laura Leal-Taixé, Qunjie Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27081-27091
affiliations: ["University of Cambridge", "University of Toronto", "NVIDIA", "Vector Institute"]
cities: ["Cambridge", "Toronto", "Santa Clara", "Toronto"]
countries: ["United Kingdom", "Canada", "USA", "Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_MATCHA_Towards_Matching_Anything_CVPR_2025_paper.html

abstract:
Establishing correspondences across images is a fundamental challenge in computer vision, underpinning tasks like Structure-from-Motion, image editing, and point tracking. Traditional methods are often specialized for specific correspondence types, geometric, semantic, or temporal, whereas humans naturally identify alignments across these domains. Inspired by this flexibility, we propose MATCHA, a unified feature model designed to "rule them all", establishing robust correspondences across diverse matching tasks. Building on insights that diffusion model features can encode multiple correspondence types, MATCHA augments this capacity by dynamically fusing high-level semantic and low-level geometric features through an attention-based module, creating expressive, versatile, and robust features. Additionally, MATCHA integrates object-level features from DINOv2 to further boost generalization, enabling a single feature capable of matching anything. Extensive experiments validate that MATCHA consistently surpasses state-of-the-art methods across geometric, semantic, and temporal matching tasks, setting a new foundation for a unified approach for the fundamental correspondence problem in computer vision. To the best of our knowledge, MATCHA is the first approach that is able to effectively tackle diverse matching tasks with a single unified feature. Project page: https://github.com/feixue94/matcha.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_MATCHA_Towards_Matching_Anything_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_MATCHA_Towards_Matching_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Fei and Elflein, Sven and Leal-Taix\'e, Laura and Zhou, Qunjie},
    title     = {MATCHA: Towards Matching Anything},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27081-27091}
}

raw_affiliation:
[{"affiliation": "University of Cambridge", "city": "Cambridge", "country": "United Kingdom"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}]


title: CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion
authors: Kai He, Chin-Hsuan Wu, Igor Gilitschenski
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26630-26640
raw_affiliation: [{"affiliation": "University of Toronto Vector Institute", "city": "Toronto", "country": "Canada"}]
affiliations: ["University of Toronto Vector Institute"]
cities: ["Toronto"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_CTRL-D_Controllable_Dynamic_3D_Scene_Editing_with_Personalized_2D_Diffusion_CVPR_2025_paper.html

abstract:
Achieving controllable and consistent editing in dynamic 3D scenes remains a significant challenge. Previous work is largely constrained by its editing backbones, resulting in inconsistent edits and limited controllability. We propose to address this challenge using personalized diffusion models. In our work, we introduce a novel framework that first fine-tunes the InstructPix2Pix model, followed by a two-stage optimization of the scene based on deformable 3D Gaussians. The fine-tuning enables the model to "learn" the editing ability from a single edited reference image. This reduces the more complex task of dynamic scene editing to the more simple 2D image editing problem. By directly learning editing regions and styles from the reference, our approach enables consistent and precise local edits without the need for tracking desired editing regions, effectively addressing key challenges in dynamic scene editing. Then, our two-stage optimization progressively edits the trained dynamic scene, using a designed edited image buffer to accelerate convergence and improve temporal consistency. Compared to state-of-the-art methods, our approach offers more flexible and controllable local scene editing, achieving high-quality and consistent results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_CTRL-D_Controllable_Dynamic_3D_Scene_Editing_with_Personalized_2D_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_CTRL-D_Controllable_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Kai and Wu, Chin-Hsuan and Gilitschenski, Igor},
    title     = {CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26630-26640}
}


authors: Zhen Yang, Zhuo Tao, Qi Chen, Liang Li, Yuankai Qi, Anton van den Hengel, Qingming Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24753-24762
cities: ["Beijing", "Beijing", "Adelaide", "Sydney"]
countries: ["China", "China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Separation_of_Powers_On_Segregating_Knowledge_from_Observation_in_LLM-enabled_CVPR_2025_paper.html

title:
Separation of Powers: On Segregating Knowledge from Observation in LLM-enabled Knowledge-based Visual Question Answering

abstract:
Knowledge-based visual question answering (KBVQA) separates image interpretation and knowledge retrieval into separate processes, motivated in part by the fact that they are very different tasks. In this paper, we transform the KBVQA into linguistic question-answering tasks so that we can leverage the rich world knowledge and strong reasoning abilities of Large Language Models (LLMs). The caption-then-question approach to KBVQA has been effective but relies on the captioning method to describe the detail required to answer every possible question. We propose instead a Question-Aware Captioner (QACap), which uses the question as guidance to extract correlated visual information from the image and generate a question-related caption. To train such a model, we utilize GPT-4 to build a corresponding high-quality question-aware caption dataset on top of existing KBVQA datasets. Extensive experiments demonstrate that our QACap model and dataset significantly improve KBVQA performance. Our method, QACap, achieves 68.2% accuracy on the OKVQA validation set, 73.4% on the direct-answer part of the A-OKVQA validation set, and 74.8% on the multiple-choice part, all setting new SOTA benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Separation_of_Powers_On_Segregating_Knowledge_from_Observation_in_LLM-enabled_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhen and Tao, Zhuo and Chen, Qi and Li, Liang and Qi, Yuankai and van den Hengel, Anton and Huang, Qingming},
    title     = {Separation of Powers: On Segregating Knowledge from Observation in LLM-enabled Knowledge-based Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24753-24762}
}

raw_affiliation:
[{"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Macquarie University", "city": "Sydney", "country": "Australia"}]

affiliations:
["Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "University of Adelaide", "Macquarie University"]


title: Decision SpikeFormer: Spike-Driven Transformer for Decision Making
authors: Wei Huang, Qinying Gu, Nanyang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19241-19250
affiliations: ["Shanghai AI Laboratory", "Wuhan University", "Shanghai Jiao Tong University"]
cities: ["Shanghai", "Wuhan", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Decision_SpikeFormer_Spike-Driven_Transformer_for_Decision_Making_CVPR_2025_paper.html

abstract:
Offline reinforcement learning (RL) enables policy training solely on pre-collected data, avoiding direct environment interaction--a crucial benefit for energy-constrained embodied AI applications. Although Artificial Neural Networks (ANN)-based methods perform well in offline RL, their high computational and energy demands motivate exploration of more efficient alternatives. Spiking Neural Networks (SNNs) show promise for such tasks, given their low power consumption. In this work, we introduce DSFormer, the first spike-driven transformer model designed to tackle offline RL via sequence modeling. Unlike existing SNN transformers focused on spatial dimensions for vision tasks, we develop Temporal Spiking Self-Attention (TSSA) and Positional Spiking Self-Attention (PSSA) in DSFormer to capture the temporal and positional dependencies essential for sequence modeling in RL. Additionally, we propose Progressive Threshold-dependent Batch Normalization (PTBN), which combines the benefits of LayerNorm and BatchNorm to preserve temporal dependencies while maintaining the spiking nature of SNNs. Comprehensive results in the D4RL benchmark show DSFormer's superiority over both SNN and ANN counterparts, achieving 78.4% energy savings, highlighting DSFormer's advantages not only in energy efficiency but also in competitive performance. Code and models are public at \href https://wei-nijuan.github.io/DecisionSpikeFormer/  project page .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Decision_SpikeFormer_Spike-Driven_Transformer_for_Decision_Making_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Decision_SpikeFormer_Spike-Driven_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Wei and Gu, Qinying and Ye, Nanyang},
    title     = {Decision SpikeFormer: Spike-Driven Transformer for Decision Making},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19241-19250}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding
authors: Yangliu Hu, Zikai Song, Na Feng, Yawei Luo, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29108-29117
affiliations: ["Huazhong University of Science and Technology", "Zhejiang University", "La Trobe University"]
cities: ["Wuhan", "Hangzhou", "Melbourne"]
countries: ["China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_SF2T_Self-supervised_Fragment_Finetuning_of_Video-LLMs_for_Fine-Grained_Understanding_CVPR_2025_paper.html

abstract:
Video-based Large Language Models (Video-LLMs) have witnessed substantial advancements in recent years, propelled by the advancement in multi-modal LLMs. Although these models have demonstrated proficiency in providing the overall description of videos, they struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries. To tackle these shortcomings, we find that fine-tuning Video-LLMs on self-supervised fragment tasks, greatly improve their fine-grained video understanding abilities. Hence we propose two key contributions:(1) Self-Supervised Fragment Fine-Tuning (SF^2T), a novel effortless fine-tuning method, employs the rich inherent characteristics of videos for training, while unlocking more fine-grained understanding ability of Video-LLMs. Moreover, it relieves researchers from labor-intensive annotations and smartly circumvents the limitations of natural language, which often fails to capture the complex spatiotemporal variations in videos;(2) A novel benchmark dataset, namely FineVidBench, for rigorously assessing Video-LLMs' performance at both the scene and fragment levels, offering a comprehensive evaluation of their capabilities.We assessed multiple models and validated the effectiveness of SF^2T on them. Experimental results reveal that our approach improves their ability to capture and interpret spatiotemporal details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_SF2T_Self-supervised_Fragment_Finetuning_of_Video-LLMs_for_Fine-Grained_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_SF2T_Self-supervised_Fragment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Yangliu and Song, Zikai and Feng, Na and Luo, Yawei and Yu, Junqing and Chen, Yi-Ping Phoebe and Yang, Wei},
    title     = {SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29108-29117}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "La Trobe University", "city": "Melbourne", "country": "Australia"}]


title: Theory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels
authors: Quanjiang Li, Tingjin Luo, Jiahui Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20706-20715
affiliations: ["National University of Defense Technology"]
cities: ["Changsha"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Theory-Inspired_Deep_Multi-View_Multi-Label_Learning_with_Incomplete_Views_and_Noisy_CVPR_2025_paper.html

abstract:
Incomplete features and label noise in multi-view multi-label data significantly undermine the reliability and performance, motivating researchers to explore the mechanism of representation and information recovery. However, learning for such dual deficiencies is crucial but rarely studied.  In this paper, we propose a theory-inspired Deep Multi-View Multi-Label Learning method with Incomplete Views and Noisy Labels named DMMIvNL to address these problems. Specifically, to promote the synthesis of task-relevant shared information and preserve the distinctiveness of individual features from limited views, we have developed a feature extraction modular based on the information bottleneck theory, and formulated its theoretical upper bound into its objective. Meanwhile, we theoretically prove that minimizing the volume of the transition matrix ensures the statistical consistency with classifier training. Besides, a cycle-consistency estimation principle is proposed in the volume minimization network to improve the recognition stability of multi-label noise. Moreover, leveraging inherent real semantics information and label correlations are employed as model regularization to reduce the risk of excessive noise fitting. Finally, extensive experimental results validate the effectiveness and robustness of our DMMIvNL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Theory-Inspired_Deep_Multi-View_Multi-Label_Learning_with_Incomplete_Views_and_Noisy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Theory-Inspired_Deep_Multi-View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Quanjiang and Luo, Tingjin and Liao, Jiahui},
    title     = {Theory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20706-20715}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}]


title: Fitted Neural Lossless Image Compression
authors: Zhe Zhang, Zhenzhong Chen, Shan Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23249-23258
affiliations: ["Wuhan University", "Tencent Media Lab"]
cities: ["Wuhan", "unknown"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Fitted_Neural_Lossless_Image_Compression_CVPR_2025_paper.html

abstract:
Neural lossless image compression methods have recently achieved impressive compression ratios by fitting neural networks to represent data distributions of large datasets. However, these methods often require complex networks to capture intricate data distributions effectively, resulting in high decoding complexity. In this paper, we present a novel approach named Fitted Neural Lossless Image Compression (FNLIC) that enhances efficiency through a two-phase fitting process. For each image, a latent variable model is overfitted to optimize the representation of the individual image's probability distribution, which is inherently simpler than the distribution of an entire dataset and requires less complex neural networks. Additionally, we pre-fit a lightweight autoregressive model on a comprehensive dataset to learn a beneficial prior for overfitted models. To improve coordination between the pre-fitting and overfitting phases, we introduce independent fitting for the pre-fitter and the adaptive prior transformation for the overfitted model. Extensive experimental results on high-resolution datasets show that FNLIC achieves competitive compression ratios compared to both traditional and neural lossless image compression methods, with decoding complexity significantly lower than other neural methods of similar performance. The code is at https://github.com/ZZ022/FNLIC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Fitted_Neural_Lossless_Image_Compression_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Fitted_Neural_Lossless_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhe and Chen, Zhenzhong and Liu, Shan},
    title     = {Fitted Neural Lossless Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23249-23258}
}

raw_affiliation:
[{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Tencent Media Lab", "city": "unknown", "country": "USA"}]


authors: K Naveen Kumar, Ranjeet Ranjan Jha, C Krishna Mohan, Ravindra Babu Tallamraju
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4999-5009
affiliations: ["IIT Hyderabad", "IIT Patna", "Sahaj AI Software Pvt Ltd."]
cities: ["Hyderabad", "Patna", "Unknown"]
countries: ["India", "India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.html

title:
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution

abstract:
Ensuring auditability and verifiability in FL is both challenging and essential to guarantee that local data remains untampered and client updates are trustworthy. Recent FL frameworks assess client contributions through a trusted central server using various client selection and aggregation techniques. However, reliance on a central server can create a single point of failure, making it vulnerable to privacy-centric attacks and limiting its ability to audit and verify client-side data contributions due to restricted access. In addition, data quality and fairness evaluations are often inadequate, failing to distinguish between high-impact contributions and those from low-quality or poisoned data. To address these challenges, we propose Federated Auditable and Verifiable Data valuation (FAVD), a privacy-preserving method that ensures auditability and verifiability of client contributions through data valuation, independent of any central authority or predefined training algorithm. FAVD utilizes shared local data density functions to construct a global density function, aligning data contributions and facilitating effective valuation prior to local model training. This proactive approach improves transparency in data valuation and ensures that only benign updates are generated, even in the presence of malicious data. Further, to mitigate privacy risks associated with sharing data density functions, we add Gaussian noise to each client's local density function before sharing it with the server. We theoretically demonstrate the convergence, auditability, and verifiability of FAVD, along with its resilience against data poisoning threats. Our experiments on five diverse benchmarks, including three medical datasets, show that FAVD achieves significant performance gains, accurate data valuation, and fair client contributions under threat, highlighting its reliability as a trustworthy FL approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumar_Fortifying_Federated_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kumar_2025_CVPR,
    author    = {Kumar, K Naveen and Jha, Ranjeet Ranjan and Mohan, C Krishna and Tallamraju, Ravindra Babu},
    title     = {Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4999-5009}
}

raw_affiliation:
[{"affiliation": "IIT Hyderabad", "city": "Hyderabad", "country": "India"}, {"affiliation": "IIT Patna", "city": "Patna", "country": "India"}, {"affiliation": "Sahaj AI Software Pvt Ltd.", "city": "Unknown", "country": "India"}]


title: EMOE: Modality-Specific Enhanced Dynamic Emotion Experts
authors: Yiyang Fang, Wenke Huang, Guancheng Wan, Kehua Su, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14314-14324
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_EMOE_Modality-Specific_Enhanced_Dynamic_Emotion_Experts_CVPR_2025_paper.html

abstract:
Multimodal Emotion Recognition (MER) aims to predict human emotions by leveraging multiple modalities, such as vision, acoustics, and language. However, due to the heterogeneity of these modalities, MER faces two key challenges: modality balance dilemma and modality specialization disappearance. Existing methods often overlook the varying importance of modalities across samples in tackling the modality balance dilemma. Moreover, mainstream decoupling methods, while preserving modality-specific information, often neglect the predictive capability of unimodal data. To address these, we propose a novel model, Modality-Specific Enhanced Dynamic Emotion Experts (EMOE), consisting of: (1) Mixture of Modality Experts for dynamically adjusting modality importance based on sample features, and (2) Unimodal Distillation to retain single-modality predictive ability within fused features. EMOE enables adaptive fusion by learning a unique modality weight distribution for each sample, enhancing multimodal predictions with single-modality predictions to balance invariant and specific features in emotion recognition. Experimental results on benchmark datasets show that EMOE achieves superior or comparable performance to state-of-the-art methods. Additionally, we extend EMOE to Multimodal Intent Recognition (MIR), further demonstrating its effectiveness and versatility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_EMOE_Modality-Specific_Enhanced_Dynamic_Emotion_Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_EMOE_Modality-Specific_Enhanced_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Yiyang and Huang, Wenke and Wan, Guancheng and Su, Kehua and Ye, Mang},
    title     = {EMOE: Modality-Specific Enhanced Dynamic Emotion Experts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14314-14324}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University.", "city": "Wuhan", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University."]


title: JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22369-22380
cities: ["Xiamen", "Guangzhou", "Unknown", "Shenzhen", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_JarvisIR_Elevating_Autonomous_Driving_Perception_with_Intelligent_Image_Restoration_CVPR_2025_paper.html

authors:
Yunlong Lin, Zixu Lin, Haoyu Chen, Panwang Pan, Chenxin Li, Sixiang Chen, Kairun Wen, Yeying Jin, Wenbo Li, Xinghao Ding

abstract:
Vision-centric perception systems often struggle with unpredictable and coupled weather degradations in the wild. Current solutions are often limited, as they either depend on specific degradation priors or suffer from significant domain gaps. To enable robust and autonomous operation in real-world conditions, we propose JarvisIR, a VLM-powered agent that leverages the VLM (e.g., Llava-Llama3) as a controller to manage multiple expert restoration models. To further enhance system robustness, reduce hallucinations, and improve generalizability in real-world adverse weather, JarvisIR employs a novel two-stage framework consisting of supervised fine-tuning and human feedback alignment. Specifically, to address the lack of paired data in real-world scenarios, the human feedback alignment enables the VLM to be fine-tuned effectively on large-scale real-world data in an unsupervised manner. To support the training and evaluation of JarvisIR, we introduce CleanBench, a comprehensive dataset consisting of high-quality and large-scale instruction-responses pairs, including 150K synthetic entries and 80K real entries. Extensive experiments demonstrate that JarvisIR exhibits superior decision-making and restoration capabilities. Compared with existing methods, it achieves a 50% improvement in the average of all perception metrics on CleanBench-Real.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_JarvisIR_Elevating_Autonomous_Driving_Perception_with_Intelligent_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_JarvisIR_Elevating_Autonomous_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Yunlong and Lin, Zixu and Chen, Haoyu and Pan, Panwang and Li, Chenxin and Chen, Sixiang and Wen, Kairun and Jin, Yeying and Li, Wenbo and Ding, Xinghao},
    title     = {JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22369-22380}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Bytedance\u2019s Pico", "city": "Unknown", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "The Hong Kong University of Science and Technology (Guangzhou)", "Bytedance\u2019s Pico", "Tencent", "Huawei Noah\u2019s Ark Lab", "The Chinese University of Hong Kong"]


title: UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting
authors: Ziyi Wang, Yanran Zhang, Jie Zhou, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1319-1329
affiliations: ["Department of Automation, Tsinghua University, China"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_UniPre3D_Unified_Pre-training_of_3D_Point_Cloud_Models_with_Cross-Modal_CVPR_2025_paper.html

abstract:
The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D models, and no existing pre-training method is equally effective for both object- and scene-level point clouds. In this paper, we introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture. Our approach predicts Gaussian primitives as the pre-training task and employs differentiable Gaussian splatting to render images, enabling precise pixel-level supervision and end-to-end optimization. To further regulate the complexity of the pre-training task and direct the model's focus toward geometric structures, we integrate 2D features from pre-trained image models to incorporate well-established texture knowledge. We validate the universal effectiveness of our proposed method through extensive experiments across a variety of object- and scene-level tasks, using diverse point cloud models as backbones. Code is available at https://github.com/wangzy22/UniPre3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_UniPre3D_Unified_Pre-training_of_3D_Point_Cloud_Models_with_Cross-Modal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_UniPre3D_Unified_Pre-training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ziyi and Zhang, Yanran and Zhou, Jie and Lu, Jiwen},
    title     = {UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1319-1329}
}

raw_affiliation:
[{"affiliation": "Department of Automation, Tsinghua University, China", "city": "Beijing", "country": "China"}]


title: Charm: The Missing Piece in ViT Fine-Tuning for Image Aesthetic Assessment
authors: Fatemeh Behrad, Tinne Tuytelaars, Johan Wagemans
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7815-7824
raw_affiliation: [{"affiliation": "KU Leuven University", "city": "Leuven", "country": "Belgium"}]
affiliations: ["KU Leuven University"]
cities: ["Leuven"]
countries: ["Belgium"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Behrad_Charm_The_Missing_Piece_in_ViT_Fine-Tuning_for_Image_Aesthetic_CVPR_2025_paper.html

abstract:
The capacity of Vision transformers (ViTs) to handle variable-sized inputs is often constrained by computational complexity and batch processing limitations. Consequently, ViTs are typically trained on small, fixed-size images obtained through downscaling or cropping. While reducing computational burden, these methods result in significant information loss, negatively affecting tasks like image aesthetic assessment. We introduce Charm, a novel tokenization approach that preserves Composition, High-resolution, Aspect Ratio, and Multi-scale information simultaneously. Charm prioritizes high-resolution details in specific regions while downscaling others, enabling shorter fixed-size input sequences for ViTs while incorporating essential information. Charm is designed to be compatible with pre-trained ViTs and their learned positional embeddings. By providing multiscale input and introducing variety to input tokens, Charm improves ViT performance and generalizability for image aesthetic assessment. We avoid cropping or changing the aspect ratio to further preserve information. Extensive experiments demonstrate significant performance improvements on various image aesthetic and quality assessment datasets (up to 8.1%) using a lightweight ViT backbone. Code and pre-trained models are available at https://github.com/FBehrad/Charm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Behrad_Charm_The_Missing_Piece_in_ViT_Fine-Tuning_for_Image_Aesthetic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Behrad_Charm_The_Missing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Behrad_2025_CVPR,
    author    = {Behrad, Fatemeh and Tuytelaars, Tinne and Wagemans, Johan},
    title     = {Charm: The Missing Piece in ViT Fine-Tuning for Image Aesthetic Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7815-7824}
}


title: F-LMM: Grounding Frozen Large Multimodal Models
authors: Size Wu, Sheng Jin, Wenwei Zhang, Lumin Xu, Wentao Liu, Wei Li, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24710-24721
cities: ["Singapore", "Shanghai", "Shanghai", "Hong Kong"]
countries: ["Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_F-LMM_Grounding_Frozen_Large_Multimodal_Models_CVPR_2025_paper.html

abstract:
Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs' understanding of the visual world and their interaction with humans. However, existing methods typically fine-tune the parameters of LMMs to learn additional segmentation tokens and overfit grounding and segmentation datasets. Such a design would inevitably cause a catastrophic diminution in the indispensable conversational capability of general AI assistants. In this paper, we comprehensively evaluate state-of-the-art grounding LMMs across a suite of multimodal question-answering benchmarks, observing drastic performance drops that indicate vanishing general knowledge comprehension and weakened instruction following ability. To address this issue, we present F-LMM---grounding frozen off-the-shelf LMMs in human-AI conversations---a straightforward yet effective design based on the fact that word-pixel correspondences conducive to visual grounding inherently exist in the attention mechanism of well-trained LMMs. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits, which a SAM-based mask refiner can further optimise. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data, but achieves competitive performance on referring expression segmentation and panoptic narrative grounding benchmarks while completely preserving LMMs' original conversational ability. Additionally, with instruction-following ability preserved and grounding ability obtained, F-LMM can be directly applied to complex tasks like reasoning segmentation, grounded conversation generation and visual chain-of-thought reasoning. Our code can be found at https://github.com/wusize/F-LMM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_F-LMM_Grounding_Frozen_Large_Multimodal_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_F-LMM_Grounding_Frozen_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Size and Jin, Sheng and Zhang, Wenwei and Xu, Lumin and Liu, Wentao and Li, Wei and Loy, Chen Change},
    title     = {F-LMM: Grounding Frozen Large Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24710-24721}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SenseTime Research and Tetras.AI", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["S-Lab, Nanyang Technological University", "SenseTime Research and Tetras.AI", "Shanghai AI Laboratory", "The Chinese University of Hong Kong"]


title: EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation and Completion
authors: Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28274-28283
cities: ["Guangzhou", "Beijing", "Guangzhou", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.html

abstract:
This paper presents EntityErasure, a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable sundries. Moreover, we develop two entity segmentation based metrics for quantitatively assessing the performance of object erasure, which are shown be more effective than existing metrics. Experimental results demonstrate that our approach outperforms other state-of-the-art object erasure methods. Our code and data are available at https://zyxunh.github.io/EntityErasure-ProjectPage/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_EntityErasure_Erasing_Entity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yixing and Zhang, Qing and Wang, Yitong and Nie, Yongwei and Zheng, Wei-Shi},
    title     = {EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation and Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28274-28283}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "ByteDance Inc", "city": "Beijing", "country": "China"}, {"affiliation": "South China University of Technology, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "ByteDance Inc", "South China University of Technology, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: Generative Video Propagation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17712-17722
cities: ["Hong Kong", "San Jose", "Hong Kong", "Hong Kong"]
countries: ["China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Generative_Video_Propagation_CVPR_2025_paper.html

authors:
Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang, Qing Liu, Zhifei Zhang, Joon-Young Lee, Yijun Li, Bei Yu, Zhe Lin, Soo Ye Kim, Jiaya Jia

abstract:
Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be addressed in a unified way by leveraging the generative power of such models. Specifically, our framework, GenProp, encodes the original video with a selective content encoder and propagates the changes made to the first frame using an image-to-video generation model. We propose a data generation scheme to cover multiple video tasks based on instance-level video segmentation datasets. Our model is trained by incorporating a mask prediction decoder head and optimizing a region-aware loss to aid the encoder to preserve the original content while the generation model propagates the modified region. This novel design opens up new possibilities: In editing scenarios, GenProp allows substantial changes to an object's shape; for insertion, the inserted objects can exhibit independent motion; for removal, GenProp effectively removes effects like shadows and reflections from the whole video; for tracking, GenProp is capable of tracking objects and their associated effects together. Experiment results demonstrate the leading performance of our model in various video tasks, and we further provide in-depth analyses of the proposed framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Generative_Video_Propagation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Generative_Video_Propagation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Shaoteng and Wang, Tianyu and Wang, Jui-Hsien and Liu, Qing and Zhang, Zhifei and Lee, Joon-Young and Li, Yijun and Yu, Bei and Lin, Zhe and Kim, Soo Ye and Jia, Jiaya},
    title     = {Generative Video Propagation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17712-17722}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "SmartMore", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Chinese University of Hong Kong", "Adobe Research", "The Hong Kong University of Science and Technology", "SmartMore"]


title: From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10644-10655
affiliations: ["Apple", "Georgia Tech"]
cities: ["Cupertino", "Atlanta"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Szot_From_Multimodal_LLMs_to_Generalist_Embodied_Agents_Methods_and_Lessons_CVPR_2025_paper.html

authors:
Andrew Szot, Bogdan Mazoure, Omar Attia, Aleksei Timofeev, Harsh Agrawal, Devon Hjelm, Zhe Gan, Zsolt Kira, Alexander Toshev

abstract:
We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typically trained on. Specifically, our focus lies in areas such as Embodied AI, Games, UI Control, and Planning. To this end, we introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA). GEA is a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained with supervised learning on a large dataset of embodied experiences and with online RL in interactive simulators. We explore the data and algorithmic choices necessary to develop such a model. Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents. The final GEA model achieves strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Szot_From_Multimodal_LLMs_to_Generalist_Embodied_Agents_Methods_and_Lessons_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Szot_From_Multimodal_LLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Szot_2025_CVPR,
    author    = {Szot, Andrew and Mazoure, Bogdan and Attia, Omar and Timofeev, Aleksei and Agrawal, Harsh and Hjelm, Devon and Gan, Zhe and Kira, Zsolt and Toshev, Alexander},
    title     = {From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10644-10655}
}

raw_affiliation:
[{"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "Georgia Tech", "city": "Atlanta", "country": "USA"}]


title: Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation
authors: Junha Lee, Chunghyun Park, Jaesung Choe, Yu-Chiang Frank Wang, Jan Kautz, Minsu Cho, Chris Choy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14089-14101
affiliations: ["NVIDIA", "POSTECH"]
cities: ["Santa Clara", "Pohang"]
countries: ["USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Mosaic3D_Foundation_Dataset_and_Model_for_Open-Vocabulary_3D_Segmentation_CVPR_2025_paper.html

abstract:
We tackle open-vocabulary 3D scene segmentation tasks by introducing a novel data generation pipeline and training framework. Our work targets three essential aspects required for an effective dataset: precise 3D region segmentation, comprehensive textual descriptions, and sufficient dataset scale. By leveraging state-of-the-art open-vocabulary image segmentation models and region-aware vision-language models (VLM), we develop an automatic pipeline capable of producing high-quality 3D mask-text pairs. Applying this pipeline to multiple 3D scene datasets, we create Mosaic3D-5.6M, a dataset of more than 30K annotated scenes with 5.6M mask-text pairs - significantly larger than existing datasets. Building on these data, we propose Mosaic3D, a 3D visiual foundation model (3D-VFM) combining a 3D encoder trained with contrastive learning and a lightweight mask decoder for open-vocabulary 3D semantic and instance segmentation. Our approach achieves state-of-the-art results on open-vocabulary 3D semantic and instance segmentation benchmarks including ScanNet200, Matterport3D, and ScanNet++, with ablation studies validating the effectiveness of our large-scale training data. https://nvlabs.github.io/Mosaic3D/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Mosaic3D_Foundation_Dataset_and_Model_for_Open-Vocabulary_3D_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Mosaic3D_Foundation_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Junha and Park, Chunghyun and Choe, Jaesung and Wang, Yu-Chiang Frank and Kautz, Jan and Cho, Minsu and Choy, Chris},
    title     = {Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14089-14101}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]


authors: Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15339-15348
raw_affiliation: [{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]
affiliations: ["KAIST"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hwang_T-CIL_Temperature_Scaling_using_Adversarial_Perturbation_for_Calibration_in_Class-Incremental_CVPR_2025_paper.html

title:
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning

abstract:
We study model confidence calibration in class-incremental learning, where models learn from sequential tasks with different class sets. While existing works primarily focus on accuracy, maintaining calibrated confidence has been largely overlooked. Unfortunately, most post-hoc calibration techniques are not designed to work with the limited memories of old-task data typical in class-incremental learning, as retaining a sufficient validation set would be impractical. Thus, we propose T-CIL, a novel temperature scaling approach for class-incremental learning without a validation set for old tasks, that leverages adversarially perturbed exemplars from memory. Directly using exemplars is inadequate for temperature optimization, since they are already used for training. The key idea of T-CIL is to perturb exemplars more strongly for old tasks than for the new task by adjusting the perturbation direction based on feature distance, with the single magnitude determined using the new-task validation set. This strategy makes the perturbation magnitude computed from the new task also applicable to old tasks, leveraging the tendency that the accuracy of old tasks is lower than that of the new task. We empirically show that T-CIL significantly outperforms various baselines in terms of calibration on real datasets and can be integrated with existing class-incremental learning techniques with minimal impact on accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hwang_T-CIL_Temperature_Scaling_using_Adversarial_Perturbation_for_Calibration_in_Class-Incremental_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hwang_T-CIL_Temperature_Scaling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hwang_2025_CVPR,
    author    = {Hwang, Seong-Hyeon and Kim, Minsu and Whang, Steven Euijong},
    title     = {T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15339-15348}
}


title: LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
authors: Xuan Liu, Xiaobin Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15308-15318
cities: ["Guangzhou", "Guangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_LoRA_Subtraction_for_Drift-Resistant_Space_in_Exemplar-Free_Continual_Learning_CVPR_2025_paper.html

abstract:
In continual learning (CL), catastrophic forgetting often arises due to feature drift. This challenge is particularly prominent in the exemplar-free continual learning (EFCL) setting, where samples from previous tasks cannot be retained, making it difficult to preserve prior knowledge. To address this issue, some EFCL methods aim to identify feature spaces that minimize the impact on previous tasks while accommodating new ones. However, they rely on static features or outdated statistics stored from old tasks, which prevents them from capturing the dynamic evolution of the feature space in CL, leading to performance degradation over time. In this paper, we introduce the Drift-Resistant Space (DRS), which effectively handles feature drifts without requiring explicit feature modeling or the storage of previous tasks. A novel parameter-efficient fine-tuning approach called Low-Rank Adaptation Subtraction (LoRA^-) is proposed to develop the DRS. This method subtracts the LoRA weights of old tasks from the initial pre-trained weight before processing new task data to establish the DRS for model training. Therefore, LoRA^- enhances stability, improves efficiency, and simplifies implementation. Furthermore, stabilizing feature drifts allows for better plasticity by learning with a triplet loss. Our method consistently achieves state-of-the-art results, especially for long task sequences, across multiple datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_LoRA_Subtraction_for_Drift-Resistant_Space_in_Exemplar-Free_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_LoRA_Subtraction_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xuan and Chang, Xiaobin},
    title     = {LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15308-15318}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Arti\ufb01cial Intelligence, Sun Yat-sen University, China", "Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: Joint Out-of-Distribution Filtering and Data Discovery Active Learning
authors: Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25677-25687
affiliations: ["Technical University of Munich", "BMW Group", "SPRIND"]
cities: ["Munich", "Munich", "Berlin"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Schmidt_Joint_Out-of-Distribution_Filtering_and_Data_Discovery_Active_Learning_CVPR_2025_paper.html

abstract:
As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and reduces training costs. Real-world scenarios necessitate the consideration of incomplete data knowledge within AL. Prior works address handling out-of-distribution (OOD) data, while another research direction has focused on category discovery. However, a combined analysis of real-world considerations combining AL with out-of-distribution data and category discovery remains unexplored. To address this gap, we propose Joint Out-of-distribution filtering and data Discovery Active learning (Joda), to uniquely address both challenges simultaneously by filtering out OOD data before selecting candidates for labeling. In contrast to previous methods, we deeply entangle the training procedure with filter and selection to construct a common feature space that aligns known and novel categories while separating OOD samples. Unlike previous works, Joda is highly efficient and completely omits auxiliary models and training access to the unlabeled pool for filtering or selection. In extensive experiments on 18 configurations and 3 metrics, Joda consistently achieves the highest accuracy with the best class discovery to OOD filtering balance compared to state-of-the-art competitor approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Schmidt_Joint_Out-of-Distribution_Filtering_and_Data_Discovery_Active_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Schmidt_Joint_Out-of-Distribution_Filtering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Schmidt_2025_CVPR,
    author    = {Schmidt, Sebastian and Schenk, Leonard and Schwinn, Leo and G\"unnemann, Stephan},
    title     = {Joint Out-of-Distribution Filtering and Data Discovery Active Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25677-25687}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "BMW Group", "city": "Munich", "country": "Germany"}, {"affiliation": "SPRIND", "city": "Berlin", "country": "Germany"}]


title: AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer
authors: Jin Lyu, Tianyi Zhu, Yi Gu, Li Lin, Pujin Cheng, Yebin Liu, Xiaoying Tang, Liang An
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17486-17496
cities: ["Shenzhen", "Beijing", "Hong Kong", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lyu_AniMer_Animal_Pose_and_Shape_Estimation_Using_Family_Aware_Transformer_CVPR_2025_paper.html

abstract:
Quantitative analysis of animal behavior and biomechanics requires accurate animal pose and shape estimation across species, and is important for animal welfare and biological research. However, the small network capacity of previous methods and limited multi-species dataset leave this problem underexplored. To this end, this paper presents AniMer to estimate animal pose and shape using family-aware Transformer, enhancing the reconstruction accuracy of diverse quadrupedal families. A key insight of AniMer is its integration of a high-capacity Transformer-based backbone and an animal family supervised contrastive learning scheme, unifying the discriminative understanding of various quadrupedal shapes within a single framework. For effective training, we aggregate most available open-sourced quadrupedal datasets, either with 3D or 2D labels. To improve the diversity of 3D labeled data, we introduce CtrlAni3D, a novel large-scale synthetic dataset created through a new diffusion-based conditional image generation pipeline. CtrlAni3D consists of about 10k images with pixel-aligned SMAL labels. In total, we obtain 41.3k annotated images for training and validation. Consequently, the combination of a family aware Transformer network and an expansive dataset enables AniMer to outperform existing methods not only on 3D datasets like Animal3D and CtrlAni3D, but also on out-of-distribution Animal Kingdom dataset. Ablation studies further demonstrate the effectiveness of our network design and CtrlAni3D in enhancing the performance of AniMer for in-the-wild applications. Project page: https://luoxuestar.github.io/AniMer_project_page/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lyu_AniMer_Animal_Pose_and_Shape_Estimation_Using_Family_Aware_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lyu_AniMer_Animal_Pose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lyu_2025_CVPR,
    author    = {Lyu, Jin and Zhu, Tianyi and Gu, Yi and Lin, Li and Cheng, Pujin and Liu, Yebin and Tang, Xiaoying and An, Liang},
    title     = {AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17486-17496}
}

raw_affiliation:
[{"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "China Mobile Research Institute", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Southern University of Science and Technology", "China Mobile Research Institute", "The Hong Kong University of Science and Technology", "The University of Hong Kong", "Tsinghua University"]


title: Co-op: Correspondence-based Novel Object Pose Estimation
authors: Sungphill Moon, Hyeontae Son, Dongcheol Hur, Sangwook Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11622-11632
raw_affiliation: [{"affiliation": "NA VER LABS", "city": "Seoul", "country": "South Korea"}]
affiliations: ["NA VER LABS"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Moon_Co-op_Correspondence-based_Novel_Object_Pose_Estimation_CVPR_2025_paper.html

abstract:
We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large number of templates, our method enables fast and accurate estimation with a small number of templates. This improvement is achieved by finding semi-dense correspondences between the input image and the pre-rendered templates. Our method achieves strong generalization performance by leveraging a hybrid representation that combines patch-level classification and offset regression. Additionally, our pose refinement model estimates probabilistic flow between the input image and the rendered image, refining the initial estimate to an accurate pose using a differentiable PnP layer. We demonstrate that our method not only estimates object poses rapidly but also outperforms existing methods by a large margin on the seven core datasets of the BOP Challenge, achieving state-of-the-art accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Moon_Co-op_Correspondence-based_Novel_Object_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Moon_Co-op_Correspondence-based_Novel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Moon_2025_CVPR,
    author    = {Moon, Sungphill and Son, Hyeontae and Hur, Dongcheol and Kim, Sangwook},
    title     = {Co-op: Correspondence-based Novel Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11622-11632}
}


title: Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23227-23236
cities: ["Harbin", "Shenzhen", "Cleveland"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Finding_Local_Diffusion_Schrodinger_Bridge_using_Kolmogorov-Arnold_Network_CVPR_2025_paper.html

authors:
Xingyu Qiu, Mengying Yang, Xinghua Ma, Fanding Li, Dong Liang, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li

abstract:
In image generation, Schrodinger Bridge (SB)-based methods theoretically enhance the efficiency and quality compared to the diffusion models by finding the least costly path between two distributions. However, they are computationally expensive and time-consuming when applied to complex image data. The reason is that they focus on fitting globally optimal paths in high-dimensional spaces, directly generating images as next step on the path using complex networks through self-supervised training, which typically results in a gap with the global optimum. Meanwhile, most diffusion models are in the same path subspace generated by weights f_A(t) and f_B(t), as they follow the paradigm (x_t = f_A(t)x_ Img  + f_B(t)epsilon). To address the limitations of SB-based methods, this paper proposes for the first time to find local Diffusion Schrodinger Bridges (LDSB) in the diffusion path subspace, which strengthens the connection between the SB problem and diffusion models. Specifically, our method optimizes the diffusion paths using Kolmogorov-Arnold Network (KAN), which has the advantage of resistance to forgetting and continuous output. The experiment shows that our LDSB significantly improves the quality and efficiency of image generation using the same pre-trained denoising network and the KAN for optimising is only less than 0.1MB. The FID metric is reduced by more than 15%, especially with a reduction of 48.50% when NFE of DDIM is 5 for the CelebA dataset. Code is available at https://github.com/PerceptionComputingLab/LDSB.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Finding_Local_Diffusion_Schrodinger_Bridge_using_Kolmogorov-Arnold_Network_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Finding_Local_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Xingyu and Yang, Mengying and Ma, Xinghua and Li, Fanding and Liang, Dong and Luo, Gongning and Wang, Wei and Wang, Kuanquan and Li, Shuo},
    title     = {Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23227-23236}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Case Western Reserve University", "city": "Cleveland", "country": "USA"}]

affiliations:
["Harbin Institute of Technology", "Harbin Institute of Technology", "Case Western Reserve University"]


title: CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes
authors: Ziteng Xue, Mingzhe Guo, Heng Fan, Shihui Zhang, Zhipeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27413-27423
cities: ["Qinhuangdao", "Shanghai", "Beijing", "Denton", "Qinhuangdao"]
countries: ["China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_CorrBEV_Multi-View_3D_Object_Detection_by_Correlation_Learning_with_Multi-modal_CVPR_2025_paper.html

abstract:
Camera-only multi-view 3D object detection in autonomous driving has witnessed encouraging developments in recent years, largely attributed to the revolution of fundamental architectures in modeling bird's eye view (BEV). Despite the growing overall average performance, we contend that the exploration of more specific and challenging corner cases hasn't received adequate attention. In this work, we delve into a specific yet critical issue for safe autonomous driving: occlusion. To alleviate this challenge, we draw inspiration from the human amodal perception system, which is proven to have the capacity for mentally reconstructing the complete semantic concept of occluded objects with prior knowledge. More specifically, we introduce auxiliary visual and language prototypes, akin to human prior knowledge, to enhance the diminished object features caused by occlusion. Inspired by Siamese object tracking, we fuse the information from these prototypes with the baseline model through an efficient depth-wise correlation, thereby enhancing the quality of object-related features and guiding the learning of 3D object queries, especially for partially occluded ones. Furthermore, we propose the random pixel drop to mimic occlusion and the multi-modal contrastive loss to align visual features of different occlusion levels to a unified space during training. Our inspiration originates from addressing occlusion, however, we are surprised to find that the proposed framework also enhances robustness in various challenging scenarios that diminish object representation, such as inclement weather conditions. By applying our model to different baselines, i.e., BEVFormer and SparseBEV, we demonstrate consistent improvements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_CorrBEV_Multi-View_3D_Object_Detection_by_Correlation_Learning_with_Multi-modal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_CorrBEV_Multi-View_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Ziteng and Guo, Mingzhe and Fan, Heng and Zhang, Shihui and Zhang, Zhipeng},
    title     = {CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27413-27423}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Engineering, Yanshan University", "city": "Qinhuangdao", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "University of North Texas", "city": "Denton", "country": "USA"}, {"affiliation": "Hebei Key Laboratory of CVT & SI", "city": "Qinhuangdao", "country": "China"}]

affiliations:
["School of Information Science and Engineering, Yanshan University", "School of Artificial Intelligence, Shanghai Jiao Tong University", "Beijing Jiaotong University", "University of North Texas", "Hebei Key Laboratory of CVT & SI"]


title: Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
authors: Zhiqiang Yan, Zhengxue Wang, Kun Wang, Jun Li, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26943-26953
affiliations: ["PCA Lab\u2020, Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Completion_as_Enhancement_A_Degradation-Aware_Selective_Image_Guided_Network_for_CVPR_2025_paper.html

abstract:
In this paper, we introduce the  Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time. Moving beyond direct completion using convolutional neural networks (CNNs), SigNet initially densifies sparse depth data through non-CNN densification tools to obtain coarse yet dense depth. This approach eliminates the mismatch and ambiguity caused by direct convolution over irregularly sampled sparse data. Subsequently, SigNet redefines completion as enhancement, establishing a self-supervised degradation bridge between the coarse depth and the targeted dense depth for effective RGB-D fusion. To achieve this, SigNet leverages the implicit degradation to adaptively select high-frequency components (e.g., edges) of RGB data to compensate for the coarse depth. This degradation is further integrated into a multi-modal conditional Mamba, dynamically generating the state parameters to enable efficient global high-frequency information interaction. We conduct extensive experiments on the NYUv2, DIML, SUN RGBD, and TOFDC datasets, demonstrating the state-of-the-art (SOTA) performance of SigNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Completion_as_Enhancement_A_Degradation-Aware_Selective_Image_Guided_Network_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Completion_as_Enhancement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Zhiqiang and Wang, Zhengxue and Wang, Kun and Li, Jun and Yang, Jian},
    title     = {Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26943-26953}
}

raw_affiliation:
[{"affiliation": "PCA Lab\u2020, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]


title: CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
authors: Xin Liu, Jie Liu, Jie Tang, Gangshan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17902-17912
affiliations: ["State Key Laboratory for Novel Software Technology, Nanjing University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_CATANet_Efficient_Content-Aware_Token_Aggregation_for_Lightweight_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its computational complexity grows quadratically with the spatial resolution. A series of works attempt to alleviate this problem by dividing Low-Resolution images into local windows, axial stripes, or dilated windows. SR typically leverages the redundancy of images for reconstruction, and this redundancy appears not only in local regions but also in long-range regions. However, these methods limit attention computation to content-agnostic local regions, limiting directly the ability of attention to capture long-range dependency. To address these issues, we propose a lightweight Content-Aware Token Aggregation Network (CATANet). Specifically, we propose an efficient Content-Aware Token Aggregation module for aggregate long-range content-similar tokens, which shares token centers across all image tokens and updates them only during the training phase. Then we utilize intra-group self-attention to enable long-range information interaction. Moreover, we design an inter-group cross-attention to further enhance global information interaction. The experimental results show that, compared with the state-of-the-art cluster-based method SPIN, our method achieves superior performance, with a maximum PSNR improvement of 0.33dB and nearly double the inference speed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_CATANet_Efficient_Content-Aware_Token_Aggregation_for_Lightweight_Image_Super-Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_CATANet_Efficient_Content-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xin and Liu, Jie and Tang, Jie and Wu, Gangshan},
    title     = {CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17902-17912}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}]


title: SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding
authors: Rong Li, Shijie Li, Lingdong Kong, Xulei Yang, Junwei Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3707-3717
affiliations: ["HKUST(GZ)", "I2R, A*STAR", "National University of Singapore", "CSE, HKUST"]
cities: ["Guangzhou", "Singapore", "Singapore", "Hong Kong"]
countries: ["China", "Singapore", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SeeGround_See_and_Ground_for_Zero-Shot_Open-Vocabulary_3D_Visual_Grounding_CVPR_2025_paper.html

abstract:
3D Visual Grounding (3DVG) aims to locate objects in 3D scenes based on textual descriptions, essential for applications like augmented reality and robotics. Traditional 3DVG approaches rely on annotated 3D datasets and predefined object categories, limiting scalability and adaptability. To overcome these limitations, we introduce SeeGround, a zero-shot 3DVG framework leveraging 2D Vision-Language Models (VLMs) trained on large-scale 2D data. SeeGround represents 3D scenes as a hybrid of query-aligned rendered images and spatially enriched text descriptions, bridging the gap between 3D data and 2D-VLMs input formats. We propose two modules: the Perspective Adaptation Module, which dynamically selects viewpoints for query-relevant image rendering, and the Fusion Alignment Module, which integrates 2D images with 3D spatial descriptions to enhance object localization. Extensive experiments on ScanRefer and Nr3D demonstrate that our approach outperforms existing zero-shot methods by large margins. Notably, we exceed weakly supervised methods and rival some fully supervised ones, outperforming previous SOTA by 7.7% on ScanRefer and 7.1% on Nr3D, showcasing its effectiveness in complex 3DVG task. Project website (with demo and code): https://seeground.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SeeGround_See_and_Ground_for_Zero-Shot_Open-Vocabulary_3D_Visual_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SeeGround_See_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Rong and Li, Shijie and Kong, Lingdong and Yang, Xulei and Liang, Junwei},
    title     = {SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3707-3717}
}

raw_affiliation:
[{"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "I2R, A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CSE, HKUST", "city": "Hong Kong", "country": "China"}]


title: RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
authors: Huiyang Shao, Xin Xia, Yuhong Yang, Yuxi Ren, Xing Wang, Xuefeng Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18113-18123
affiliations: ["Tsinghua University", "ByteDance Inc."]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_RayFlow_Instance-Aware_Diffusion_Acceleration_via_Adaptive_Flow_Trajectories_CVPR_2025_paper.html

abstract:
Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while aiming to reduce steps, often compromise sample quality, controllability, or introduce training complexities. Therefore, we propose \underline RayFlow , a novel diffusion framework that addresses these limitations. Unlike previous methods, RayFlow guides each sample along a unique path towards an instance-specific target distribution. This method minimizes sampling steps while preserving generation diversity and stability. Furthermore, we introduce Time Sampler, an importance sampling technique to enhance training efficiency by focusing on crucial timesteps. Extensive experiments demonstrate RayFlow's superiority in generating high-quality images with improved speed, control, and training efficiency compared to existing acceleration techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_RayFlow_Instance-Aware_Diffusion_Acceleration_via_Adaptive_Flow_Trajectories_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_RayFlow_Instance-Aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Huiyang and Xia, Xin and Yang, Yuhong and Ren, Yuxi and Wang, Xing and Xiao, Xuefeng},
    title     = {RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18113-18123}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "ByteDance Inc.", "city": "Beijing", "country": "China"}]


title: Linear Attention Modeling for Learned Image Compression
authors: Donghui Feng, Zhengxue Cheng, Shen Wang, Ronghua Wu, Hongwei Hu, Guo Lu, Li Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7623-7632
affiliations: ["Shanghai Jiao Tong University", "Ant Group"]
cities: ["Shanghai", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Linear_Attention_Modeling_for_Learned_Image_Compression_CVPR_2025_paper.html

abstract:
Recent years, learned image compression has made tremendous progress to achieve impressive coding efficiency. Its coding gain mainly comes from non-linear neural network-based transform and learnable entropy modeling. However, most studies focus on a strong backbone, and few studies consider a low complexity design. In this paper, we propose LALIC, a linear attention modeling for learned image compression. Specially, we propose to use Bi-RWKV blocks, by utilizing the Spatial Mix and Channel Mix modules to achieve more compact feature extraction, and apply the Conv based Omni-Shift module to adapt to two-dimensional latent representation. Furthermore, we propose a RWKV-based Spatial-Channel ConTeXt model (RWKV-SCCTX), that leverages the Bi-RWKV to modeling the correlation between neighboring features effectively. To our knowledge, our work is the first work to utilize efficient Bi-RWKV models with linear attention for learned image compression. Experimental results demonstrate that our method achieves competitive RD performances by outperforming VTM-9.1 by -15.26%, -15.41%, -17.63% in BD-rate on Kodak, CLIC and Tecnick datasets. The code is available at https://github.com/sjtu-medialab/RwkvCompress .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Linear_Attention_Modeling_for_Learned_Image_Compression_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_Linear_Attention_Modeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Donghui and Cheng, Zhengxue and Wang, Shen and Wu, Ronghua and Hu, Hongwei and Lu, Guo and Song, Li},
    title     = {Linear Attention Modeling for Learned Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7623-7632}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]


title: Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation
authors: Nicolas Dufour, Vicky Kalogeiton, David Picard, Loic Landrieu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23016-23026
affiliations: ["LIGM, Ecole des Ponts, IP Paris, CNRS, UGE", "LIX, Ecole Polytechnique, IP Paris"]
cities: ["Paris", "Paris"]
countries: ["France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dufour_Around_the_World_in_80_Timesteps_A_Generative_Approach_to_CVPR_2025_paper.html

abstract:
Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and overlook this aspect. In this paper, we aim to close the gap between traditional geolocalization and modern generative methods. We propose the first generative geolocation approach based on diffusion and Riemannian flow matching, where the denoising process operates directly on the Earth's surface. Our model achieves state-of-the-art performance on three visual geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition, we introduce the task of probabilistic visual geolocation, where the model predicts a probability distribution over all possible locations instead of a single point. We introduce new metrics and baselines for this task, demonstrating the advantages of our diffusion-based approach. Codes and models will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dufour_Around_the_World_in_80_Timesteps_A_Generative_Approach_to_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dufour_Around_the_World_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dufour_2025_CVPR,
    author    = {Dufour, Nicolas and Kalogeiton, Vicky and Picard, David and Landrieu, Loic},
    title     = {Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23016-23026}
}

raw_affiliation:
[{"affiliation": "LIGM, Ecole des Ponts, IP Paris, CNRS, UGE", "city": "Paris", "country": "France"}, {"affiliation": "LIX, Ecole Polytechnique, IP Paris", "city": "Paris", "country": "France"}]


title: Asynchronous Collaborative Graph Representation for Frames and Events
authors: Dianze Li, Jianing Li, Xu Liu, Xiaopeng Fan, Yonghong Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1655-1666
cities: ["Beijing", "Harbin", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Asynchronous_Collaborative_Graph_Representation_for_Frames_and_Events_CVPR_2025_paper.html

abstract:
Integrating frames and events has become a widely accepted solution for various tasks in challenging scenarios. However, most multimodal methods directly convert events into image-like formats synchronized with frames and process each stream through separate two-branch backbones, making it difficult to fully exploit the spatiotemporal events while limiting inference frequency to the frame rate. To address these problems, we propose a novel asynchronous collaborative graph representation, namely ACGR, which is the first trial to explore a unified graph framework for asynchronously processing frames and events with high performance and low latency. Technically, we first construct unimodal graphs for frames and events to preserve their spatiotemporal properties and sparsity. Then, an asynchronous collaborative alignment module is designed to align and fuse frames and events into a unified graph and the ACGR is generated through graph convolutional networks. Finally, we innovatively introduce domain adaptation to enable cross-modal interactions between frames and events by aligning their feature spaces. Experimental results show that our approach outperforms state-of-the-art methods in both object detection and depth estimation tasks, while significantly reducing computational latency and achieving real-time inference up to 200 Hz. Our code can be available at https://github.com/dianzl/ACGR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Asynchronous_Collaborative_Graph_Representation_for_Frames_and_Events_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Asynchronous_Collaborative_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Dianze and Li, Jianing and Liu, Xu and Fan, Xiaopeng and Tian, Yonghong},
    title     = {Asynchronous Collaborative Graph Representation for Frames and Events},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1655-1666}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science and Technology, Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science, Peking University", "Department of Computer Science and Technology, Harbin Institute of Technology", "Peng Cheng Laboratory", "School of Electronic and Computer Engineering, Peking University"]


authors: Youyi Zhan, Tianjia Shao, Yin Yang, Kun Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26297-26307
affiliations: ["State Key Lab of CAD&CG, Zhejiang University", "University of Utah"]
cities: ["Hangzhou", "Salt Lake City"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhan_Real-time_High-fidelity_Gaussian_Human_Avatars_with_Position-based_Interpolation_of_Spatially_CVPR_2025_paper.html

title:
Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs

abstract:
Many works have succeeded in reconstructing Gaussian human avatars from multi-view videos. However, they either struggle to capture pose-dependent appearance details with a single MLP, or rely on a computationally intensive neural network to reconstruct high-fidelity appearance but with rendering performance degraded to non-real-time. We propose a novel Gaussian human avatar representation that can reconstruct high-fidelity pose-dependence appearance with details and meanwhile can be rendered in real time. Our Gaussian avatar is empowered by spatially distributed MLPs which are explicitly located on different positions on human body. The parameters stored in each Gaussian are obtained by interpolating from the outputs of its nearby MLPs based on their distances. To avoid undesired smooth Gaussian property changing during interpolation, for each Gaussian we define a set of Gaussian offset basis, and a linear combination of basis represents the Gaussian property offsets relative to the neutral properties. Then we propose to let the MLPs output a set of coefficients corresponding to the basis. In this way, although Gaussian coefficients are derived from interpolation and change smoothly, the Gaussian offset basis is learned freely without constraints. The smoothly varying coefficients combined with freely learned basis can still produce distinctly different Gaussian property offsets, allowing the ability to learn high-frequency spatial signals. We further use control points to constrain the Gaussians distributed on a surface layer rather than allowing them to be irregularly distributed inside the body, to help the human avatar generalize better when animated under novel poses. Compared to the state-of-the-art method, our method achieves better appearance quality with finer details while the rendering speed is significantly faster under novel views and novel poses.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhan_Real-time_High-fidelity_Gaussian_Human_Avatars_with_Position-based_Interpolation_of_Spatially_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhan_Real-time_High-fidelity_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhan_2025_CVPR,
    author    = {Zhan, Youyi and Shao, Tianjia and Yang, Yin and Zhou, Kun},
    title     = {Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26297-26307}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}]


title: ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1559-1569
cities: ["Beijing", "Unknown", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_ReconDreamer_Crafting_World_Models_for_Driving_Scene_Reconstruction_via_Online_CVPR_2025_paper.html

authors:
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Wenkang Qin, Guan Huang, Chen Liu, Yuyin Chen, Yida Wang, Xueyang Zhang, Yifei Zhan, Kun Zhan, Peng Jia, Xianpeng Lang, Xingang Wang, Wenjun Mei

abstract:
Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent work, DriveDreamer4D, has demonstrated that integrating world model knowledge alleviates these issues. Although the training-free integration approach is efficient, it still struggles to render larger maneuvers, such as multi-lane shifts.Therefore, we introduce ReconDreamer, which enhances driving scene reconstruction through incremental integration of world model knowledge. Specifically, based on the world model, DriveRestorer is proposed to mitigate ghosting artifacts via online restoration. Additionally,  we propose the progressive data update strategy to ensure high-quality rendering for larger maneuvers. Notably, ReconDreamer is the first method to effectively render in large maneuvers (e.g., across multiple lanes, spanning up to 6 meters). Additionally, experimental results demonstrate that ReconDreamer outperforms Street Gaussians in the NTA-IoU, NTL-IoU, and FID, with a relative improvement by 24.87%, 6.72%, and 29.97%. Furthermore, ReconDreamer surpasses DriveDreamer4D with PVG during large maneuver rendering, as verified by a relative improvement of 195.87% in the NTA-IoU metric and a comprehensive user study.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_ReconDreamer_Crafting_World_Models_for_Driving_Scene_Reconstruction_via_Online_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ni_ReconDreamer_Crafting_World_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Chaojun and Zhao, Guosheng and Wang, Xiaofeng and Zhu, Zheng and Qin, Wenkang and Huang, Guan and Liu, Chen and Chen, Yuyin and Wang, Yida and Zhang, Xueyang and Zhan, Yifei and Zhan, Kun and Jia, Peng and Lang, Xianpeng and Wang, Xingang and Mei, Wenjun},
    title     = {ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1559-1569}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "GigaAI", "city": "Unknown", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Li Auto Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["Peking University", "GigaAI", "Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "Li Auto Inc."]


authors: Haisheng Su, Feixiang Song, Cong Ma, Wei Wu, Junchi Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27446-27455
cities: ["Shanghai", "Unknown", "Beijing"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_RoboSense_Large-scale_Dataset_and_Benchmark_for_Egocentric_Robot_Perception_and_CVPR_2025_paper.html

title:
RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments

abstract:
Reliable embodied perception from an egocentric perspective is challenging yet essential for autonomous navigation technology of intelligent mobile agents. With the growing demand of social robotics, near-field scene understanding becomes an important research topic in the areas of egocentric perceptual tasks related to navigation in both crowded and unstructured environments. Due to the complexity of environmental conditions and difficulty of surrounding obstacles owing to truncation and occlusion, the perception capability under this circumstance is still inferior. To further enhance the intelligence of mobile robots, in this paper, we setup an egocentric multi-sensor data collection platform based on 3 main types of sensors (Camera, LiDAR and Fisheye), which supports flexible sensor configurations to enable dynamic sight of view from ego-perspective, capturing either near or farther areas. Meanwhile, a large-scale multimodal dataset is constructed, named RoboSense, to facilitate egocentric robot perception. Specifically, RoboSense contains more than 133K synchronized data with 1.4M 3D bounding box and IDs annotated in the full 360^ \circ  view, forming 216K trajectories across 7.6K temporal sequences. It has 270xand 18xas many annotations of surrounding obstacles within near ranges as the previous datasets collected for autonomous driving scenarios such as KITTI and nuScenes. Moreover, we define a novel matching criterion for near-field 3D perception and prediction metrics. Based on RoboSense, we formulate 6 popular tasks to facilitate the future research development, where the detailed analysis as well as benchmarks are also provided accordingly. Data desensitization measures have been conducted for privacy protection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_RoboSense_Large-scale_Dataset_and_Benchmark_for_Egocentric_Robot_Perception_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_RoboSense_Large-scale_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Haisheng and Song, Feixiang and Ma, Cong and Wu, Wei and Yan, Junchi},
    title     = {RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27446-27455}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "SenseAuto Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science, Shanghai Jiao Tong University", "SenseAuto Research", "Tsinghua University"]


title: Self-Supervised Large Scale Point Cloud Completion for Archaeological Site Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11759-11768
affiliations: ["Purdue University", "Vanderbilt University", "Brown University"]
cities: ["West Lafayette", "Nashville", "Providence"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Self-Supervised_Large_Scale_Point_Cloud_Completion_for_Archaeological_Site_Restoration_CVPR_2025_paper.html

authors:
Aocheng Li, James R. Zimmer-Dauphinee, Rajesh Kalyanam, Ian Lindsay, Parker VanValkenburgh, Steven Wernke, Daniel Aliaga

abstract:
Point cloud completion helps restore partial incomplete point clouds suffering occlusions. Current self-supervised methods fail to give high fidelity completion for large objects with missing surfaces and unbalanced distribution of available points. In this paper, we present a novel method for restoring large-scale point clouds with limited and imbalanced ground-truth. Using rough boundary annotations for a region of interest, we project the original point clouds into a multiple-center-of-projection (MCOP) image, where fragments are projected to images of 5 channels (RGB, depth, and rotation). Completion of the original point cloud is reduced to inpainting the missing pixels in the MCOP images. Due to lack of complete structures and an unbalanced distribution of existing parts, we develop a self-supervised scheme which learns to infill the MCOP image with points resembling existing "complete" patches. Special losses are applied to further enhance the regularity and consistency of completed MCOP images, which is mapped back to 3D to form final restoration. Extensive experiments demonstrate the superiority of our method in completing 600+ incomplete and unbalanced archaeological structures in Peru.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Self-Supervised_Large_Scale_Point_Cloud_Completion_for_Archaeological_Site_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Self-Supervised_Large_Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Aocheng and Zimmer-Dauphinee, James R. and Kalyanam, Rajesh and Lindsay, Ian and VanValkenburgh, Parker and Wernke, Steven and Aliaga, Daniel},
    title     = {Self-Supervised Large Scale Point Cloud Completion for Archaeological Site Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11759-11768}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Vanderbilt University", "city": "Nashville", "country": "USA"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}]


authors: Peng Xie, Yequan Bie, Jianda Mao, Yangqiu Song, Yang Wang, Hao Chen, Kani Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14679-14689
affiliations: ["The Hong Kong University of Science and Technology"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Chain_of_Attack_On_the_Robustness_of_Vision-Language_Models_Against_CVPR_2025_paper.html

title:
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks

abstract:
Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation. As the practical applications of VLMs become increasingly widespread, their potential safety and robustness issues raise concerns that adversaries may evade the system and cause these models to generate toxic content through malicious attacks. Therefore, evaluating the robustness of open-source VLMs against adversarial attacks has garnered growing attention, with transfer-based attacks as a representative black-box attacking strategy. However, most existing transfer-based attacks neglect the importance of the semantic correlations between vision and text modalities, leading to sub-optimal adversarial example generation and attack performance. To address this issue, we present Chain of Attack (CoA), which iteratively enhances the generation of adversarial examples based on the multi-modal semantic update using a series of intermediate attacking steps, achieving superior adversarial transferability and efficiency. A unified attack success rate computing method is further proposed for automatic evasion evaluation. Extensive experiments conducted under the most realistic and high-stakes scenario, demonstrate that our attacking strategy is able to effectively mislead models to generate targeted responses using only black-box attacks without any knowledge of the victim models. The comprehensive robustness evaluation in our paper provides insight into the vulnerabilities of VLMs and offers a reference for the safety considerations of future model developments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Chain_of_Attack_On_the_Robustness_of_Vision-Language_Models_Against_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Chain_of_Attack_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Peng and Bie, Yequan and Mao, Jianda and Song, Yangqiu and Wang, Yang and Chen, Hao and Chen, Kani},
    title     = {Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14679-14689}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]


authors: Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20757-20766
affiliations: ["Yale University", "New York University", "Meta FAIR"]
cities: ["New Haven", "New York", "Menlo Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeevi_Rate-In_Information-Driven_Adaptive_Dropout_Rates_for_Improved_Inference-Time_Uncertainty_Estimation_CVPR_2025_paper.html

title:
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation

abstract:
Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forward passes with dropout during inference. However, using static dropout rates across all layers and inputs can lead to suboptimal uncertainty estimates, as it fails to adapt to the varying characteristics of individual inputs and network layers. Existing approaches optimize dropout rates during training using labeled data, resulting in fixed inference-time parameters that cannot adjust to new data distributions, compromising uncertainty estimates in Monte Carlo simulations. In this paper, we propose Rate-In, an algorithm that dynamically adjusts dropout rates during inference by quantifying the information loss induced by dropout in each layer's feature maps. By treating dropout as controlled noise injection and leveraging information-theoretic principles, Rate-In adapts dropout rates per layer and per input instance without requiring ground truth labels. By quantifying the functional information loss in feature maps, we adaptively tune dropout rates to maintain perceptual quality across diverse medical imaging tasks and architectural configurations. Our extensive empirical study on synthetic data and real-world medical imaging tasks demonstrates that Rate-In improves calibration and sharpens uncertainty estimates compared to fixed or heuristic dropout rates without compromising predictive performance. Rate-In offers a practical, unsupervised, inference-time approach to optimizing dropout for more reliable predictive uncertainty estimation in critical applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeevi_Rate-In_Information-Driven_Adaptive_Dropout_Rates_for_Improved_Inference-Time_Uncertainty_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeevi_Rate-In_Information-Driven_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeevi_2025_CVPR,
    author    = {Zeevi, Tal and Shwartz-Ziv, Ravid and LeCun, Yann and Staib, Lawrence H. and Onofrey, John A.},
    title     = {Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20757-20766}
}

raw_affiliation:
[{"affiliation": "Yale University", "city": "New Haven", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Meta FAIR", "city": "Menlo Park", "country": "USA"}]


title: Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields
authors: Navami Kairanda, Marc Habermann, Shanthika Naik, Christian Theobalt, Vladislav Golyanik
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11373-11383
affiliations: ["MPI for Informatics, SIC", "VIA Research Center", "IIT Jodhpur"]
cities: ["Saarbr\u00fccken", "Saarbr\u00fccken", "Jodhpur"]
countries: ["Germany", "Germany", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kairanda_Thin-Shell-SfT_Fine-Grained_Monocular_Non-rigid_3D_Surface_Tracking_with_Neural_Deformation_CVPR_2025_paper.html

abstract:
3D reconstruction of highly deformable surfaces (e.g. cloths) from monocular RGB videos is a challenging problem, and no solution provides a consistent and accurate recovery of fine-grained surface details. To account for the ill-posed nature of the setting, existing methods use deformation models with statistical, neural, or physical priors. They also predominantly rely on nonadaptive discrete surface representations (e.g. polygonal meshes), perform frame-by-frame optimisation leading to error propagation, and suffer from poor gradients of the mesh-based differentiable renderers. Consequently, fine surface details such as cloth wrinkles are often not recovered with the desired accuracy. In response to these limitations, we propose Thin-Shell-SfT, a new method for non-rigid 3D tracking that represents a surface as an implicit and continuous spatiotemporal neural field. We incorporate continuous thin shell physics prior based on the Kirchhoff-Love model for spatial regularisation, which starkly contrasts the discretised alternatives of earlier works. Lastly, we leverage 3D Gaussian splatting to differentiably render the surface into image space and optimise the deformations based on analysis-by-synthesis principles. Our Thin-Shell-SfT outperforms prior works qualitatively and quantitatively thanks to our continuous surface formulation in conjunction with a specially tailored simulation prior and surface-induced 3D Gaussians. See our project page at https://4dqv.mpi-inf.mpg.de/ThinShellSfT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kairanda_Thin-Shell-SfT_Fine-Grained_Monocular_Non-rigid_3D_Surface_Tracking_with_Neural_Deformation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kairanda_Thin-Shell-SfT_Fine-Grained_Monocular_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kairanda_2025_CVPR,
    author    = {Kairanda, Navami and Habermann, Marc and Naik, Shanthika and Theobalt, Christian and Golyanik, Vladislav},
    title     = {Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11373-11383}
}

raw_affiliation:
[{"affiliation": "MPI for Informatics, SIC", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "VIA Research Center", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "IIT Jodhpur", "city": "Jodhpur", "country": "India"}]


title: DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception
authors: Junjie Wang, Bin Chen, Yulin Li, Bin Kang, Yichi Chen, Zhuotao Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14824-14834
cities: ["Shenzhen", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DeCLIP_Decoupled_Learning_for_Open-Vocabulary_Dense_Perception_CVPR_2025_paper.html

abstract:
Dense visual prediction tasks have been constrained by their reliance on predefined categories, limiting their applicability in real-world scenarios where visual concepts are unbounded. While Vision-Language Models (VLMs) like CLIP have shown promise in open-vocabulary tasks, their direct application to dense prediction often leads to suboptimal performance due to limitations in local feature representation. In this work, we present our observation that CLIP's image tokens struggle to effectively aggregate information from spatially or semantically related regions, resulting in features that lack local discriminability and spatial consistency. To address this issue, we propose DeCLIP, a novel framework that enhances CLIP by decoupling the self-attention module to obtain "content" and "context" features respectively. The "content" features are aligned with image crop representations to improve local discriminability, while "context" features learn to retain the spatial correlations under the guidance of vision foundation models, such as DINO. Extensive experiments demonstrate that DeCLIP significantly outperforms existing methods across multiple open-vocabulary dense prediction tasks, including object detection and semantic segmentation. Code is available at https://github.com/xiaomoguhz/DeCLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DeCLIP_Decoupled_Learning_for_Open-Vocabulary_Dense_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DeCLIP_Decoupled_Learning_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Junjie and Chen, Bin and Li, Yulin and Kang, Bin and Chen, Yichi and Tian, Zhuotao},
    title     = {DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14824-14834}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, HIT, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "International Research Institute for Artificial Intelligence, HIT, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Technology, HIT, Shenzhen", "International Research Institute for Artificial Intelligence, HIT, Shenzhen", "University of Chinese Academy of Sciences"]


title: SocialGesture: Delving into Multi-person Gesture Understanding
authors: Xu Cao, Pranav Virupaksha, Wenqi Jia, Bolin Lai, Fiona Ryan, Sangmin Lee, James M. Rehg
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19509-19519
cities: ["Urbana-Champaign", "Atlanta", "Suwon"]
countries: ["USA", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_SocialGesture_Delving_into_Multi-person_Gesture_Understanding_CVPR_2025_paper.html

abstract:
Previous research in human gesture recognition has largely overlooked multi-person interactions, which are crucial for understanding the social context of naturally occurring gestures. This limitation in existing datasets presents a significant challenge in aligning human gestures with other modalities like language and speech. To address this issue, we introduce SocialGesture, the first large-scale dataset specifically designed for multi-person gesture analysis. SocialGesture features a diverse range of natural scenarios and supports multiple gesture analysis tasks, including video-based recognition and temporal localization, providing a valuable resource for advancing the study of gesture during complex social interactions. Furthermore, we propose a novel visual question answering (VQA) task to benchmark vision language models' (VLMs) performance on social gesture understanding. Our findings highlight several limitations of current gesture recognition models, offering insights into future directions for improvement in this field. SocialGesture is available at huggingface.co/datasets/IrohXu/SocialGesture.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_SocialGesture_Delving_into_Multi-person_Gesture_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_SocialGesture_Delving_into_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Xu and Virupaksha, Pranav and Jia, Wenqi and Lai, Bolin and Ryan, Fiona and Lee, Sangmin and Rehg, James M.},
    title     = {SocialGesture: Delving into Multi-person Gesture Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19509-19519}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}]

affiliations:
["University of Illinois Urbana-Champaign", "Georgia Institute of Technology", "Sungkyunkwan University"]


title: GenFusion: Closing the Loop between Reconstruction and Generation via Videos
authors: Sibo Wu, Congrong Xu, Binbin Huang, Andreas Geiger, Anpei Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6078-6088
cities: ["Hangzhou", "Munich", "Shanghai", "Hong Kong", "T\u00fcbingen"]
countries: ["China", "Germany", "China", "China", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_GenFusion_Closing_the_Loop_between_Reconstruction_and_Generation_via_Videos_CVPR_2025_paper.html

abstract:
Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency.  However, a notable conditioning gap can be observed between these two fields, e.g. , scalable 3D scene reconstruction often requires densely captured views, whereas 3D generation typically relies on a single or no input view, which significantly limits their applications. We found that the source of this phenomenon lies in the misalignment between 3D constraints and generative priors. To address this problem, we propose a reconstruction-driven video diffusion model that learns to condition video frames on artifact-prone RGB-D renderings.  Moreover, we propose a cyclical fusion pipeline that iteratively adds restoration frames from the generative model to the training set, enabling progressive expansion and addressing the viewpoint saturation limitations seen in previous reconstruction and generation pipelines. Our evaluation, including view synthesis from sparse view and masked input, validates the effectiveness of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_GenFusion_Closing_the_Loop_between_Reconstruction_and_Generation_via_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_GenFusion_Closing_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Sibo and Xu, Congrong and Huang, Binbin and Geiger, Andreas and Chen, Anpei},
    title     = {GenFusion: Closing the Loop between Reconstruction and Generation via Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6078-6088}
}

raw_affiliation:
[{"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}]

affiliations:
["Westlake University", "Technical University of Munich", "ShanghaiTech University", "The University of Hong Kong", "University of T\u00a8ubingen"]


title: The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5433-5443
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Brookes_The_PanAf-FGBG_Dataset_Understanding_the_Impact_of_Backgrounds_in_Wildlife_CVPR_2025_paper.html

authors:
Otto Brookes, Maksim Kukushkin, Majid Mirmehdi, Colleen Stephens, Paula Dieguez, Thurston C. Hicks, Sorrel Jones, Kevin Lee, Maureen S. McCarthy, Amelia Meier, Emmanuelle Normand, Erin G. Wessling, Roman M. Wittig, Kevin Langergraber, Klaus Zuberbühler, Lukas Boesch, Thomas Schmid, Mimi Arandjelovic, Hjalmar Kühl, Tilo Burghardt

abstract:
Computer vision analysis of camera trap video footage is essential for wildlife conservation, as captured behaviours offer some of the earliest indicators of changes in population health. Recently, several high-impact animal behaviour datasets and methods have been introduced to encourage their use; however, the role of behaviour-correlated background information and its significant effect on out-of-distribution generalisation remain unexplored. In response, we present the PanAf-FGBG dataset, featuring 21 hours of wild chimpanzee behaviours, recorded at 389 individual camera locations. Uniquely, it pairs every video with a chimpanzee (referred to as a foreground video) with a corresponding background video (with no chimpanzee) from the same camera location. We present two views of the dataset: one with overlapping camera locations and one with disjoint locations. This setup enables, for the first time, direct evaluation of in-distribution and out-of-distribution conditions, and for the impact of backgrounds on behaviour recognition models to be quantified. All clips have rich behavioural annotations and metadata, including unique camera IDs. Additionally, we establish several baselines and present a highly effective latent-space normalisation technique that boosts out-of-distribution performance by +5.42% mAP for convolutional and +3.75% mAP for transformer-based models. Finally, we provide an in-depth analysis on the role of backgrounds in out-of-distribution behaviour recognition, including the so far unexplored impact of background durations. (i.e., the count of background frames within foreground videos). The dataset is available at https://obrookes.github.io/panaf-fgbg.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Brookes_The_PanAf-FGBG_Dataset_Understanding_the_Impact_of_Backgrounds_in_Wildlife_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Brookes_The_PanAf-FGBG_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Brookes_2025_CVPR,
    author    = {Brookes, Otto and Kukushkin, Maksim and Mirmehdi, Majid and Stephens, Colleen and Dieguez, Paula and Hicks, Thurston C. and Jones, Sorrel and Lee, Kevin and McCarthy, Maureen S. and Meier, Amelia and Normand, Emmanuelle and Wessling, Erin G. and Wittig, Roman M. and Langergraber, Kevin and Zuberb\"uhler, Klaus and Boesch, Lukas and Schmid, Thomas and Arandjelovic, Mimi and K\"uhl, Hjalmar and Burghardt, Tilo},
    title     = {The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5433-5443}
}

raw_affiliation:
[{"affiliation": "University of Bristol", "city": "Bristol", "country": "United Kingdom"}, {"affiliation": "Wild Chimpanzee Foundation", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Martin Luther University Halle-Wittenberg", "city": "Halle", "country": "Germany"}, {"affiliation": "Leipzig University", "city": "Leipzig", "country": "Germany"}, {"affiliation": "Max Planck Institute for Evolutionary Anthropology", "city": "Leipzig", "country": "Germany"}, {"affiliation": "University of Warsaw", "city": "Warsaw", "country": "Poland"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "University of Lyon", "city": "Lyon", "country": "France"}, {"affiliation": "Arizona State University", "city": "Tempe", "country": "USA"}, {"affiliation": "University of St Andrews", "city": "St Andrews", "country": "United Kingdom"}, {"affiliation": "Lancaster University in Leipzig", "city": "Leipzig", "country": "Germany"}, {"affiliation": "Senckenberg Museum of Natural History", "city": "Unknown", "country": "Germany"}]

affiliations:
["University of Bristol", "Wild Chimpanzee Foundation", "Martin Luther University Halle-Wittenberg", "Leipzig University", "Max Planck Institute for Evolutionary Anthropology", "University of Warsaw", "Harvard University", "University of Lyon", "Arizona State University", "University of St Andrews", "Lancaster University in Leipzig", "Senckenberg Museum of Natural History"]

cities:
["Bristol", "Unknown", "Halle", "Leipzig", "Leipzig", "Warsaw", "Cambridge", "Lyon", "Tempe", "St Andrews", "Leipzig", "Unknown"]

countries:
["United Kingdom", "Unknown", "Germany", "Germany", "Germany", "Poland", "USA", "France", "USA", "United Kingdom", "Germany", "Germany"]


authors: Hang Shi, Changxi Chi, Peng Wan, Daoqiang Zhang, Wei Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20810-20819
cities: ["Nanjing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Multi-modal_Topology-embedded_Graph_Learning_for_Spatially_Resolved_Genes_Prediction_from_CVPR_2025_paper.html

title:
Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information

abstract:
The rapid development of spatial transcriptomics (ST) allows researchers to measure the spatial-level gene expression in tissues. Although powerful, the cost for collecting the ST data is expensive, and thus several studies aim to predict gene expression in ST by utilizing their corresponding H/E stained pathology images. The existing ST based gene expression prediction models either adopt the pre-trained networks or rely on the handcrafted features to describe the pathology images, which still lack a systematic way to combine them together to define a spot-level representation that can reflect the topological profiles of different spots. On the other hand, all the ST based gene prediction models treat the prediction task for each gene independently, which overlook the fact that the exploration of potential interrelationships among them can help improve the prediction performance for individual genes.To address the above issues, we propose a multi-modal topology-embedded graph learning algorithm guided by prior Gene Ontology similarity information (i.e., M2TGLGO) to predict the spatial resolved genes from pathology image. Specifically, M2TGLGO co-learns the image representation of different spots from both deep and handcrafted features by considering the within-modal and inter-modal interactions. Next, to keep the topological structure among different spots, a spatial-oriented  ranking module is also incorporated to preserve their neighborhood similarity information. Finally, we present a Gene Ontology knowledge guided graph neural network for simultaneously predicting multiple gene expressions by considering their functional associations. We evaluate our method on three public available ST datasets, the experimental results show the effectiveness of our M2TGLGO in comparison with the existing studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Multi-modal_Topology-embedded_Graph_Learning_for_Spatially_Resolved_Genes_Prediction_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Multi-modal_Topology-embedded_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Hang and Chi, Changxi and Wan, Peng and Zhang, Daoqiang and Shao, Wei},
    title     = {Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20810-20819}
}

raw_affiliation:
[{"affiliation": "The College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education", "city": "Beijing", "country": "China"}]

affiliations:
["The College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics", "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education"]


title: Question-Aware Gaussian Experts for Audio-Visual Question Answering
authors: Hongyeob Kim, Inyoung Jung, Dayoon Suh, Youjia Zhang, Sangmin Lee, Sungeun Hong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13681-13690
affiliations: ["Sungkyunkwan University", "Purdue University"]
cities: ["Suwon", "West Lafayette"]
countries: ["South Korea", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Question-Aware_Gaussian_Experts_for_Audio-Visual_Question_Answering_CVPR_2025_paper.html

abstract:
Audio-Visual Question Answering (AVQA) requires not only question-based multimodal reasoning but also precise temporal grounding to capture subtle dynamics for accurate prediction. However, existing methods mainly use question information implicitly, limiting focus on question-specific details. Furthermore, most studies rely on uniform frame sampling, which can miss key question-relevant frames. Although recent Top-K frame selection methods aim to address this, their discrete nature still overlooks fine-grained temporal details. This paper proposes QA-TIGER, a novel framework that explicitly incorporates question information and models continuous temporal dynamics. Our key idea is to use Gaussian-based modeling to adaptively focus on both consecutive and non-consecutive frames based on the question, while explicitly injecting question information and applying progressive refinement. We leverage a Mixture of Experts (MoE) to flexibly implement multiple Gaussian models, activating temporal experts specifically tailored to the question. Extensive experiments on multiple AVQA benchmarks show that QA-TIGER consistently achieves state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Question-Aware_Gaussian_Experts_for_Audio-Visual_Question_Answering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Question-Aware_Gaussian_Experts_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Hongyeob and Jung, Inyoung and Suh, Dayoon and Zhang, Youjia and Lee, Sangmin and Hong, Sungeun},
    title     = {Question-Aware Gaussian Experts for Audio-Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13681-13690}
}

raw_affiliation:
[{"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}]


title: Sonic: Shifting Focus to Global Audio Perception in Portrait Animation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 193-203
affiliations: ["Tencent", "Zhejiang University"]
cities: ["Shenzhen", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_Sonic_Shifting_Focus_to_Global_Audio_Perception_in_Portrait_Animation_CVPR_2025_paper.html

authors:
Xiaozhong Ji, Xiaobin Hu, Zhihong Xu, Junwei Zhu, Chuming Lin, Qingdong He, Jiangning Zhang, Donghao Luo, Yi Chen, Qin Lin, Qinglin Lu, Chengjie Wang

abstract:
The study of talking face generation mainly explores the intricacies of synchronizing facial movements and crafting  visually appealing, temporally-coherent animations. However, due to the limited exploration of global audio perception, current approaches predominantly employ auxiliary visual and spatial knowledge to stabilize the movements, which often results in the deterioration of the naturalness and temporal inconsistencies.Considering the essence of audio-driven animation, the audio signal serves as the ideal and unique priors to adjust facial expressions and lip movements, without resorting to interference of any visual signals. Based on this motivation, we propose a novel paradigm, dubbed as Sonic, to shift focus on the exploration of global audio perception. To effectively leverage global audio knowledge, we disentangle it into intra- and inter-clip audio perception and collaborate with both aspects to enhance overall perception. For the intra-clip audio perception, 1). Context-enhanced audio learning, in which long-range intra-clip temporal audio knowledge is extracted to provide facial expression and lip  motion priors implicitly expressed as the tone and speed of speech. 2). Motion-decoupled Controller, in which the motion of the head and expression movement are disentangled and independently controlled by intra-audio clips. Most importantly, for inter-clip audio perception, as a bridge to connect the intra-clips to achieve the global perception, Time-aware position shift fusion, in which the global inter-clip audio information is considered and fused for long-audio inference via through consecutively time-aware shifted windows. Extensive experiments demonstrate that the novel audio-driven paradigm outperform existing SOTA methodologies in terms of video quality, temporally consistency, lip synchronization precision, and motion diversity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_Sonic_Shifting_Focus_to_Global_Audio_Perception_in_Portrait_Animation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Xiaozhong and Hu, Xiaobin and Xu, Zhihong and Zhu, Junwei and Lin, Chuming and He, Qingdong and Zhang, Jiangning and Luo, Donghao and Chen, Yi and Lin, Qin and Lu, Qinglin and Wang, Chengjie},
    title     = {Sonic: Shifting Focus to Global Audio Perception in Portrait Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {193-203}
}

raw_affiliation:
[{"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Multitwine: Multi-Object Compositing with Text and Layout Control
authors: Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang, He Zhang, Andrew Gilbert, John Collomosse, Soo Ye Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8094-8104
affiliations: ["University of Surrey", "Adobe Research"]
cities: ["Guildford", "San Jose"]
countries: ["United Kingdom", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tarres_Multitwine_Multi-Object_Compositing_with_Text_and_Layout_Control_CVPR_2025_paper.html

abstract:
We introduce the first generative model capable of simultaneous multi-object compositing, guided by both text and layout. Our model allows for the addition of multiple objects within a scene, capturing a range of interactions from  simple positional relations (e.g., next to, in front of) to complex actions requiring reposing (e.g., hugging, playing guitar). When an interaction implies additional props, like 'taking a selfie', our model autonomously generates these supporting objects. By jointly training for compositing and subject-driven generation, also known as customization, we achieve a more balanced integration of textual and visual inputs for text-driven object compositing. As a result, we obtain a versatile model with state-of-the-art performance in both tasks. We further present a data generation pipeline leveraging visual and language models to effortlessly synthesize multimodal, aligned training data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tarres_Multitwine_Multi-Object_Compositing_with_Text_and_Layout_Control_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tarres_Multitwine_Multi-Object_Compositing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tarres_2025_CVPR,
    author    = {Tarr\'es, Gemma Canet and Lin, Zhe and Zhang, Zhifei and Zhang, He and Gilbert, Andrew and Collomosse, John and Kim, Soo Ye},
    title     = {Multitwine: Multi-Object Compositing with Text and Layout Control},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8094-8104}
}

raw_affiliation:
[{"affiliation": "University of Surrey", "city": "Guildford", "country": "United Kingdom"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
authors: Hualie Jiang, Zhiqiang Lou, Laiyan Ding, Rui Xu, Minglang Tan, Wenjie Jiang, Rui Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21857-21867
affiliations: ["Insta360 Research", "The Chinese University of Hong Kong, Shenzhen"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_DEFOM-Stereo_Depth_Foundation_Model_Based_Stereo_Matching_CVPR_2025_paper.html

abstract:
Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estimation from binocular matching cues. Recently, monocular relative depth estimation has shown remarkable generalization using vision foundation models. Thus, to facilitate robust stereo matching with monocular depth cues, we incorporate a robust monocular relative depth model into the recurrent stereo-matching framework, building a new framework for depth foundation model-based stereo-matching, DEFOM-Stereo. In the feature extraction stage, we construct the combined context and matching feature encoder by integrating features from conventional CNNs and DEFOM. In the update stage, we use the depth predicted by DEFOM to initialize the recurrent disparity and introduce a scale update module to refine the disparity at the correct scale. DEFOM-Stereo is verified to have much stronger zero-shot generalization compared with SOTA methods. Moreover, DEFOM-Stereo achieves top performance on the KITTI 2012, KITTI 2015, Middlebury, and ETH3D benchmarks, ranking 1^ st  on many metrics. In the joint evaluation under the robust vision challenge, our model simultaneously outperforms previous models on the individual benchmarks, further demonstrating its outstanding capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_DEFOM-Stereo_Depth_Foundation_Model_Based_Stereo_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_DEFOM-Stereo_Depth_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Hualie and Lou, Zhiqiang and Ding, Laiyan and Xu, Rui and Tan, Minglang and Jiang, Wenjie and Huang, Rui},
    title     = {DEFOM-Stereo: Depth Foundation Model Based Stereo Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21857-21867}
}

raw_affiliation:
[{"affiliation": "Insta360 Research", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]


title: Adaptive Rectangular Convolution for Remote Sensing Pansharpening
authors: Xueyang Wang, Zhixin Zheng, Jiandong Shao, Yule Duan, Liang-Jian Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17872-17881
affiliations: ["University of Electronic Science and Technology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Adaptive_Rectangular_Convolution_for_Remote_Sensing_Pansharpening_CVPR_2025_paper.html

abstract:
Recent advancements in convolutional neural network (CNN)-based techniques for remote sensing pansharpening have markedly enhanced image quality. However, conventional convolutional modules in these methods have two critical drawbacks. First, the sampling positions in convolution operations are confined to a fixed square window. Second, the number of sampling points is preset and remains unchanged. Given the diverse object sizes in remote sensing images, these rigid parameters lead to suboptimal feature extraction. To overcome these limitations, we introduce an innovative convolutional module, Adaptive Rectangular Convolution (ARConv). ARConv adaptively learns both the height and width of the convolutional kernel and dynamically adjusts the number of sampling points based on the learned scale. This approach enables ARConv to effectively capture scale-specific features of various objects within an image, optimizing kernel sizes and sampling locations. Additionally, we propose ARNet, a network architecture in which ARConv is the primary convolutional module. Extensive evaluations across multiple datasets reveal the superiority of our method in enhancing pansharpening performance over previous techniques. Ablation studies and visualization further confirm the efficacy of ARConv. The source code can be available at  https://github.com/WangXueyang-uestc/ARConv.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Adaptive_Rectangular_Convolution_for_Remote_Sensing_Pansharpening_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Adaptive_Rectangular_Convolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xueyang and Zheng, Zhixin and Shao, Jiandong and Duan, Yule and Deng, Liang-Jian},
    title     = {Adaptive Rectangular Convolution for Remote Sensing Pansharpening},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17872-17881}
}


title: Video Depth without Video Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7233-7243
affiliations: ["ETH Zurich", "Carnegie Mellon University"]
cities: ["Zurich", "Pittsburgh"]
countries: ["Switzerland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ke_Video_Depth_without_Video_Models_CVPR_2025_paper.html

authors:
Bingxin Ke, Dominik Narnhofer, Shengyu Huang, Lei Ke, Torben Peters, Katerina Fragkiadaki, Anton Obukhov, Konrad Schindler

abstract:
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of large foundation models and the use of synthetic training data, have fueled a renewed interest in video depth. However, naively applying a single-image depth estimator to every frame of a video disregards temporal continuity, which not only leads to flickering but may also break when camera motion causes sudden changes in depth range. An obvious and principled solution would be to build on top of video foundation models, but these come with their own limitations; including expensive training and inference, imperfect 3D consistency, and stitching routines for the fixed-length (short) outputs. We take a step back and demonstrate how to turn a single-image latent diffusion model (LDM) into a state-of-the-art video depth estimator. Our model, which we call RollingDepth, has two main ingredients: (i) a multi-frame depth estimator that is derived from a single-image LDM and maps very short video snippets (typically frame triplets) to depth snippets. (ii) a robust, optimization-based registration algorithm that optimally assembles depth snippets sampled at various different frame rates back into a consistent video. RollingDepth is able to efficiently handle long videos with hundreds of frames and delivers more accurate depth videos than both dedicated video depth estimators and high-performing single-frame models. Project page: https://rollingdepth.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_Video_Depth_without_Video_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_Video_Depth_without_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ke_2025_CVPR,
    author    = {Ke, Bingxin and Narnhofer, Dominik and Huang, Shengyu and Ke, Lei and Peters, Torben and Fragkiadaki, Katerina and Obukhov, Anton and Schindler, Konrad},
    title     = {Video Depth without Video Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7233-7243}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6605-6615
affiliations: ["ZJU", "NUS", "AD Lab, CaiNiao, Alibaba", "NUAA"]
cities: ["Hangzhou", "Singapore", "Hangzhou", "Nanjing"]
countries: ["China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_PointLoRA_Low-Rank_Adaptation_with_Token_Selection_for_Point_Cloud_Learning_CVPR_2025_paper.html

authors:
Song Wang, Xiaolu Liu, Lingdong Kong, Jianyun Xu, Chunyong Hu, Gongfan Fang, Wentong Li, Jianke Zhu, Xinchao Wang

abstract:
Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks. However, as pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources. Parameter-efficient fine-tuning (PEFT) methods offer a promising solution to mitigate these resource requirements, yet most current approaches rely on complex adapter and prompt mechanisms that increase tunable parameters. In this paper, we propose PointLoRA, a simple yet effective method that combines low-rank adaptation (LoRA) with multi-scale token selection to efficiently fine-tune point cloud models. Our approach embeds LoRA layers within the most parameter-intensive components of point cloud transformers, reducing the need for tunable parameters while enhancing global feature capture. Additionally, multi-scale token selection extracts critical local information to serve as prompts for downstream fine-tuning, effectively complementing the global context captured by LoRA. The experimental results across various pre-trained models and three challenging public datasets demonstrate that our approach achieves competitive performance with only 3.43% of the trainable parameters, making it highly effective for resource-constrained applications. Source code is available at: https://github.com/songw-zju/PointLoRA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_PointLoRA_Low-Rank_Adaptation_with_Token_Selection_for_Point_Cloud_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_PointLoRA_Low-Rank_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Song and Liu, Xiaolu and Kong, Lingdong and Xu, Jianyun and Hu, Chunyong and Fang, Gongfan and Li, Wentong and Zhu, Jianke and Wang, Xinchao},
    title     = {PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6605-6615}
}

raw_affiliation:
[{"affiliation": "ZJU", "city": "Hangzhou", "country": "China"}, {"affiliation": "NUS", "city": "Singapore", "country": "Singapore"}, {"affiliation": "AD Lab, CaiNiao, Alibaba", "city": "Hangzhou", "country": "China"}, {"affiliation": "NUAA", "city": "Nanjing", "country": "China"}]


title: HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset
authors: Zedong Chu, Feng Xiong, Meiduo Liu, Jinzhi Zhang, Mingqi Shao, Zhaoxu Sun, Di Wang, Mu Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 304-313
raw_affiliation: [{"affiliation": "Amap, Alibaba Group", "city": "Hangzhou", "country": "China"}]
affiliations: ["Amap, Alibaba Group"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chu_HumanRig_Learning_Automatic_Rigging_for_Humanoid_Character_in_a_Large_CVPR_2025_paper.html

abstract:
With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging--a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, encompassing 11,434 meticulously curated T-posed meshes adhered to a uniform skeleton topology. Capitalizing on this dataset, we introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNN-based methods in handling complex AI-generated meshes. Our approach integrates a Prior-Guided Skeleton Estimator (PGSE) module, which uses 2D skeleton joints to provide a preliminary 3D skeleton, and a Mesh-Skeleton Mutual Attention Network (MSMAN) that fuses skeleton features with 3D mesh features extracted by a U-shaped point transformer. This enables a coarse-to-fine 3D skeleton joint regression and a robust skinning estimation, surpassing previous methods in quality and versatility. This work not only remedies the dataset deficiency in rigging research but also propels the animation industry towards more efficient and automated character rigging pipelines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chu_HumanRig_Learning_Automatic_Rigging_for_Humanoid_Character_in_a_Large_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chu_2025_CVPR,
    author    = {Chu, Zedong and Xiong, Feng and Liu, Meiduo and Zhang, Jinzhi and Shao, Mingqi and Sun, Zhaoxu and Wang, Di and Xu, Mu},
    title     = {HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {304-313}
}


title: GaussHDR: High Dynamic Range Gaussian Splatting via Learning Unified 3D and 2D Local Tone Mapping
authors: Jinfeng Liu, Lingtong Kong, Bo Li, Dan Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5991-6000
affiliations: ["The Hong Kong University of Science and Technology", "vivo Mobile Communication Co., Ltd"]
cities: ["Hong Kong", "Dongguan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_GaussHDR_High_Dynamic_Range_Gaussian_Splatting_via_Learning_Unified_3D_CVPR_2025_paper.html

abstract:
High dynamic range (HDR) novel view synthesis (NVS) aims to reconstruct HDR scenes by leveraging multi-view low dynamic range (LDR) images captured at different exposure levels. Current training paradigms with 3D tone mapping often result in unstable HDR reconstruction, while training with 2D tone mapping reduces the model's capacity to fit LDR images. Additionally, the global tone mapper used in existing methods can impede the learning of both HDR and LDR representations. To address these challenges, we present GaussHDR, which unifies 3D and 2D local tone mapping through 3D Gaussian splatting. Specifically, we design a residual local tone mapper for both 3D and 2D tone mapping that accepts an additional context feature as input. We then propose combining the dual LDR rendering results from both 3D and 2D local tone mapping at the loss level. Finally, recognizing that different scenes may exhibit varying balances between the dual results, we introduce uncertainty learning and use the uncertainties for adaptive modulation. Extensive experiments demonstrate that GaussHDR significantly outperforms state-of-the-art methods in both synthetic and real-world scenarios. The project page for this paper is available at https://liujf1226.github.io/GaussHDR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_GaussHDR_High_Dynamic_Range_Gaussian_Splatting_via_Learning_Unified_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_GaussHDR_High_Dynamic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jinfeng and Kong, Lingtong and Li, Bo and Xu, Dan},
    title     = {GaussHDR: High Dynamic Range Gaussian Splatting via Learning Unified 3D and 2D Local Tone Mapping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5991-6000}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "vivo Mobile Communication Co., Ltd", "city": "Dongguan", "country": "China"}]


title: UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models
authors: Yuning Han, Bingyin Zhao, Rui Chu, Feng Luo, Biplab Sikdar, Yingjie Lao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19186-19196
cities: ["New York", "Singapore", "Medford", "Clemson"]
countries: ["USA", "Singapore", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_UIBDiffusion_Universal_Imperceptible_Backdoor_Attack_for_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent studies show that diffusion models (DMs) are vulnerable to backdoor attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray box and eyeglasses) that contain evident patterns, rendering remarkable attack effects yet easy detection upon human inspection and defensive algorithms. While it is possible to improve stealthiness by reducing the strength of the backdoor, doing so can significantly compromise its generality and effectiveness. In this paper, we propose UIBDiffusion, the universal imperceptible backdoor attack for diffusion models, which allows us to achieve superior attack and generation performance while evading state-of-the-art defenses. We propose a novel trigger generation approach based on universal adversarial perturbations (UAPs) and reveal that such perturbations, which are initially devised for fooling pre-trained discriminative models, can be adapted as potent imperceptible backdoor triggers for DMs. We evaluate UIBDiffusion on multiple types of DMs with different kinds of samplers across various datasets and targets. Experimental results demonstrate that UIBDiffusion brings three advantages: 1) Universality, the imperceptible trigger is universal (i.e., image and model agnostic) where a single trigger is effective to any images and all diffusion models with different samplers; 2) Utility, it achieves comparable generation quality (e.g., FID) and even better attack success rate (i.e., ASR) at low poison rates compared to the prior works; and 3) Undetectability, UIBDiffusion is plausible to human perception and can bypass Elijah and TERD, the SOTA defenses against backdoors for DMs. We will release our backdoor triggers and code.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_UIBDiffusion_Universal_Imperceptible_Backdoor_Attack_for_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_UIBDiffusion_Universal_Imperceptible_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Yuning and Zhao, Bingyin and Chu, Rui and Luo, Feng and Sikdar, Biplab and Lao, Yingjie},
    title     = {UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19186-19196}
}

raw_affiliation:
[{"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tufts University", "city": "Medford", "country": "USA"}, {"affiliation": "Clemson University", "city": "Clemson", "country": "USA"}]

affiliations:
["Columbia University", "National University of Singapore", "Tufts University", "Clemson University"]


title: DiskVPS: Vanishing Point Detector via Hough Transform in a Disk Region
authors: Jianping Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27049-27058
raw_affiliation: [{"affiliation": "Suzhou V ocational University", "city": "Suzhou", "country": "China"}]
affiliations: ["Suzhou V ocational University"]
cities: ["Suzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DiskVPS_Vanishing_Point_Detector_via_Hough_Transform_in_a_Disk_CVPR_2025_paper.html

abstract:
DiskVPS is a novel and robust vanishing point (VP) detection scheme based on Hough Transform (HT) over an image-plane-mapped disk region. In the DiskVPS, the image plane is first mapped into a disk region, which is then partitioned into concentrical ring-shaped subspaces. Each subspace is further partitioned into approximately equal-probability Hough cells. By using individual edges rather than edge pairs as voters, DiskVPS can achieve high accuracy with extreme efficiency. Involving no calibration parameters, DiskVPS is particularly suitable for VP detection in uncalibrated images and thus can be applied in image calibration. A comparative experimental study demonstrates that the basic DiskVPS model without parameter optimization achieved significantly better performance over the SOTA in detection accuracy and processing speed with real-world images. The study also shows that DiskVPS is robust against parameter changes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DiskVPS_Vanishing_Point_Detector_via_Hough_Transform_in_a_Disk_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jianping},
    title     = {DiskVPS: Vanishing Point Detector via Hough Transform in a Disk Region},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27049-27058}
}


title: Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26147-26159
cities: ["Melbourne", "Abu Dhabi", "Suzhou", "Xi'an", "Shanghai", "Shanghai", "Minneapolis", "Ithaca"]
countries: ["Australia", "UAE", "China", "China", "China", "China", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Seeing_Far_and_Clearly_Mitigating_Hallucinations_in_MLLMs_with_Attention_CVPR_2025_paper.html

authors:
Feilong Tang, Chengzhi Liu, Zhongxing Xu, Ming Hu, Zile Huang, Haochen Xue, Ziyang Chen, Zelin Peng, Zhiwei Yang, Sijin Zhou, Wenxue Li, Yulong Li, Wenxuan Song, Shiyan Su, Wei Feng, Jionglong Su, Mingquan Lin, Yifan Peng, Xuelian Cheng, Imran Razzak, Zongyuan Ge

abstract:
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations. In this work, hallucinations are categorized into two main types: initial hallucinations and snowball hallucinations. We argue that adequate contextual information can be extracted directly from the token interaction process. Inspired by causal inference in decoding strategy, we propose to leverage causal masks to establish information propagation between multimodal tokens. The hypothesis is that insufficient interaction between those tokens may lead the model to rely on outlier tokens, overlooking dense and rich contextual cues. Therefore, we propose to intervene in the propagation process by tackling outlier tokens to enhance in-context inference. With this goal, we present FarSight, a versatile plug-and-play decoding strategy to reduce attention interference from outlier tokens merely by optimizing the causal mask. The heart of our method is effective token propagation. We design an attention register structure within the upper triangular matrix of the causal mask, dynamically allocating attention capture attention diverted to outlier tokens. Moreover, a positional awareness encoding method with a diminishing masking rate is proposed, allowing the model to attend to further preceding tokens, especially for video sequence tasks. With extensive experiments, FarSight demonstrates significant hallucination-mitigating performance across different MLLMs on both image and video benchmarks, proving its effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Seeing_Far_and_Clearly_Mitigating_Hallucinations_in_MLLMs_with_Attention_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Feilong and Liu, Chengzhi and Xu, Zhongxing and Hu, Ming and Huang, Zile and Xue, Haochen and Chen, Ziyang and Peng, Zelin and Yang, Zhiwei and Zhou, Sijin and Li, Wenxue and Li, Yulong and Song, Wenxuan and Su, Shiyan and Feng, Wei and Su, Jionglong and Lin, Mingquan and Peng, Yifan and Cheng, Xuelian and Razzak, Imran and Ge, Zongyuan},
    title     = {Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26147-26159}
}

raw_affiliation:
[{"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "XJTLU", "city": "Suzhou", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Minnesota", "city": "Minneapolis", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]

affiliations:
["Monash University", "MBZUAI", "XJTLU", "Northwestern Polytechnical University", "Shanghai Jiaotong University", "Fudan University", "University of Minnesota", "Cornell University"]


title: Towards Autonomous Micromobility through Scalable Urban Simulation
authors: Wayne Wu, Honglin He, Chaoyuan Zhang, Jack He, Seth Z. Zhao, Ran Gong, Quanyi Li, Bolei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27553-27563
affiliations: ["University of California, Los Angeles", "University of Washington"]
cities: ["Los Angeles", "Seattle"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Towards_Autonomous_Micromobility_through_Scalable_Urban_Simulation_CVPR_2025_paper.html

abstract:
Micromobility, which utilizes lightweight devices moving in urban public spaces - such as delivery robots and electric wheelchairs - emerges as a promising alternative to vehicular mobility. Current micromobility depends mostly on human manual operation (in-person or remote control), which raises safety and efficiency concerns when navigating busy urban environments full of obstacles and pedestrians. Assisting humans with AI agents in maneuvering micromobility devices presents a viable solution for enhancing safety and efficiency. In this work, we present a scalable urban simulation solution to advance autonomous micromobility. First, we build URBAN-SIM -- a high-performance robot learning platform for large-scale training of embodied agents in interactive urban scenes. URBAN-SIM contains three critical modules: Hierarchical Urban Generation pipeline, Interactive Dynamics Generation strategy, and Asynchronous Scene Sampling scheme, to improve the diversity, realism, and efficiency of robot learning in simulation. Then, we propose URBAN-BENCH -- a suite of essential tasks and benchmarks to gauge various capabilities of the AI agents in achieving autonomous micromobility. URBAN-BENCH includes eight tasks based on three core skills of the agents: Urban Locomotion, Urban Navigation, and Urban Traverse. We evaluate four robots with heterogeneous embodiments, such as the wheeled and legged robots, across these tasks. Experiments on diverse terrains and urban structures reveal each robot's unique strengths and limitations. This work will be open-sourced and under sustainable maintenance to foster future research in autonomous micromobility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Towards_Autonomous_Micromobility_through_Scalable_Urban_Simulation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Wayne and He, Honglin and Zhang, Chaoyuan and He, Jack and Zhao, Seth Z. and Gong, Ran and Li, Quanyi and Zhou, Bolei},
    title     = {Towards Autonomous Micromobility through Scalable Urban Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27553-27563}
}

raw_affiliation:
[{"affiliation": "University of California, Los Angeles", "city": "Los Angeles", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}]


authors: Dong Zhao, Jinlong Li, Shuang Wang, Mengyao Wu, Qi Zang, Nicu Sebe, Zhun Zhong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15043-15054
cities: ["Xi'an", "Trento", "Hefei"]
countries: ["China", "Italy", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_FisherTune_Fisher-Guided_Robust_Tuning_of_Vision_Foundation_Models_for_Domain_CVPR_2025_paper.html

title:
FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation

abstract:
Vision Foundation Models (VFMs) excel in generalization due to large-scale pretraining, but fine-tuning them for Domain Generalized Semantic Segmentation (DGSS) while maintaining this ability remains a challenge. Existing approaches either selectively fine-tune parameters or freeze the VFMs and update only the adapters,  both of which may underutilize the VFMs' full potential in DGSS tasks. We observe that domain-sensitive parameters in VFMs, arising from task and distribution differences, can hinder generalization. To address this, we propose FisherTune, a robust fine-tuning method guided by the Domain-Related Fisher Information Matrix (DR-FIM). DR-FIM measures parameter sensitivity across tasks and domains, enabling selective updates that preserve generalization and enhance DGSS adaptability. To stabilize DR-FIM estimation, FisherTune incorporates variational inference, treating parameters as Gaussian-distributed variables and leveraging pretrained priors. Extensive experiments show that FisherTune achieves superior cross-domain segmentation while maintaining generalization, outperforming both selective-parameter and adapter-based methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_FisherTune_Fisher-Guided_Robust_Tuning_of_Vision_Foundation_Models_for_Domain_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_FisherTune_Fisher-Guided_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Dong and Li, Jinlong and Wang, Shuang and Wu, Mengyao and Zang, Qi and Sebe, Nicu and Zhong, Zhun},
    title     = {FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15043-15054}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Xidian University, Shaanxi, China", "city": "Xi'an", "country": "China"}, {"affiliation": "Department of Information Engineering and Computer Science, University of Trento, Italy", "city": "Trento", "country": "Italy"}, {"affiliation": "School of Computer Science and Information Engineering, Hefei University of Technology, China", "city": "Hefei", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Xidian University, Shaanxi, China", "Department of Information Engineering and Computer Science, University of Trento, Italy", "School of Computer Science and Information Engineering, Hefei University of Technology, China"]


title: Language-Assisted Debiasing and Smoothing for Foundation Model-Based Semi-Supervised Learning
authors: Na Zheng, Xuemeng Song, Xue Dong, Aashish Nikhil Ghosh, Liqiang Nie, Roger Zimmermann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25708-25717
cities: ["Singapore", "Hong Kong", "Beijing", "Shenzhen"]
countries: ["Singapore", "Hong Kong", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Language-Assisted_Debiasing_and_Smoothing_for_Foundation_Model-Based_Semi-Supervised_Learning_CVPR_2025_paper.html

abstract:
Recent studies have focused on introducing pre-trained foundation models into semi-supervised learning (SSL) tasks. Nevertheless, these foundation models can exhibit biases toward different classes and tend to generate imbalanced pseudo-labels for SSL. Thus, efforts have been made to introduce the logit adjustment offset to reduce the inherent bias in foundation models for SSL tasks.Despite their success,  existing foundation model-based SSL methods face challenges: 1) unreliability in the estimated logit adjustment offset, 2) overlooking the potential of linguistic knowledge in capturing model biases and 3) fail to fully exploit the unlabeled samples. To address these issues, we propose   Language-Assisted Debiasing and Smoothing framework, namely LADaS, for foundation model-based SSL.   It consists of two components: 1) Language-assisted Pseudo-Label Debiasing (LPLD) to reduce biases in foundation models, and 2) Language-aware Pseudo-Label Smoothing (LPLS) to fully exploit  low-confidence samples to facilitate SSL training. In particular, LPLD introduces a reliability score to dynamically assess the  reliability of the logit adjustment. Additionally, it incorporates a language-oriented preference to reduce model biases using linguistic knowledge derived from pre-trained language models. Finally,  LPLS introduces  language-aware soft labels and devises language-aware pseudo-label smoothing loss to guide the learning of unlabeled samples with low-quality pseudo-labels. Extensive experiments demonstrate the superiority of our proposed LADaS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Language-Assisted_Debiasing_and_Smoothing_for_Foundation_Model-Based_Semi-Supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Language-Assisted_Debiasing_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Na and Song, Xuemeng and Dong, Xue and Ghosh, Aashish Nikhil and Nie, Liqiang and Zimmermann, Roger},
    title     = {Language-Assisted Debiasing and Smoothing for Foundation Model-Based Semi-Supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25708-25717}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}]

affiliations:
["National University of Singapore", "City University of Hong Kong", "Tsinghua University", "Harbin Institute of Technology (Shenzhen)"]


title: EdgeMovingNet: Edge-preserving Point Cloud Reconstruction via Joint Geometry Features
authors: Xinran Yang, Donghao Ji, Yuanqi Li, Junyuan Xie, Jie Guo, Yanwen Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22150-22160
affiliations: ["Nanjing University", "School of Software, North University of China"]
cities: ["Nanjing", "Unknown"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_EdgeMovingNet_Edge-preserving_Point_Cloud_Reconstruction_via_Joint_Geometry_Features_CVPR_2025_paper.html

abstract:
Point cloud reconstruction is a critical process in 3D representation and reverse engineering. When it comes to CAD models, edges are significant features that play a crucial role in characterizing the geometry of 3D shapes. However, few points are exactly sampled on edges during acquisition, resulting in apparent artifacts for the reconstruction task. Upsampling point cloud is a direct technical route, but there is a main challenge that the upsampled points may not align with the model edge accurately. To overcome this, we develop an integrated framework to estimate edges by joint regression of three geometry features--point-to-edge direction, point-to-edge distance and point normal. Benefiting these features, we implement a novel refinement process to move and produce more points which lie accurately on edges of the model, allowing for high-quality edge-preserving reconstruction. Experiments and comparisons against previous methods demonstrate our method's effectiveness and superiority.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_EdgeMovingNet_Edge-preserving_Point_Cloud_Reconstruction_via_Joint_Geometry_Features_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_EdgeMovingNet_Edge-preserving_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Xinran and Ji, Donghao and Li, Yuanqi and Xie, Junyuan and Guo, Jie and Guo, Yanwen},
    title     = {EdgeMovingNet: Edge-preserving Point Cloud Reconstruction via Joint Geometry Features},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22150-22160}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Software, North University of China", "city": "Unknown", "country": "China"}]


title: AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20569-20579
cities: ["Riverside", "Upton", "Ann Arbor", "Adelphi"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_AdMiT_Adaptive_Multi-Source_Tuning_in_Dynamic_Environments_CVPR_2025_paper.html

authors:
Xiangyu Chang, Fahim Faisal Niloy, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit Roy-Chowdhury

abstract:
Incorporating transformer models into edge devices poses a significant challenge due to the computational demands of adapting these large models across diverse applications. Parameter-efficient tuning (PET) methods (e.g. LoRA, Adapter, Visual Prompt Tuning, etc.) allow for targeted adaptation by modifying only small parts of the transformer model. However, adapting to dynamic unlabeled target distributions at the test time remains complex. To address this, we introduce AdMiT: Adaptive Multi -Source Tuning in Dynamic Environments. AdMiT innovates by pre-training a set of PET modules, each optimized for different source distributions or tasks, and dynamically selecting and integrating a sparse subset of relevant modules when encountering a new, few-shot, unlabeled target distribution. This integration leverages Kernel Mean Embedding (KME)-based matching to align the target distribution with relevant source knowledge efficiently, without requiring additional routing networks or hyperparameter tuning. AdMiT achieves adaptation with a single inference step, making it particularly suitable for resource-constrained edge deployments. Furthermore, AdMiT preserves privacy by performing an adaptation locally on each edge device, without the need for data exchange. Our theoretical analysis establishes guarantees for AdMiT's generalization, while extensive benchmarks demonstrate that AdMiT consistently outperforms other PET methods across a range of tasks, achieving robust and efficient adaptation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_AdMiT_Adaptive_Multi-Source_Tuning_in_Dynamic_Environments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_AdMiT_Adaptive_Multi-Source_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Xiangyu and Niloy, Fahim Faisal and Ahmed, Sk Miraj and Krishnamurthy, Srikanth V. and Guler, Basak and Swami, Ananthram and Oymak, Samet and Roy-Chowdhury, Amit},
    title     = {AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20569-20579}
}

raw_affiliation:
[{"affiliation": "University of California, Riverside", "city": "Riverside", "country": "USA"}, {"affiliation": "Brookhaven National Laboratory", "city": "Upton", "country": "USA"}, {"affiliation": "University of Michigan, Ann Arbor", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "DEVCOM Army Research Laboratory", "city": "Adelphi", "country": "USA"}]

affiliations:
["University of California, Riverside", "Brookhaven National Laboratory", "University of Michigan, Ann Arbor", "DEVCOM Army Research Laboratory"]


title: Unbiased Video Scene Graph Generation via Visual and Semantic Dual Debiasing
authors: Yanjun Li, Zhaoyang Li, Honghui Chen, Lizhi Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19047-19056
cities: ["Hefei", "Fuzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Unbiased_Video_Scene_Graph_Generation_via_Visual_and_Semantic_Dual_CVPR_2025_paper.html

abstract:
Video Scene Graph Generation (VidSGG) aims to capture dynamic relationships among entities by sequentially analyzing video frames and integrating visual and semantic information. However, VidSGG is challenged by significant biases that skew predictions. To mitigate these biases, we propose a VIsual and Semantic Awareness (VISA) framework for unbiased VidSGG. VISA addresses visual bias through an innovative memory update mechanism that enhances object representations and concurrently reduces semantic bias by iteratively integrating object features with comprehensive semantic information derived from triplet relationships. This visual-semantics dual debiasing approach results in more unbiased representations of complex scene dynamics. Extensive experiments demonstrate the effectiveness of our method, where VISA outperforms existing unbiased VidSGG approaches by a substantial margin (e.g., +13.1% improvement in mR@20 and mR@50 for the SGCLS task under Semi Constraint).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Unbiased_Video_Scene_Graph_Generation_via_Visual_and_Semantic_Dual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Unbiased_Video_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yanjun and Li, Zhaoyang and Chen, Honghui and Xu, Lizhi},
    title     = {Unbiased Video Scene Graph Generation via Visual and Semantic Dual Debiasing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19047-19056}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "College of Physics and Information Engineering, Fuzhou University", "city": "Fuzhou", "country": "China"}]

affiliations:
["University of Science and Technology of China", "College of Physics and Information Engineering, Fuzhou University"]


title: Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes
authors: JunYong Choi, Min-cheol Sagong, SeokYeong Lee, Seung-Won Jung, Ig-Jae Kim, Junghyun Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5773-5782
cities: ["Seoul", "Seoul", "Daejeon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_Channel-wise_Noise_Scheduled_Diffusion_for_Inverse_Rendering_in_Indoor_Scenes_CVPR_2025_paper.html

abstract:
We propose a diffusion-based inverse rendering framework that decomposes a single RGB image into geometry, material, and lighting. Inverse rendering is inherently ill-posed, making it difficult to predict a single accurate solution. To address this challenge, recent generative model-based methods aim to present a range of possible solutions. However, finding a single accurate solution and generating diverse solutions can be conflicting. In this paper, we propose a channel-wise noise scheduling approach that allows a single diffusion model architecture to achieve two conflicting objectives. The resulting two diffusion models, trained with different channel-wise noise schedules, can predict a single highly accurate solution and present multiple possible solutions. The experimental results demonstrate the superiority of our two models in terms of both diversity and accuracy, which translates to enhanced performance in downstream applications such as object insertion and material editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_Channel-wise_Noise_Scheduled_Diffusion_for_Inverse_Rendering_in_Indoor_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_Channel-wise_Noise_Scheduled_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, JunYong and Sagong, Min-cheol and Lee, SeokYeong and Jung, Seung-Won and Kim, Ig-Jae and Cho, Junghyun},
    title     = {Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5773-5782}
}

raw_affiliation:
[{"affiliation": "Korea Institute of Science and Technology(KIST)", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "AI-Robotics, KIST School, University of Science and Technology", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Yonsei-KIST Convergence Research Institute", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Korea Institute of Science and Technology(KIST)", "Korea University", "AI-Robotics, KIST School, University of Science and Technology", "Yonsei-KIST Convergence Research Institute"]


title: Targeted Forgetting of Image Subgroups in CLIP Models
authors: Zeliang Zhang, Gaowen Liu, Charles Fleming, Ramana Rao Kompella, Chenliang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9870-9880
affiliations: ["University of Rochester", "Cisco Research"]
cities: ["Rochester", "Unknown"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Targeted_Forgetting_of_Image_Subgroups_in_CLIP_Models_CVPR_2025_paper.html

abstract:
Foundation models (FMs) such as CLIP have demonstrated impressive zero-shot performance across various tasks by leveraging large-scale, unsupervised pre-training. However, they often inherit harmful or unwanted knowledge from noisy internet-sourced datasets, compromising their reliability in real-world applications. Existing model unlearning methods either rely on access to pre-trained datasets or focus on coarse-grained unlearning (e.g., entire classes), leaving a critical gap for fine-grained unlearning. In this paper, we address the challenging scenario of selectively forgetting specific portions of knowledge within a class--without access to pre-trained data--while preserving the model's overall performance. We propose a novel three-stage approach that progressively unlearns targeted knowledge while mitigating over-forgetting. It consists of (1) a forgetting stage to fine-tune the CLIP on samples to be forgotten, (2) a reminding stage to restore performance on retained samples, and (3) a restoring stage to recover zero-shot capabilities using model souping. Additionally, we introduce knowledge distillation to handle the distribution disparity between forgetting/retaining samples and unseen pre-trained data. Extensive experiments on CIFAR-10, ImageNet-1K, and style datasets demonstrate that our approach effectively unlearns specific subgroups while maintaining strong zero-shot performance on semantically similar subgroups and other categories, significantly outperforming baseline unlearning methods, which lose effectiveness under the CLIP unlearning setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Targeted_Forgetting_of_Image_Subgroups_in_CLIP_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Targeted_Forgetting_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zeliang and Liu, Gaowen and Fleming, Charles and Kompella, Ramana Rao and Xu, Chenliang},
    title     = {Targeted Forgetting of Image Subgroups in CLIP Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9870-9880}
}

raw_affiliation:
[{"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "Cisco Research", "city": "Unknown", "country": "USA"}]


title: Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18346-18357
cities: ["Menlo Park", "Atlanta", "Urbana", "Suwon", "Madison"]
countries: ["USA", "USA", "USA", "South Korea", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Unleashing_In-context_Learning_of_Autoregressive_Models_for_Few-shot_Image_Manipulation_CVPR_2025_paper.html

authors:
Bolin Lai, Felix Juefei-Xu, Miao Liu, Xiaoliang Dai, Nikhil Mehta, Chenguang Zhu, Zeyi Huang, James M. Rehg, Sangmin Lee, Ning Zhang, Tong Xiao

abstract:
Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been applied for instructions that are underrepresented in the training set, or difficult to describe purely in language. However, learning from visual prompts requires strong reasoning capability, which diffusion models are struggling with. To address this issue, we introduce a novel multi-modal autoregressive model, dubbed InstaManip, that can instantly learn a new image manipulation operation from textual and visual guidance via in-context learning, and apply it to new query images. Specifically, we propose an innovative group self-attention mechanism to break down the in-context learning process into two separate stages -- learning and applying, which simplifies the complex problem into two easier tasks. We also introduce a relation regularization method to further disentangle image transformation features from irrelevant contents in exemplar images. Extensive experiments suggest that our method surpasses previous few-shot image manipulation models by a notable margin (>=19% in human evaluation). We also find our model can be further boosted by increasing the number or diversity of exemplar images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Unleashing_In-context_Learning_of_Autoregressive_Models_for_Few-shot_Image_Manipulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_Unleashing_In-context_Learning_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Bolin and Juefei-Xu, Felix and Liu, Miao and Dai, Xiaoliang and Mehta, Nikhil and Zhu, Chenguang and Huang, Zeyi and Rehg, James M. and Lee, Sangmin and Zhang, Ning and Xiao, Tong},
    title     = {Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18346-18357}
}

raw_affiliation:
[{"affiliation": "GenAI, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}, {"affiliation": "University of Wisconsin\u2013Madison", "city": "Madison", "country": "USA"}]

affiliations:
["GenAI, Meta", "Georgia Institute of Technology", "University of Illinois Urbana-Champaign", "Sungkyunkwan University", "University of Wisconsin\u2013Madison"]


title: Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29847-29857
raw_affiliation: [{"affiliation": "Trinity College Dublin", "city": "Dublin", "country": "Ireland"}]
affiliations: ["Trinity College Dublin"]
cities: ["Dublin"]
countries: ["Ireland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Maniparambil_Harnessing_Frozen_Unimodal_Encoders_for_Flexible_Multimodal_Alignment_CVPR_2025_paper.html

authors:
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, Noel E. O'Connor

abstract:
Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language applications. However, recent findings suggest high semantic similarity between well-trained unimodal encoders, which raises a key question: Are semantically similar embedding spaces separated only by simple projection transformations? To validate this, we propose a novel framework that aligns vision and language using frozen unimodal encoders. It involves selecting semantically similar encoders in the latent space, curating a concept-rich dataset of image-caption pairs, and training simple MLP projectors. We evaluated our approach on various tasks involving both strong unimodal vision (0-shot localization) and language encoders (multi-lingual, long context) and show that simple Projectors retain unimodal capabilities in joint embedding space. Furthermore, our best model, utilizing DINOv2 and All-Roberta-Large text encoder, achieves 76(%) accuracy on ImageNet with a 20-fold reduction in data and 65-fold reduction in compute requirements compared to multimodal alignment where models are trained from scratch. The proposed framework enhances the accessibility of multimodal model development while enabling flexible adaptation across diverse scenarios. Code and curated datasets are available at github.com/mayug/freeze-align.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Maniparambil_Harnessing_Frozen_Unimodal_Encoders_for_Flexible_Multimodal_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maniparambil_Harnessing_Frozen_Unimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Maniparambil_2025_CVPR,
    author    = {Maniparambil, Mayug and Akshulakov, Raiymbek and Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and Singh, Ankit and O'Connor, Noel E.},
    title     = {Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29847-29857}
}


title: Feature Information Driven Position Gaussian Distribution Estimation for Tiny Object Detection
authors: Jinghao Bian, Mingtao Feng, Weisheng Dong, Fangfang Wu, Jianqiao Luo, Yaonan Wang, Guangming Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30376-30386
cities: ["Xi'an", "Changsha", "Nanchang"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bian_Feature_Information_Driven_Position_Gaussian_Distribution_Estimation_for_Tiny_Object_CVPR_2025_paper.html

abstract:
Tiny object detection remains challenging in spite of the success of generic detectors. The dramatic performance degradation of generic detectors on tiny objects is mainly due to the the weak representations of extremely limited pixels. To address this issue, we propose a plug-and-play architecture to enhance the extinguished regions. We for the first time exploit the regions to be enhanced from the perspective of pixel-wise amount of information. Specifically, we model the entire image pixels feature information by minimizing Information Entropy loss, generating an information map to attentively highlight weak activated regions in an unsupervised way. To effectively assist the above phase with more attention to tiny objects, we next introduce the Position Gaussian Distribution Map, explicitly modeled using a Gaussian Mixture distribution, where each Gaussian component's parameters depend on the position and size of object instance labels, serving as supervision for further feature enhancement. Taking the information map as prior knowledge guidance, we construct a multi-scale position gaussian distribution map prediction module, simultaneously modulating the information map and distribution map to focus on tiny objects during training. Extensive experiments on three public tiny object datasets demonstrate the superiority of our method over current state-of-the-art competitors.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bian_Feature_Information_Driven_Position_Gaussian_Distribution_Estimation_for_Tiny_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bian_Feature_Information_Driven_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bian_2025_CVPR,
    author    = {Bian, Jinghao and Feng, Mingtao and Dong, Weisheng and Wu, Fangfang and Luo, Jianqiao and Wang, Yaonan and Shi, Guangming},
    title     = {Feature Information Driven Position Gaussian Distribution Estimation for Tiny Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30376-30386}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "Jiangxi Communication Terminal Industrial Technology Research Institute", "city": "Nanchang", "country": "China"}]

affiliations:
["Xidian University", "Hunan University", "Jiangxi Communication Terminal Industrial Technology Research Institute"]


title: Enhancing Diversity for Data-free Quantization
authors: Kai Zhao, Zhihao Zhuang, Miao Zhang, Chenjuan Guo, Yang Shu, Bin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20969-20978
affiliations: ["Aalborg University", "East China Normal University", "HIT Shenzhen"]
cities: ["Aalborg", "Shanghai", "Shenzhen"]
countries: ["Denmark", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Enhancing_Diversity_for_Data-free_Quantization_CVPR_2025_paper.html

abstract:
Model quantization is an effective way to compress deep neural networks and accelerate the inference time on edge devices. Existing quantization methods usually require original data for calibration during the compressing process, which may be inaccessible due to privacy issues. A common way is to generate calibration data to mimic the origin data. However, the generators in these methods have the mode collapse problem, making them unable to synthesize diverse data. To solve this problem, we leverage the information from the full-precision model and enhance both inter-class and intra-class diversity for generating better calibration data, by devising a multi-layer features mixer and normalization flow based attention. Besides, novel regulation losses are proposed to make the generator produce diverse data with more patterns from the perspective of activated feature values and for the quantized model to learn better clip ranges adaptive to our diverse calibration data. Extensive experiments show that our method achieves state-of-the-art quantization results for both Transformer and CNN architectures. In addition, we visualize the generated data to verify that our strategies can effectively handle the mode collapse issue. Our codes are available at https://anonymous.4open.science/r/DFQ-84E6 and will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Enhancing_Diversity_for_Data-free_Quantization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Enhancing_Diversity_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Kai and Zhuang, Zhihao and Zhang, Miao and Guo, Chenjuan and Shu, Yang and Yang, Bin},
    title     = {Enhancing Diversity for Data-free Quantization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20969-20978}
}

raw_affiliation:
[{"affiliation": "Aalborg University", "city": "Aalborg", "country": "Denmark"}, {"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "HIT Shenzhen", "city": "Shenzhen", "country": "China"}]


title: SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
authors: Chunlin Yu, Hanqing Wang, Ye Shi, Haoyang Luo, Sibei Yang, Jingyi Yu, Jingya Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1691-1701
raw_affiliation: [{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}]
affiliations: ["ShanghaiTech University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_SeqAfford_Sequential_3D_Affordance_Reasoning_via_Multimodal_Large_Language_Model_CVPR_2025_paper.html

abstract:
3D affordance segmentation aims to link human instructions to touchable regions of 3D objects for embodied manipulations. Existing efforts typically adhere to single-object, single-affordance paradigms, where each affordance type or explicit instruction strictly corresponds to a specific affordance region and are unable to handle long-horizon tasks. Such a paradigm cannot actively reason about complex user intentions that often imply sequential affordances. In this paper, we introduce the Sequential 3D Affordance Reasoning task, which extends the traditional paradigm by reasoning from cumbersome user intentions and then decomposing them into a series of segmentation maps. Toward this, we construct the first instruction-based affordance segmentation benchmark that includes reasoning over both single and sequential affordances, comprising 180K instruction-point cloud pairs. Based on the benchmark, we propose our model, SeqAfford, to unlock the 3D multi-modal large language model with additional affordance segmentation abilities, which ensures reasoning with world knowledge and fine-grained affordance grounding in a cohesive framework. We further introduce a multi-granular language-point integration module to endow 3D dense prediction. Extensive experimental evaluations show that our model excels over well-established methods and exhibits open-world generalization with sequential reasoning abilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_SeqAfford_Sequential_3D_Affordance_Reasoning_via_Multimodal_Large_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_SeqAfford_Sequential_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Chunlin and Wang, Hanqing and Shi, Ye and Luo, Haoyang and Yang, Sibei and Yu, Jingyi and Wang, Jingya},
    title     = {SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1691-1701}
}


title: DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation
authors: Amin Karimi, Charalambos Poullis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4584-4594
affiliations: ["Immersive and Creative Technologies Lab, Concordia University"]
cities: ["Montreal"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Karimi_DSV-LFS_Unifying_LLM-Driven_Semantic_Cues_with_Visual_Features_for_Robust_CVPR_2025_paper.html

abstract:
Few-shot semantic segmentation (FSS) aims to enable models to segment novel/unseen object classes using only a limited number of labeled examples. However, current FSS methods frequently struggle with generalization due to incomplete and biased feature representations, especially when support images do not capture the full appearance variability of the target class. To improve the FSS pipeline, we propose a novel framework that utilizes large language models (LLMs) to adapt general class semantic information to the query image. Furthermore, the framework employs dense pixel-wise matching to identify similarities between query and support images, resulting in enhanced FSS performance. Inspired by reasoning-based segmentation frameworks, our method, named DSV-LFS, introduces an additional token into the LLM vocabulary, allowing a multimodal LLM to generate a "semantic prompt" from class descriptions. In parallel, a dense matching module identifies visual similarities between the query and support images, generating a "visual prompt". These prompts are then jointly employed to guide the prompt-based decoder for accurate segmentation of the query image. Comprehensive experiments on the benchmark datasets Pascal-5i and COCO-20i demonstrate that our framework achieves state-of-the-art performance-by a significant margin-demonstrating superior generalization to novel classes and robustness across diverse scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Karimi_DSV-LFS_Unifying_LLM-Driven_Semantic_Cues_with_Visual_Features_for_Robust_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Karimi_DSV-LFS_Unifying_LLM-Driven_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Karimi_2025_CVPR,
    author    = {Karimi, Amin and Poullis, Charalambos},
    title     = {DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4584-4594}
}

raw_affiliation:
[{"affiliation": "Immersive and Creative Technologies Lab, Concordia University", "city": "Montreal", "country": "Canada"}]


title: Revisiting Generative Replay for Class Incremental Object Detection
authors: Shizhou Zhang, Xueqiang Lv, Yinghui Xing, Qirui Wu, Di Xu, Yanning Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20340-20349
affiliations: ["Northwestern Polytechnical University", "Huawei Cloud Computing Technologies Co., Ltd"]
cities: ["Xi'an", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Revisiting_Generative_Replay_for_Class_Incremental_Object_Detection_CVPR_2025_paper.html

abstract:
Generative replay has gained significant attention in class-incremental learning; however, its application to Class Incremental Object Detection (CIOD) remains limited due to the challenges in generating complex images with precise spatial arrangements. In this study, motivated by the observation that the forgetting of prior knowledge is predominantly present in the classification sub-task as opposed to the localization sub-task, we revisit the generative replay method for class incremental object detection. Our method utilize a standard Stable Diffusion model to generate image-level replay data for all old and new tasks. Accordingly, the old detector and a stage-wise detector are conducted on the synthetic images respectively to determine the bounding box positions through pseudo-labeling. Furthermore, we propose to use a Similarity-based Cross Sampling mechanism to select more valuable confusing data between old and new tasks to more effectively mitigate catastrophic forgetting and reduce the false alarm rate for the new task. Finally, all synthetic and real data are integrated for current-stage detector training, where the images generated for previous tasks are highly beneficial in minimizing the forgetting of existing knowledge, while those synthesized for the new task can help bridge the domain gap between real and synthetic images. We conducted extensive experiments on PASCAL VOC 2007 and MS COCO benchmark datasets in multiple settings to showcase the efficacy of our proposed approach, which achieves state-of-the-art results. The code are available at https://github.com/qiangzai-lv/RGR-IOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Revisiting_Generative_Replay_for_Class_Incremental_Object_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shizhou and Lv, Xueqiang and Xing, Yinghui and Wu, Qirui and Xu, Di and Zhang, Yanning},
    title     = {Revisiting Generative Replay for Class Incremental Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20340-20349}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Huawei Cloud Computing Technologies Co., Ltd", "city": "Shenzhen", "country": "China"}]


authors: Krispin Wandel, Hesheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1138-1147
raw_affiliation: [{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]
affiliations: ["Shanghai Jiao Tong University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wandel_SemAlign3D_Semantic_Correspondence_between_RGB-Images_through_Aligning_3D_Object-Class_Representations_CVPR_2025_paper.html

title:
SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations

abstract:
Semantic correspondence made tremendous progress through the recent advancements of large vision models (LVM). While these LVMs have been shown to reliably capture local semantics, the same can currently not be said for capturing global geometric relationships between semantic object regions. This problem leads to unreliable performance for semantic correspondence between images with extreme view variation. In this work, we aim to leverage monocular depth estimates to capture these geometric relationships for more robust and data-efficient semantic correspondence. First, we introduce a simple but effective method to build 3D object-class representations from monocular depth estimates and LVM features using a sparsely annotated image correspondence dataset. Second, we formulate an alignment energy that can be minimized using gradient descent to obtain an alignment between the 3D object-class representation and the object-class instance in the input RGB-image. Our method achieves state-of-the-art matching accuracy in multiple categories on the challenging SPair-71k dataset, increasing the PCK@0.1 score by more than 10 points on three categories and overall by 3.3 points from 85.6% to 88.9%. Additional resources and code are available at dub.sh/semalign3d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wandel_SemAlign3D_Semantic_Correspondence_between_RGB-Images_through_Aligning_3D_Object-Class_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wandel_SemAlign3D_Semantic_Correspondence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wandel_2025_CVPR,
    author    = {Wandel, Krispin and Wang, Hesheng},
    title     = {SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1138-1147}
}


title: Bridging Viewpoint Gaps: Geometric Reasoning Boosts Semantic Correspondence
authors: Qiyang Qian, Hansheng Chen, Masayoshi Tomizuka, Kurt Keutzer, Qianqian Wang, Chenfeng Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11579-11589
affiliations: ["University of California, Berkeley", "Stanford University"]
cities: ["Berkeley", "Stanford"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_Bridging_Viewpoint_Gaps_Geometric_Reasoning_Boosts_Semantic_Correspondence_CVPR_2025_paper.html

abstract:
Finding semantic correspondences between images is a challenging problem in computer vision, particularly under significant viewpoint changes. Previous methods rely on semantic features from pre-trained 2D models like Stable Diffusion and DINOv2, which often struggle to extract viewpoint-invariant features. To overcome this, we propose a novel approach that integrates geometric and semantic reasoning. Unlike prior methods relying on heuristic geometric enhancements, our framework fine-tunes DUSt3R on synthetic cross-instance data to reconstruct distinct objects in an aligned 3D space. By learning to deform these objects into similar shapes using semantic supervision, we enable efficient KNN-based geometric matching, followed by sparse semantic matching within local KNN candidates. While trained on synthetic data, our method generalizes effectively to real-world images, achieving up to 7.4-point improvements in zero-shot settings on the rigid-body subset of Spair-71K and up to 19.6-point gains under extreme viewpoint variations. Additionally, it accelerates runtime by up to 40 times, demonstrating both its robustness to viewpoint changes and its efficiency for practical applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_Bridging_Viewpoint_Gaps_Geometric_Reasoning_Boosts_Semantic_Correspondence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qian_Bridging_Viewpoint_Gaps_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Qiyang and Chen, Hansheng and Tomizuka, Masayoshi and Keutzer, Kurt and Wang, Qianqian and Xu, Chenfeng},
    title     = {Bridging Viewpoint Gaps: Geometric Reasoning Boosts Semantic Correspondence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11579-11589}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
authors: Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang, Zhengzhong Tu, Yue Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10193-10202
affiliations: ["University of Southern California", "ETH Z\u00a8urich", "Texas A&M University"]
cities: ["Los Angeles", "Zurich", "College Station"]
countries: ["USA", "Switzerland", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DPU_Dynamic_Prototype_Updating_for_Multimodal_Out-of-Distribution_Detection_CVPR_2025_paper.html

abstract:
Out-of-distribution (OOD) detection is crucial for ensuring the robustness of machine learning models by identifying samples that deviate from the training distribution. While traditional OOD detection has predominantly focused on single-modality inputs, such as images, recent advancements in multimodal models have shown the potential of utilizing multiple modalities (e.g., video, optical flow, audio) to improve detection performance. However, existing approaches often neglect intra-class variability within in-distribution (ID) data, assuming that samples of the same class are perfectly cohesive and consistent. This assumption can lead to performance degradation, especially when prediction discrepancies are indiscriminately amplified across all samples. To address this issue, we propose Dynamic Prototype Updating (DPU), a novel plug-and-play framework for multimodal OOD detection that accounts for intra-class variations. Our method dynamically updates class center representations for each class by measuring the variance of similar samples within each batch, enabling tailored adjustments. This approach allows us to intensify prediction discrepancies based on the updated class centers, thereby enhancing the model's robustness and generalization across different modalities. Extensive experiments on two tasks, five datasets, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performances, setting a new state-of-the-art in multimodal OOD detection, including improvements up to 80% in Far-OOD detection.To improve accessibility and reproducibility, our code is released anonymously at https://anonymous.4open.science/r/CVPR-9177.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DPU_Dynamic_Prototype_Updating_for_Multimodal_Out-of-Distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DPU_Dynamic_Prototype_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shawn and Gong, Huixian and Dong, Hao and Yang, Tiankai and Tu, Zhengzhong and Zhao, Yue},
    title     = {DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10193-10202}
}

raw_affiliation:
[{"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]


title: Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
authors: Rohith Peddi, Saurabh Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8648-8657
affiliations: ["UT Dallas", "IIT Delhi", "IIT Delhi", "IIT Delhi", "UT Dallas"]
cities: ["Dallas", "Delhi", "Delhi", "Delhi", "Dallas"]
countries: ["USA", "India", "India", "India", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peddi_Towards_Unbiased_and_Robust_Spatio-Temporal_Scene_Graph_Generation_and_Anticipation_CVPR_2025_paper.html

abstract:
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modeling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation (VidSGG) and Scene Graph Anticipation (SGA) to produce biased scene graphs. To this end, we propose ImparTail, a novel training framework that leverages loss masking and curriculum learning to mitigate bias in the generation and anticipation of spatio-temporal scene graphs. Unlike prior methods that add extra architectural components to learn unbiased estimators, we propose an impartial training objective that reduces the dominance of head classes during learning and focuses on underrepresented tail relationships. Our curriculum-driven mask generation strategy further empowers the model to adaptively adjust its bias mitigation strategy over time, enabling more balanced and robust estimations. To thoroughly assess performance under various distribution shifts, we also introduce two new tasks--Robust Spatio-Temporal Scene Graph Generation and Robust Scene Graph Anticipation--offering a challenging benchmark for evaluating the resilience of STSG models. Extensive experiments on the Action Genome dataset demonstrate the superior unbiased performance and robustness of our method compared to existing baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peddi_Towards_Unbiased_and_Robust_Spatio-Temporal_Scene_Graph_Generation_and_Anticipation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peddi_Towards_Unbiased_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peddi_2025_CVPR,
    author    = {Peddi, Rohith and Saurabh, Saurabh and Shrivastava, Ayush Abhay and Singla, Parag and Gogate, Vibhav},
    title     = {Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8648-8657}
}

raw_affiliation:
[{"affiliation": "UT Dallas", "city": "Dallas", "country": "USA"}, {"affiliation": "IIT Delhi", "city": "Delhi", "country": "India"}, {"affiliation": "IIT Delhi", "city": "Delhi", "country": "India"}, {"affiliation": "IIT Delhi", "city": "Delhi", "country": "India"}, {"affiliation": "UT Dallas", "city": "Dallas", "country": "USA"}]


authors: Woojung Han, Yeonkyung Lee, Chanyoung Kim, Kwanghyun Park, Seong Jae Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18401-18410
raw_affiliation: [{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Yonsei University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Spatial_Transport_Optimization_by_Repositioning_Attention_Map_for_Training-Free_Text-to-Image_CVPR_2025_paper.html

title:
Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis

abstract:
Diffusion-based text-to-image (T2I) models have recently excelled in high-quality image generation, particularly in a training-free manner, enabling cost-effective adaptability and generalization across diverse tasks. However, while the existing methods have been continuously focusing on several challenges such as "missing objects" and "mismatched attributes," another critical issue of "mislocated objects" remains where generated spatial positions fail to align with text prompts. Surprisingly, ensuring such seemingly basic functionality remains challenging in popular T2I models due to the inherent difficulty of imposing explicit spatial guidance via text forms. To address this, we propose STORM (Spatial Transport Optimization by Repositioning Attention Map), a novel training-free approach for spatially coherent T2I synthesis. STORM employs Spatial Transport Optimization (STO), rooted in optimal transport theory, to dynamically adjust object attention maps for precise spatial adherence, supported by a Spatial Transport (ST) Cost function that enhances spatial understanding. Our analysis shows that integrating spatial awareness is most effective in the early denoising stages, while later phases refine details. Extensive experiments demonstrate that STORM surpasses existing methods, effectively mitigating mislocated objects while improving missing and mismatched attributes, setting a new benchmark for spatial alignment in T2I synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Spatial_Transport_Optimization_by_Repositioning_Attention_Map_for_Training-Free_Text-to-Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Spatial_Transport_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Woojung and Lee, Yeonkyung and Kim, Chanyoung and Park, Kwanghyun and Hwang, Seong Jae},
    title     = {Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18401-18410}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25250-25260
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bouniot_From_Alexnet_to_Transformers_Measuring_the_Non-linearity_of_Deep_Neural_CVPR_2025_paper.html

title:
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport

authors:
Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski

abstract:
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -- common factors associated with their expressive power -- may exhibit a drastically different performance even when trained on the same dataset. In this paper, we introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for approximately measuring the non-linearity of deep neural networks. Built upon a score derived from closed-form optimal transport mappings, this signature provides a better understanding of the inner workings of a wide range of DNN architectures and learning paradigms, with a particular emphasis on the computer vision task. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature and its potential for long-reaching implications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bouniot_From_Alexnet_to_Transformers_Measuring_the_Non-linearity_of_Deep_Neural_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bouniot_From_Alexnet_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bouniot_2025_CVPR,
    author    = {Bouniot, Quentin and Redko, Ievgen and Mallasto, Anton and Laclau, Charlotte and Struckmeier, Oliver and Arndt, Karol and Heinonen, Markus and Kyrki, Ville and Kaski, Samuel},
    title     = {From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25250-25260}
}

raw_affiliation:
[{"affiliation": "LTCI, T\u00e9l\u00e9com Paris, Institut Polytechnique de Paris", "city": "Paris", "country": "France"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Helmholtz Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning (MCML)", "city": "Munich", "country": "Germany"}, {"affiliation": "Noah\u2019s Ark Lab", "city": "Paris", "country": "France"}, {"affiliation": "Readpeak", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Aalto University, Finland, Dept. of Electrical Engineering and Automation", "city": "Espoo", "country": "Finland"}, {"affiliation": "Nomagic", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Aalto University, Finland, Department of Computer Science", "city": "Espoo", "country": "Finland"}, {"affiliation": "University of Manchester, UK, Department of Computer Science", "city": "Manchester", "country": "UK"}]

affiliations:
["LTCI, T\u00e9l\u00e9com Paris, Institut Polytechnique de Paris", "Technical University of Munich", "Helmholtz Munich", "Munich Center for Machine Learning (MCML)", "Noah\u2019s Ark Lab", "Readpeak", "Aalto University, Finland, Dept. of Electrical Engineering and Automation", "Nomagic", "Aalto University, Finland, Department of Computer Science", "University of Manchester, UK, Department of Computer Science"]

cities:
["Paris", "Munich", "Munich", "Munich", "Paris", "Unknown", "Espoo", "Unknown", "Espoo", "Manchester"]

countries:
["France", "Germany", "Germany", "Germany", "France", "Unknown", "Finland", "Unknown", "Finland", "UK"]


