# papers

authors: Junho Kim, Hyunjun Kim, Hosu Lee, Yong Man Ro
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3352-3362
affiliations: ["Integrated Vision and Language Lab, KAIST"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_SALOVA_Segment-Augmented_Long_Video_Assistant_for_Targeted_Retrieval_and_Routing_CVPR_2025_paper.html

title:
SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis

abstract:
Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and reduced relevance in the model responses. With the exponential growth of video data across web platforms, understanding long-form video is crucial for advancing generalized intelligence. In this paper, we introduce SALOVA: Segment-Augmented LOng Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content through targeted retrieval process. We address two main challenges to achieve it: (i) We present the SceneWalk dataset, a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich descriptive context. (ii) We develop robust architectural designs integrating dynamic routing mechanism and spatio-temporal projector to efficiently retrieve and process relevant video segments based on user queries. Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries, thereby improving the contextual relevance of the generated responses. Through extensive experiments, SALOVA demonstrates enhanced capability in processing complex long-form videos, showing significant capability to maintain contextual integrity across extended sequences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_SALOVA_Segment-Augmented_Long_Video_Assistant_for_Targeted_Retrieval_and_Routing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_SALOVA_Segment-Augmented_Long_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Junho and Kim, Hyunjun and Lee, Hosu and Ro, Yong Man},
    title     = {SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3352-3362}
}

raw_affiliation:
[{"affiliation": "Integrated Vision and Language Lab, KAIST", "city": "Daejeon", "country": "South Korea"}]


authors: Wenlong Fang, Qiaofeng Wu, Jing Chen, Yun Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19597-19607
cities: ["Guangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Notes-guided_MLLM_Reasoning_Enhancing_MLLM_with_Knowledge_and_Visual_Notes_CVPR_2025_paper.html

title:
Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question Answering

abstract:
The knowledge-based visual question answering (KB-VQA) task involves using external knowledge about the image to assist reasoning. Building on the impressive performance of multimodal large language model (MLLM), recent methods have commenced leveraging MLLM as an implicit knowledge base for reasoning. However, the direct employment of MLLM with raw external knowledge might result in reasoning errors due to misdirected knowledge information.  Additionally, MLLM may lack fine-grained perception of visual features, which can result in hallucinations during reasoning. To address these challenges, we propose Notes-guided MLLM Reasoning (NoteMR), a novel framework that guides MLLM in better reasoning by utilizing knowledge notes and visual notes. Specifically, we initially obtain explicit knowledge from an external knowledge base. Then, this explicit knowledge, combined with images, is used to assist the MLLM in generating knowledge notes. These notes are designed to filter explicit knowledge and identify relevant internal implicit knowledge within the MLLM. We then identify highly correlated regions between the images and knowledge notes, retaining them as image notes to enhance the model's fine-grained perception, thereby mitigating MLLM induced hallucinations. Finally, both notes are fed into the MLLM, enabling a more comprehensive understanding of the image-question pair and enhancing the model's reasoning capabilities. Our method achieves state-of-the-art performance on the OK-VQA and A-OKVQA datasets, demonstrating its robustness and effectiveness across diverse VQA scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Notes-guided_MLLM_Reasoning_Enhancing_MLLM_with_Knowledge_and_Visual_Notes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Notes-guided_MLLM_Reasoning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Wenlong and Wu, Qiaofeng and Chen, Jing and Xue, Yun},
    title     = {Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19597-19607}
}

raw_affiliation:
[{"affiliation": "School of Electronic Science and Engineering (School of Microelectronics), South China Normal University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Electronic Science and Engineering (School of Microelectronics), South China Normal University"]


title: Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
authors: Jianyang Xie, Yitian Zhao, Yanda Meng, He Zhao, Anh Nguyen, Yalin Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24309-24319
cities: ["Liverpool", "Ningbo", "Exeter"]
countries: ["UK", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Are_Spatial-Temporal_Graph_Convolution_Networks_for_Human_Action_Recognition_Over-Parameterized_CVPR_2025_paper.html

abstract:
Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numerous models, their recognition performance does not differ significantly after aligning the input settings. With this observation, we hypothesize that ST-GCNs are over-parameterized for HAR, a conjecture subsequently confirmed through experiments employing the lottery ticket hypothesis. Additionally, a novel sparse ST-GCNs generator is proposed, which trains a sparse architecture from a randomly initialized dense network while maintaining comparable performance levels to the dense components. Moreover, we generate multi-level sparsity ST-GCNs by integrating sparse structures at various sparsity levels and demonstrate that the assembled model yields a significant enhancement in HAR performance. Thorough experiments on four datasets, including NTU-RGB+D 60(120), Kinetics-400, and FineGYM, demonstrate that the proposed sparse ST-GCNs can achieve comparable performance to their dense components. Even with 95% fewer parameters, the sparse ST-GCNs exhibit a degradation of <1% in top-1 accuracy. Meanwhile, the multi-level sparsity ST-GCNs, which require only 66% of the parameters of the dense ST-GCNs, demonstrate an improvement of >1%  in top-1 accuracy. The code is available at https://github.com/davelailai/Sparse-ST-GCN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Are_Spatial-Temporal_Graph_Convolution_Networks_for_Human_Action_Recognition_Over-Parameterized_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Are_Spatial-Temporal_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Jianyang and Zhao, Yitian and Meng, Yanda and Zhao, He and Nguyen, Anh and Zheng, Yalin},
    title     = {Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24309-24319}
}

raw_affiliation:
[{"affiliation": "University of Liverpool, UK", "city": "Liverpool", "country": "UK"}, {"affiliation": "Ningbo Institute of Materials Technology and Engineering, CAS, China", "city": "Ningbo", "country": "China"}, {"affiliation": "University of Exeter, UK", "city": "Exeter", "country": "UK"}]

affiliations:
["University of Liverpool, UK", "Ningbo Institute of Materials Technology and Engineering, CAS, China", "University of Exeter, UK"]


title: DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
authors: Li Ren, Chen Chen, Liqiang Wang, Kien Hua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4353-4363
affiliations: ["Department of Computer Science, University of Central Florida"]
cities: ["Orlando"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_DA-VPT_Semantic-Guided_Visual_Prompt_Tuning_for_Vision_Transformers_CVPR_2025_paper.html

abstract:
Visual Prompt Tuning (VPT) has become a promising solution for Parameter-Efficient Fine-Tuning (PEFT) approach for Vision Transformer (ViT) models by partially fine-tuning learnable tokens while keeping most model parameters frozen. Recent research has explored modifying the connection structures of the prompts. However, the fundamental correlation and distribution between the prompts and image tokens remain unexplored. In this paper, we leverage metric learning techniques to investigate how the distribution of prompts affects fine-tuning performance. Specifically, we propose a novel framework, Distribution Aware Visual Prompt Tuning (DA-VPT), to guide the distributions of the prompts by learning the distance metric from their class-related semantic data. Our method demonstrates that the prompts can serve as an effective bridge to share semantic information between image patches and the class token. We extensively evaluated our approach on popular benchmarks in both recognition and segmentation tasks. The results demonstrate that our approach enables more effective and efficient fine-tuning of ViT models by leveraging semantic information to guide the learning of the prompts, leading to improved performance on various downstream vision tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_DA-VPT_Semantic-Guided_Visual_Prompt_Tuning_for_Vision_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_DA-VPT_Semantic-Guided_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Li and Chen, Chen and Wang, Liqiang and Hua, Kien},
    title     = {DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4353-4363}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, University of Central Florida", "city": "Orlando", "country": "USA"}]


title: Towards Lossless Implicit Neural Representation via Bit Plane Decomposition
authors: Woo Kyoung Han, Byeonghun Lee, Hyunmin Cho, Sunghoon Im, Kyong Hwan Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2269-2278
affiliations: ["Korea University", "DGIST"]
cities: ["Seoul", "Daegu"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Towards_Lossless_Implicit_Neural_Representation_via_Bit_Plane_Decomposition_CVPR_2025_paper.html

abstract:
We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Towards_Lossless_Implicit_Neural_Representation_via_Bit_Plane_Decomposition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Towards_Lossless_Implicit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Woo Kyoung and Lee, Byeonghun and Cho, Hyunmin and Im, Sunghoon and Jin, Kyong Hwan},
    title     = {Towards Lossless Implicit Neural Representation via Bit Plane Decomposition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2269-2278}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "DGIST", "city": "Daegu", "country": "South Korea"}]


title: Spectral State Space Model for Rotation-Invariant Visual Representation Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23881-23890
cities: ["Montreal", "Montreal", "Unknown", "Montreal"]
countries: ["Canada", "Canada", "Unknown", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dastani_Spectral_State_Space_Model_for_Rotation-Invariant_Visual_Representation_Learning_CVPR_2025_paper.html

authors:
Sahar Dastani, Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim, Farzad Beizaee, Milad Cheraghalikhani, Arnab Kumar Mondal, Herve Lombaert, Christian Desrosiers

abstract:
State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to identify relationships between conceptually related yet not adjacent patches. This limitation arises from the non-causal nature of image data, which lacks inherent directional relationships. Additionally, current vision-based SSMs are highly sensitive to transformations such as rotation. Their predefined scanning directions depend on the original image orientation, which can cause the model to produce inconsistent patch-processing sequences after rotation.To address these limitations, we introduce Spectral VMamba, a novel approach that effectively captures the global structure within an image by leveraging spectral information derived from the graph Laplacian of image patches. Through spectral decomposition, our approach encodes patch relationships independently of image orientation, achieving rotation invariance with the aid of our Rotational Feature Normalizer (RFN) module. Our experiments on classification tasks show that Spectral VMamba outperforms the leading SSM models in vision, such as VMamba, while maintaining invariance to rotations and a providing a similar runtime efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dastani_Spectral_State_Space_Model_for_Rotation-Invariant_Visual_Representation_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dastani_Spectral_State_Space_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dastani_2025_CVPR,
    author    = {Dastani, Sahar and Bahri, Ali and Yazdanpanah, Moslem and Noori, Mehrdad and Osowiechi, David and Hakim, Gustavo Adolfo Vargas and Beizaee, Farzad and Cheraghalikhani, Milad and Mondal, Arnab Kumar and Lombaert, Herve and Desrosiers, Christian},
    title     = {Spectral State Space Model for Rotation-Invariant Visual Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23881-23890}
}

raw_affiliation:
[{"affiliation": "LIVIA, ILLS, \u00c9TS Montr\u00e9al", "city": "Montreal", "country": "Canada"}, {"affiliation": "Mila - Quebec AI Institute", "city": "Montreal", "country": "Canada"}, {"affiliation": "Apple", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Polytechnique Montreal", "city": "Montreal", "country": "Canada"}]

affiliations:
["LIVIA, ILLS, \u00c9TS Montr\u00e9al", "Mila - Quebec AI Institute", "Apple", "Polytechnique Montreal"]


title: iSegMan: Interactive Segment-and-Manipulate 3D Gaussians
authors: Yian Zhao, Wanshi Xu, Ruochong Zheng, Pengchong Qiao, Chang Liu, Jie Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 661-670
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_iSegMan_Interactive_Segment-and-Manipulate_3D_Gaussians_CVPR_2025_paper.html

abstract:
The efficient rendering and explicit nature of 3DGS promote the advancement of 3D scene manipulation.However, existing methods typically encounter challenges in controlling the manipulation region and are unable to furnish the user with interactive feedback, which inevitably leads to unexpected results.Intuitively, incorporating interactive 3D segmentation tools can compensate for this deficiency. Nevertheless, existing segmentation frameworks impose a pre-processing step of scene-specific parameter training, which limits the efficiency and flexibility of scene manipulation.To deliver a 3D region control module that is well-suited for scene manipulation with reliable efficiency, we propose **i**nteractive **Seg**ment-and-**Man**ipulate 3D Gaussians (**iSegMan**), an interactive segmentation and manipulation framework that only requires simple 2D user interactions in any view.To propagate user interactions to other views, we propose Epipolar-guided Interaction Propagation (**EIP**), which innovatively exploits epipolar constraint for efficient and robust interaction matching.To avoid scene-specific training to maintain efficiency, we further propose the novel Visibility-based Gaussian Voting (**VGV**), which obtains 2D segmentations from SAM and models the region extraction as a voting game between 2D Pixels and 3D Gaussians based on Gaussian visibility.Taking advantage of the efficient and precise region control of EIP and VGV, we put forth a **Manipulation Toolbox** to implement various functions on selected regions, enhancing the controllability, flexibility and practicality of scene manipulation.Extensive results on 3D scene manipulation and segmentation tasks fully demonstrate the significant advantages of iSegMan.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_iSegMan_Interactive_Segment-and-Manipulate_3D_Gaussians_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_iSegMan_Interactive_Segment-and-Manipulate_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Yian and Xu, Wanshi and Zheng, Ruochong and Qiao, Pengchong and Liu, Chang and Chen, Jie},
    title     = {iSegMan: Interactive Segment-and-Manipulate 3D Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {661-670}
}

raw_affiliation:
[{"affiliation": "School of Electronic and Computer Engineering, Peking University, Shenzhen, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory, Shenzhen, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Department of Automation and BNRist, Tsinghua University, Beijing, China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Electronic and Computer Engineering, Peking University, Shenzhen, China", "Pengcheng Laboratory, Shenzhen, China", "AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China", "Department of Automation and BNRist, Tsinghua University, Beijing, China"]


title: BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4145-4155
affiliations: ["vivo AI Lab", "CUHK MMLab"]
cities: ["Shenzhen", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_BlueLM-V-3B_Algorithm_and_System_Co-Design_for_Multimodal_Large_Language_Models_CVPR_2025_paper.html

authors:
Xudong Lu, Yinghao Chen, Cheng Chen, Hui Tan, Boheng Chen, Yina Xie, Rui Hu, Guanxin Tan, Renshou Wu, Yan Hu, Yi Zeng, Lei Wu, Liuyang Bian, Zhaoxiong Wang, Long Liu, Yanzhou Yang, Han Xiao, Aojun Zhou, Yafei Wen, Xiaoxin Chen, Shuai Ren, Hongsheng Li

abstract:
The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essential daily companions, represent the most effective and accessible deployment platform for MLLMs, enabling seamless integration into everyday tasks. However, deploying MLLMs on mobile phones presents challenges due to limitations in memory size and computational capability, making it difficult to achieve smooth and real-time processing without extensive optimization. In this paper, we present BlueLM-V-3B, an algorithm and system co-design approach specifically tailored for the efficient deployment of MLLMs on mobile platforms. To be specific, we redesign the dynamic resolution scheme adopted by mainstream MLLMs and implement system optimization for hardware-aware deployment to optimize model inference on mobile phones. BlueLM-V-3B boasts the following key highlights: (1) Small Size: BlueLM-V-3B features a language model with 2.7B parameters and a vision encoder with 400M parameters. (2) Fast Speed: BlueLM-V-3B achieves a generation speed of 24.4 token/s on the MediaTek Dimensity 9300 processor with 4-bit LLM weight quantization. (3) Strong Performance: BlueLM-V-3B  has attained the highest average score of 66.1 on the OpenCompass benchmark among models with <= 4B parameters and surpassed a series of models with much larger parameter sizes (e.g., MiniCPM-V-2.6, InternVL2-8B).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_BlueLM-V-3B_Algorithm_and_System_Co-Design_for_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_BlueLM-V-3B_Algorithm_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Xudong and Chen, Yinghao and Chen, Cheng and Tan, Hui and Chen, Boheng and Xie, Yina and Hu, Rui and Tan, Guanxin and Wu, Renshou and Hu, Yan and Zeng, Yi and Wu, Lei and Bian, Liuyang and Wang, Zhaoxiong and Liu, Long and Yang, Yanzhou and Xiao, Han and Zhou, Aojun and Wen, Yafei and Chen, Xiaoxin and Ren, Shuai and Li, Hongsheng},
    title     = {BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4145-4155}
}

raw_affiliation:
[{"affiliation": "vivo AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "CUHK MMLab", "city": "Hong Kong", "country": "China"}]


title: Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
authors: Peirong Liu, Ana Lawry Aguila, Juan E. Iglesias
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10455-10465
affiliations: ["Harvard Medical School and Massachusetts General Hospital", "UCL", "MIT"]
cities: ["Boston", "London", "Cambridge"]
countries: ["USA", "UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Unraveling_Normal_Anatomy_via_Fluid-Driven_Anomaly_Randomization_CVPR_2025_paper.html

abstract:
Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular resolution (often isotropic). This limits their generalizability in clinical settings, where variations in scan appearance arise from differences in sequence parameters, resolution, and orientation. Furthermore, most general-purpose models are designed for healthy subjects and suffer from performance degradation when pathology is present. We introduce UNA (Unraveling Normal Anatomy), the first modality-agnostic learning approach for normal brain anatomy reconstruction that can handle both healthy scans and cases with pathology. We propose a fluid-driven anomaly randomization method that generates an unlimited number of realistic pathology profiles on-the-fly. UNA is trained on a combination of synthetic and real data, and can be applied directly to real images with potential pathology without the need for fine-tuning. We demonstrate UNA's effectiveness in reconstructing healthy brain anatomy and showcase its direct application to anomaly detection, using both simulated and real images from 3D healthy and stroke datasets, including CT and MRI scans. By bridging the gap between healthy and diseased images, UNA enables the use of general-purpose models on diseased images, opening up new opportunities for large-scale analysis of uncurated clinical images in the presence of pathology.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Unraveling_Normal_Anatomy_via_Fluid-Driven_Anomaly_Randomization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Unraveling_Normal_Anatomy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Peirong and Aguila, Ana Lawry and Iglesias, Juan E.},
    title     = {Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10455-10465}
}

raw_affiliation:
[{"affiliation": "Harvard Medical School and Massachusetts General Hospital", "city": "Boston", "country": "USA"}, {"affiliation": "UCL", "city": "London", "country": "UK"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}]


title: Taming Teacher Forcing for Masked Autoregressive Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7374-7384
affiliations: ["HKUST(GZ)", "StepFun", "UIUC", "THU"]
cities: ["Guangzhou", "Beijing", "Champaign", "Beijing"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Taming_Teacher_Forcing_for_Masked_Autoregressive_Video_Generation_CVPR_2025_paper.html

authors:
Deyu Zhou, Quan Sun, Yuang Peng, Kun Yan, Runpei Dong, Duomin Wang, Zheng Ge, Nan Duan, Xiangyu Zhang

abstract:
We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forcing, MTF), enabling a smooth transition from token-level (patch-level) to frame-level autoregressive generation. CTF significantly outperforms MTF, achieving a 23% improvement in FVD scores on first-frame conditioned video prediction. To address issues like exposure bias, we employ targeted training strategies, setting a new benchmark in autoregressive video generation. Experiments show that MAGI can generate long, coherent video sequences exceeding 100 frames, even when trained on as few as 16 frames, highlighting its potential for scalable, high-quality video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Taming_Teacher_Forcing_for_Masked_Autoregressive_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Taming_Teacher_Forcing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Deyu and Sun, Quan and Peng, Yuang and Yan, Kun and Dong, Runpei and Wang, Duomin and Ge, Zheng and Duan, Nan and Zhang, Xiangyu},
    title     = {Taming Teacher Forcing for Masked Autoregressive Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7374-7384}
}

raw_affiliation:
[{"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "StepFun", "city": "Beijing", "country": "China"}, {"affiliation": "UIUC", "city": "Champaign", "country": "USA"}, {"affiliation": "THU", "city": "Beijing", "country": "China"}]


title: UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior
authors: I-Hsiang Chen, Wei-Ting Chen, Yu-Wei Liu, Yuan-Chun Chiang, Sy-Yen Kuo, Ming-Hsuan Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17969-17979
cities: ["Taipei", "Redmond", "Taoyuan City", "Merced", "Mountain View"]
countries: ["Taiwan", "USA", "Taiwan", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_UniRestore_Unified_Perceptual_and_Task-Oriented_Image_Restoration_Model_Using_Diffusion_CVPR_2025_paper.html

abstract:
Image restoration aims to recover content from inputs degraded by various factors, such as adverse weather, blur, and noise. Perceptual Image Restoration (PIR) methods improve visual quality but often do not support downstream tasks effectively. On the other hand, Task-oriented Image Restoration (TIR) methods focus on enhancing image utility for high-level vision tasks, sometimes compromising visual quality. This paper introduces UniRestore, a unified image restoration model that bridges the gap between PIR and TIR by using a diffusion prior. The diffusion prior is designed to generate images that align with human visual quality preferences, but these images are often unsuitable for TIR scenarios. To solve this limitation, UniRestore utilizes encoder features from an autoencoder to adapt the diffusion prior to specific tasks. We propose a Complementary Feature Restoration Module (CFRM) to reconstruct degraded encoder features and a Task Feature Adapter (TFA) module to facilitate adaptive feature fusion in the decoder. This design allows UniRestore to optimize images for both human perception and downstream task requirements, addressing discrepancies between visual quality and functional needs. Integrating these modules also enhances UniRestore's adapability and efficiency across diverse tasks. Extensive expertments demonstrate the superior performance of UniRestore in both PIR and TIR scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_UniRestore_Unified_Perceptual_and_Task-Oriented_Image_Restoration_Model_Using_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_UniRestore_Unified_Perceptual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, I-Hsiang and Chen, Wei-Ting and Liu, Yu-Wei and Chiang, Yuan-Chun and Kuo, Sy-Yen and Yang, Ming-Hsuan},
    title     = {UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17969-17979}
}

raw_affiliation:
[{"affiliation": "National T aiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Chang Gung University", "city": "Taoyuan City", "country": "Taiwan"}, {"affiliation": "UC Merced", "city": "Merced", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}]

affiliations:
["National T aiwan University", "Microsoft", "Chang Gung University", "UC Merced", "Google Research"]


title: Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation
authors: Yiftach Edelstein, Or Patashnik, Dana Cohen-Bar, Lihi Zelnik-Manor
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21458-21468
affiliations: ["Technion - Israel Institute of Technology", "Tel Aviv University"]
cities: ["Haifa", "Tel Aviv"]
countries: ["Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Edelstein_Sharp-It_A_Multi-view_to_Multi-view_Diffusion_Model_for_3D_Synthesis_CVPR_2025_paper.html

abstract:
Advancements in text-to-image diffusion models have led to significant progress in fast 3D content creation. One common approach is to generate a set of multi-view images of an object, and then reconstruct it into a 3D model. However, this approach bypasses the use of a native 3D representation of the object and is hence prone to geometric artifacts and limited in controllability and manipulation capabilities. An alternative approach involves native 3D generative models that directly produce 3D representations. These models, however, are typically limited in their resolution, resulting in lower quality 3D objects. In this work, we bridge the quality gap between methods that directly generate 3D representations and ones that reconstruct 3D objects from multi-view images. We introduce a multi-view to multi-view diffusion model called Sharp-It, which takes a 3D consistent set of multi-view images rendered from a low-quality object and enriches its geometric details and texture. The diffusion model operates on the multi-view set in parallel, in the sense that it shares features across the generated views. A high-quality 3D model can then be reconstructed from the enriched multi-view set. By leveraging the advantages of both 2D and 3D approaches, our method offers an efficient and controllable method for high-quality 3D content creation. We demonstrate that Sharp-It enables various 3D applications, such as fast synthesis, editing, and controlled generation, while attaining high-quality assets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Edelstein_Sharp-It_A_Multi-view_to_Multi-view_Diffusion_Model_for_3D_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Edelstein_Sharp-It_A_Multi-view_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Edelstein_2025_CVPR,
    author    = {Edelstein, Yiftach and Patashnik, Or and Cohen-Bar, Dana and Zelnik-Manor, Lihi},
    title     = {Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21458-21468}
}

raw_affiliation:
[{"affiliation": "Technion - Israel Institute of Technology", "city": "Haifa", "country": "Israel"}, {"affiliation": "Tel Aviv University", "city": "Tel Aviv", "country": "Israel"}]


title: URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
authors: Rui Xu, Yuzhen Niu, Yuezhou Li, Huangbiao Xu, Wenxi Liu, Yuzhong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21267-21276
cities: ["Fuzhou", "Fuzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_URWKV_Unified_RWKV_Model_with_Multi-state_Perspective_for_Low-light_Image_CVPR_2025_paper.html

abstract:
Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrained by  dynamically coupled degradations. To address these challenges, we introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective, enabling flexible and effective degradation restoration for low-light images. Specifically, we customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states. First, inspired by the pupil mechanism in the human visual system, we propose Luminance-adaptive Normalization (LAN) that adjusts normalization parameters based on rich inter-stage states, allowing for adaptive, scene-aware luminance modulation. Second, we aggregate multiple intra-stage states through exponential moving average approach, effectively capturing subtle variations while mitigating information loss inherent in the single-state mechanism. To reduce the degradation effects commonly associated with conventional skip connections, we propose the State-aware Selective Fusion (SSF) module, which dynamically aligns and integrates multi-state features across encoder stages, selectively fusing contextual information. In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks,  while requiring significantly fewer parameters and computational resources. Code is available at: https://github.com/FZU-N/URWKV.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_URWKV_Unified_RWKV_Model_with_Multi-state_Perspective_for_Low-light_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_URWKV_Unified_RWKV_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Rui and Niu, Yuzhen and Li, Yuezhou and Xu, Huangbiao and Liu, Wenxi and Chen, Yuzhong},
    title     = {URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21267-21276}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University", "city": "Fuzhou", "country": "China"}, {"affiliation": "Engineering Research Center of Big Data Intelligence, Ministry of Education", "city": "Fuzhou", "country": "China"}]

affiliations:
["Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University", "Engineering Research Center of Big Data Intelligence, Ministry of Education"]


title: Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift
authors: Siyuan Liang, Jiawei Liang, Tianyu Pang, Chao Du, Aishan Liu, Mingli Zhu, Xiaochun Cao, Dacheng Tao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9477-9486
cities: ["Singapore", "Shenzhen", "Singapore", "Unknown", "Shenzhen"]
countries: ["Singapore", "China", "Singapore", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Revisiting_Backdoor_Attacks_against_Large_Vision-Language_Models_from_Domain_Shift_CVPR_2025_paper.html

abstract:
Instruction tuning enhances large vision-language models (LVLMs) but increases their vulnerability to backdoor attacks due to their open design. Unlike prior studies in static settings, this paper explores backdoor attacks in LVLM instruction tuning across mismatched training and testing domains. We introduce a new evaluation dimension, backdoor domain generalization, to assess attack robustness under visual and text domain shifts. Our findings reveal two insights: (1) backdoor generalizability improves when distinctive trigger patterns are independent of specific data domains or model architectures, and (2) the competitive interaction between trigger patterns and clean semantic regions, where guiding the model to predict triggers enhances attack generalizability. Based on these insights, we propose a multimodal attribution backdoor attack (MABA) that injects domain-agnostic triggers into critical areas using attributional interpretation. Experiments with OpenFlamingo, Blip-2, and Otter show that MABA significantly boosts the attack success rate of generalization by 36.4% over the unimodal attack, achieving a 97% success rate at a 0.2% poisoning rate. This study reveals limitations in current evaluations and highlights how enhanced backdoor generalizability poses a security threat to LVLMs, even without test data access.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Revisiting_Backdoor_Attacks_against_Large_Vision-Language_Models_from_Domain_Shift_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Revisiting_Backdoor_Attacks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Siyuan and Liang, Jiawei and Pang, Tianyu and Du, Chao and Liu, Aishan and Zhu, Mingli and Cao, Xiaochun and Tao, Dacheng},
    title     = {Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9477-9486}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shenzhen Campus of Sun Y at-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Sea AI Lab", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["Nanyang Technological University", "Shenzhen Campus of Sun Y at-sen University", "Sea AI Lab", "Independent Researcher", "The Chinese University of Hong Kong, Shenzhen"]


title: Condensing Action Segmentation Datasets via Generative Network Inversion
authors: Guodong Ding, Rongyu Chen, Angela Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17733-17742
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ding_Condensing_Action_Segmentation_Datasets_via_Generative_Network_Inversion_CVPR_2025_paper.html

abstract:
This work presents the first condensation approach for procedural video datasets used in temporal action segmentation. We propose a condensation framework that leverages generative prior learned from the dataset and network inversion to condense data into compact latent codes with significant storage reduced across temporal and channel aspects. Orthogonally, we propose sampling diverse and representative action sequences to minimize video-wise redundancy. Our evaluation on standard benchmarks demonstrates consistent effectiveness in condensing TAS datasets and achieving competitive performances. Specifically, on the Breakfast dataset, our approach reduces storage by over 500xwhile retaining 83% of the performance compared to training with the full dataset. Furthermore, when applied to a downstream incremental learning task, it yields superior performance compared to the state-of-the-art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_Condensing_Action_Segmentation_Datasets_via_Generative_Network_Inversion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_Condensing_Action_Segmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ding_2025_CVPR,
    author    = {Ding, Guodong and Chen, Rongyu and Yao, Angela},
    title     = {Condensing Action Segmentation Datasets via Generative Network Inversion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17733-17742}
}


title: TCFG: Tangential Damping Classifier-free Guidance
authors: Mingi Kwon, Shin seong Kim, Jaeseok Jeong, Yi Ting Hsiao, Youngjung Uh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2620-2629
cities: ["Seoul", "Seoul", "Seoul", "Ann Arbor", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kwon_TCFG_Tangential_Damping_Classifier-free_Guidance_CVPR_2025_paper.html

abstract:
Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition-aligned image generation. CFG combines the conditional score (e.g., text-conditioned) with the unconditional score to control the output. However, the unconditional score is in charge of estimating the transition between manifolds of adjacent timesteps from  x_t  to  x_ t-1 , which may inadvertently interfere with the trajectory toward the specific condition. In this work, we introduce a novel approach that leverages a geometric perspective on the unconditional score to enhance CFG performance when conditional scores are available. Specifically, we propose a method that filters the singular vectors of both conditional and unconditional scores using singular value decomposition. This filtering process aligns the unconditional score with the conditional score, thereby refining the sampling trajectory to stay closer to the manifold. Our approach improves image quality with negligible additional computation. We provide deeper insights into the score function behavior in diffusion models and present a practical technique for achieving more accurate and contextually coherent image synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kwon_TCFG_Tangential_Damping_Classifier-free_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kwon_TCFG_Tangential_Damping_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kwon_2025_CVPR,
    author    = {Kwon, Mingi and Kim, Shin seong and Jeong, Jaeseok and Hsiao, Yi Ting and Uh, Youngjung},
    title     = {TCFG: Tangential Damping Classifier-free Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2620-2629}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Yonsei University", "Yonsei University", "Yonsei University", "University of Michigan", "Yonsei University"]


title: MatAnyone: Stable Video Matting with Consistent Memory Propagation
authors: Peiqing Yang, Shangchen Zhou, Jixin Zhao, Qingyi Tao, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7299-7308
affiliations: ["S-Lab, Nanyang Technological University", "SenseTime Research"]
cities: ["Singapore", "Singapore"]
countries: ["Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_MatAnyone_Stable_Video_Matting_with_Consistent_Memory_Propagation_CVPR_2025_paper.html

abstract:
Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To tackle this, we propose MatAnyone, a practical framework designed for target-assigned video matting. Specifically, building on a memory-based framework, we introduce a consistent memory propagation module via region-adaptive memory fusion, which adaptively combines memory from the previous frame. This ensures stable semantic consistency in core regions while maintaining fine details along object boundaries. For robust training, we present a larger, high-quality, and diverse dataset for video matting. Additionally, we incorporate a novel training strategy that efficiently leverages large-scale segmentation data, further improving matting stability. With this new network design, dataset, and training strategy, MatAnyone delivers robust, accurate video matting in diverse real-world scenarios, outperforming existing methods. The code and model will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_MatAnyone_Stable_Video_Matting_with_Consistent_Memory_Propagation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_MatAnyone_Stable_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Peiqing and Zhou, Shangchen and Zhao, Jixin and Tao, Qingyi and Loy, Chen Change},
    title     = {MatAnyone: Stable Video Matting with Consistent Memory Propagation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7299-7308}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SenseTime Research", "city": "Singapore", "country": "Singapore"}]


title: Can Generative Video Models Help Pose Estimation?
authors: Ruojin Cai, Jason Y. Zhang, Philipp Henzler, Zhengqi Li, Noah Snavely, Ricardo Martin-Brualla
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16764-16773
affiliations: ["Google", "Cornell University"]
cities: ["Mountain View", "Ithaca"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_Can_Generative_Video_Models_Help_Pose_Estimation_CVPR_2025_paper.html

abstract:
Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in these scenarios due to the lack of identifiable correspondences or visual overlap.  Inspired by the human ability to infer spatial relationships from diverse scenes, we propose a novel approach, InterPose, that leverages the rich priors encoded within pre-trained generative video models. We propose to use a video model to hallucinate intermediate frames between two input images, effectively creating a dense, visual transition, which significantly simplifies the problem of pose estimation. Since current video models can still produce implausible motion or inconsistent geometry, we introduce a self-consistency score that evaluates the consistency of pose predictions from sampled videos. We demonstrate that our approach generalizes among three state-of-the-art video models and show consistent improvements over the state-of-the-art DUSt3R baseline on four diverse datasets encompassing indoor, outdoor, and object-centric scenes. Our findings suggest a promising avenue for improving pose estimation models by leveraging large generative models trained on vast amounts of video data, which is more readily available than 3D data. See our project page for results: Inter-Pose.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Can_Generative_Video_Models_Help_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Can_Generative_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Ruojin and Zhang, Jason Y. and Henzler, Philipp and Li, Zhengqi and Snavely, Noah and Martin-Brualla, Ricardo},
    title     = {Can Generative Video Models Help Pose Estimation?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16764-16773}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]


title: Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 91-104
affiliations: ["Allen Institute for AI", "University of Washington", "University of Pennsylvania"]
cities: ["Seattle", "Seattle", "Philadelphia"]
countries: ["USA", "USA", "USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deitke_Molmo_and_PixMo_Open_Weights_and_Open_Data_for_State-of-the-Art_CVPR_2025_paper.html

authors:
Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, Jiasen Lu, Taira Anderson, Erin Bransom, Kiana Ehsani, Huong Ngo, YenSung Chen, Ajay Patel, Mark Yatskar, Chris Callison-Burch, Andrew Head, Rose Hendrix, Favyen Bastani, Eli VanderBilt, Nathan Lambert, Yvonne Chou, Arnavi Chheda, Jenna Sparks, Sam Skjonsberg, Michael Schmitz, Aaron Sarnat, Byron Bischoff, Pete Walsh, Chris Newell, Piper Wolters, Tanmay Gupta, Kuo-Hao Zeng, Jon Borchardt, Dirk Groeneveld, Crystal Nam, Sophie Lebrecht, Caitlin Wittlif, Carissa Schoenick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, Aniruddha Kembhavi

abstract:
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a result, the community has been missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key contribution is a collection of new datasets called PixMo, including a dataset of highly detailed image captions for pre-training, a free-form image Q&A dataset for fine-tuning, and an innovative 2D pointing dataset, all collected without the use of external VLMs. The success of our approach relies on careful modeling choices, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets. Our best-in-class 72B model not only outperforms others in the class of open weight and data models, but also outperforms larger proprietary models including Claude 3.5 Sonnet, and Gemini 1.5 Pro and Flash, second only to GPT-4o based on both academic benchmarks and on a large human evaluation. Our model weights, new datasets, and source code are available at https://molmo.allenai.org/blog.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deitke_Molmo_and_PixMo_Open_Weights_and_Open_Data_for_State-of-the-Art_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deitke_Molmo_and_PixMo_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deitke_2025_CVPR,
    author    = {Deitke, Matt and Clark, Christopher and Lee, Sangho and Tripathi, Rohun and Yang, Yue and Park, Jae Sung and Salehi, Mohammadreza and Muennighoff, Niklas and Lo, Kyle and Soldaini, Luca and Lu, Jiasen and Anderson, Taira and Bransom, Erin and Ehsani, Kiana and Ngo, Huong and Chen, YenSung and Patel, Ajay and Yatskar, Mark and Callison-Burch, Chris and Head, Andrew and Hendrix, Rose and Bastani, Favyen and VanderBilt, Eli and Lambert, Nathan and Chou, Yvonne and Chheda, Arnavi and Sparks, Jenna and Skjonsberg, Sam and Schmitz, Michael and Sarnat, Aaron and Bischoff, Byron and Walsh, Pete and Newell, Chris and Wolters, Piper and Gupta, Tanmay and Zeng, Kuo-Hao and Borchardt, Jon and Groeneveld, Dirk and Nam, Crystal and Lebrecht, Sophie and Wittlif, Caitlin and Schoenick, Carissa and Michel, Oscar and Krishna, Ranjay and Weihs, Luca and Smith, Noah A. and Hajishirzi, Hannaneh and Girshick, Ross and Farhadi, Ali and Kembhavi, Aniruddha},
    title     = {Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {91-104}
}

raw_affiliation:
[{"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17261-17270
affiliations: ["The University of Hong Kong", "Tsinghua University", "Meituan"]
cities: ["Hong Kong", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_DriveGPT4-V2_Harnessing_Large_Language_Model_Capabilities_for_Enhanced_Closed-Loop_Autonomous_CVPR_2025_paper.html

title:
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving

authors:
Zhenhua Xu, Yan Bai, Yujia Zhang, Zhuoling Li, Fei Xia, Kwan-Yee K. Wong, Jianqiang Wang, Hengshuang Zhao

abstract:
Multimodal large language models (MLLMs) possess the ability to comprehend visual images or videos, and show impressive reasoning ability thanks to the vast amounts of pretrained knowledge, making them highly suitable for autonomous driving applications. Unlike the previous work, DriveGPT4-V1, which focused on open-loop tasks, this study explores the capabilities of LLMs in enhancing closed-loop autonomous driving. DriveGPT4-V2 processes camera images and vehicle states as input to generate low-level control signals for end-to-end vehicle operation. A multi-view visual tokenizer (MV-VT) is employed enabling DriveGPT4-V2 to perceive the environment with an extensive range while maintaining critical details. The model architecture has been refined to improve decision prediction and inference speed. To further enhance the performance, an additional expert LLM is trained for online imitation learning. The expert LLM, sharing a similar structure with DriveGPT4-V2, can access privileged information about surrounding objects for more robust and reliable predictions. Experimental results show that DriveGPT4-V2 outperforms all baselines on the challenging CARLA Longest6 benchmark. The code and data of DriveGPT4-V2 will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_DriveGPT4-V2_Harnessing_Large_Language_Model_Capabilities_for_Enhanced_Closed-Loop_Autonomous_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zhenhua and Bai, Yan and Zhang, Yujia and Li, Zhuoling and Xia, Fei and Wong, Kwan-Yee K. and Wang, Jianqiang and Zhao, Hengshuang},
    title     = {DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17261-17270}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Meituan", "city": "Beijing", "country": "China"}]


title: High-Fidelity Lightweight Mesh Reconstruction from Point Clouds
authors: Chen Zhang, Wentao Wang, Ximeng Li, Xinyao Liao, Wanjuan Su, Wenbing Tao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11739-11748
cities: ["Wuhan"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_High-Fidelity_Lightweight_Mesh_Reconstruction_from_Point_Clouds_CVPR_2025_paper.html

abstract:
Recently, learning signed distance functions (SDFs) from point clouds has become popular for reconstruction. To ensure accuracy, most methods require using high-resolution Marching Cubes for surface extraction. However, this results in redundant mesh elements, making the mesh inconvenient to use. To solve the problem, we propose an adaptive meshing method to extract resolution-adaptive meshes based on surface curvature, enabling the recovery of high-fidelity lightweight meshes. Specifically, we first use point-based representation to perceive implicit surfaces and calculate surface curvature. A vertex generator is designed to produce curvature-adaptive vertices with any specified number on the implicit surface, preserving the overall structure and high-curvature features. Then we develop a Delaunay meshing algorithm to generate meshes from vertices, ensuring geometric fidelity and correct topology. In addition, to obtain accurate SDFs for adaptive meshing and achieve better lightweight reconstruction, we design a hybrid representation combining feature grid and feature tri-plane for better detail capture. Experiments demonstrate that our method can generate high-quality lightweight meshes from point clouds. Compared with methods from various categories, our approach achieves superior results, especially in capturing more details with fewer elements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_High-Fidelity_Lightweight_Mesh_Reconstruction_from_Point_Clouds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_High-Fidelity_Lightweight_Mesh_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chen and Wang, Wentao and Li, Ximeng and Liao, Xinyao and Su, Wanjuan and Tao, Wenbing},
    title     = {High-Fidelity Lightweight Mesh Reconstruction from Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11739-11748}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Huazhong University of Science and Technology"]


title: MDP: Multidimensional Vision Model Pruning with Latency Constraint
authors: Xinglong Sun, Barath Lakshmanan, Maying Shen, Shiyi Lan, Jingde Chen, Jose M. Alvarez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20113-20123
raw_affiliation: [{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]
affiliations: ["NVIDIA"]
cities: ["Santa Clara"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_MDP_Multidimensional_Vision_Model_Pruning_with_Latency_Constraint_CVPR_2025_paper.html

abstract:
Current structural pruning methods face two significant limitations: (i) they often limit pruning to finer-grained levels like channels, making aggressive parameter reduction challenging, and (ii) they focus heavily on parameter and FLOP reduction, with existing latency-aware methods frequently relying on simplistic, suboptimal linear models that fail to generalize well to transformers, where multiple interacting dimensions impact latency. In this paper, we address both limitations by introducing Multi-Dimensional Pruning(MDP), a novel paradigm that jointly optimizes across a variety of pruning granularities--including channels, query/key, heads, embeddings, and blocks. MDP employs an advanced latency modeling technique to accurately capture latency variations across all prunable dimensions, achieving an optimal balance between latency and accuracy. By reformulating pruning as a Mixed-Integer Nonlinear Program (MINLP), MDP efficiently identifies the optimal pruned structure across all prunable dimensions while respecting latency constraints. This versatile framework supports both CNNs and transformers. Extensive experiments demonstrate that MDP significantly outperforms previous methods, especially at high pruning ratios. On ImageNet, MDP achieves a 28% speed increase with a +1.4 Top-1 accuracy improvement over prior work like HALP for ResNet50 pruning. Against the latest transformer pruning method, Isomorphic, MDP delivers an additional 37% acceleration with a +0.7 Top-1 accuracy improvement.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_MDP_Multidimensional_Vision_Model_Pruning_with_Latency_Constraint_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_MDP_Multidimensional_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Xinglong and Lakshmanan, Barath and Shen, Maying and Lan, Shiyi and Chen, Jingde and Alvarez, Jose M.},
    title     = {MDP: Multidimensional Vision Model Pruning with Latency Constraint},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20113-20123}
}


title: OSDFace: One-Step Diffusion Model for Face Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12626-12636
affiliations: ["Shanghai Jiao Tong University", "vivo Mobile Communication Co., Ltd"]
cities: ["Shanghai", "Dongguan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OSDFace_One-Step_Diffusion_Model_for_Face_Restoration_CVPR_2025_paper.html

authors:
Jingkai Wang, Jue Gong, Lin Zhang, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu, Yulun Zhang, Xiaokang Yang

abstract:
Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent with the subject's identity. In this work, we propose OSDFace, a novel one-step diffusion model for face restoration. Specifically, we propose a visual representation embedder (VRE) to better capture prior information and understand the input face. In VRE, low-quality faces are processed by a visual tokenizer and subsequently embedded with a vector-quantized dictionary to generate visual prompts. Additionally, we incorporate a facial identity loss derived from face recognition to further ensure identity consistency. We further employ a generative adversarial network (GAN) as a guidance model to encourage distribution alignment between the restored face and the ground truth. Experimental results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics, generating high-fidelity, natural face images with high identity consistency. The code and model will be released at https://github.com/jkwang28/OSDFace.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OSDFace_One-Step_Diffusion_Model_for_Face_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OSDFace_One-Step_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jingkai and Gong, Jue and Zhang, Lin and Chen, Zheng and Liu, Xing and Gu, Hong and Liu, Yutong and Zhang, Yulun and Yang, Xiaokang},
    title     = {OSDFace: One-Step Diffusion Model for Face Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12626-12636}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "vivo Mobile Communication Co., Ltd", "city": "Dongguan", "country": "China"}]


title: Task Singular Vectors: Reducing Task Interference in Model Merging
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18695-18705
raw_affiliation: [{"affiliation": "Sapienza University of Rome", "city": "Rome", "country": "Italy"}]
affiliations: ["Sapienza University of Rome"]
cities: ["Rome"]
countries: ["Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.html

authors:
Antonio Andrea Gargiulo, Donato Crisostomi, Maria Sofia Bucarelli, Simone Scardapane, Fabrizio Silvestri, Emanuele Rodolà

abstract:
Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is susceptible to task interference. In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular value decomposition. In particular, we concentrate on the resulting singular vectors, which we refer to as Task Singular Vectors (TSV). Recognizing that layer task matrices are often low-rank, we propose TSV-Compress, a simple procedure that compresses them to 10% of their original size while retaining 99% of accuracy. We further leverage this low-rank space to define a new measure of task interference based on the interaction of singular vectors from different tasks. Building on these findings, we introduce TSV-Merge, a novel model merging approach that combines compression with interference reduction, significantly outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gargiulo_Task_Singular_Vectors_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gargiulo_2025_CVPR,
    author    = {Gargiulo, Antonio Andrea and Crisostomi, Donato and Bucarelli, Maria Sofia and Scardapane, Simone and Silvestri, Fabrizio and Rodol\`a, Emanuele},
    title     = {Task Singular Vectors: Reducing Task Interference in Model Merging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18695-18705}
}


title: Functionality Understanding and Segmentation in 3D Scenes
authors: Jaime Corsetti, Francesco Giuliari, Alice Fasoli, Davide Boscaini, Fabio Poiesi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24550-24559
affiliations: ["Fondazione Bruno Kessler", "University of Trento"]
cities: ["Trento", "Trento"]
countries: ["Italy", "Italy"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Corsetti_Functionality_Understanding_and_Segmentation_in_3D_Scenes_CVPR_2025_paper.html

abstract:
Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception to identify fine-grained objects. For example, given a task like "turn on the ceiling light", an embodied AI agent must infer that it needs to locate the light switch, even though the switch is not explicitly mentioned in the task description.To date, no dedicated methods have been developed for this problem. In this paper, we introduce Fun3DU, the first approach designed for functionality understanding in 3D scenes. Fun3DU uses a language model to parse the task description through Chain-of-Thought reasoning in order to identify the object of interest. The identified object is segmented across multiple views of the captured scene by using a VLM. The segmentation results from each view are lifted in 3D and aggregated into the point cloud using geometric information. Fun3DU is training-free, relying entirely on pre-trained models. We evaluate Fun3DU on SceneFun3D, the most recent and only dataset to benchmark this task, which comprises over 3000 task descriptions on 230 scenes.  Our method significantly outperforms state-of-the-art open-vocabulary 3D segmentation approaches. Project page: https://tev-fbk.github.io/fun3du

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Corsetti_Functionality_Understanding_and_Segmentation_in_3D_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Corsetti_Functionality_Understanding_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Corsetti_2025_CVPR,
    author    = {Corsetti, Jaime and Giuliari, Francesco and Fasoli, Alice and Boscaini, Davide and Poiesi, Fabio},
    title     = {Functionality Understanding and Segmentation in 3D Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24550-24559}
}

raw_affiliation:
[{"affiliation": "Fondazione Bruno Kessler", "city": "Trento", "country": "Italy"}, {"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}]


title: Dragin3D: Image Editing by Dragging in 3D Space
authors: Weiran Guang, Xiaoguang Gu, Mengqi Huang, Zhendong Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21502-21512
cities: ["Hefei", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guang_Dragin3D_Image_Editing_by_Dragging_in_3D_Space_CVPR_2025_paper.html

abstract:
Interactive drag editing of images is a valuable task that has gained considerable attention for its precision and controllability. However, existing approaches have primarily focused on manipulating the shape or movement of objects in 2D plane. We propose to extend this drag-based editing task to 3D space. Firstly, we utilize the trajectory of two points to represent the rotational trajectory of the object. Gaussian maps of a circle and a square are centered at these two points, respectively. We use distinct shapes to ensure that symmetric views produce different object representations. Secondly, we introduce a lightweight mapping network to embed the object features into two Gaussian maps to obtain a continuous control condition that guides the model in learning the correspondence between the trajectory and the object. Finally, to overcome the limitations of current 3D object reconstruction datasets, which typically consist of object maps with transparent backgrounds, we affix random backgrounds to them. This modification helps improve the model's ability to ignore background interference when editing real images with complex backgrounds. Experiments demonstrate that our approach successfully achieves object rotation within the drag framework and demonstrates strong generalization to real-world images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guang_Dragin3D_Image_Editing_by_Dragging_in_3D_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guang_Dragin3D_Image_Editing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guang_2025_CVPR,
    author    = {Guang, Weiran and Gu, Xiaoguang and Huang, Mengqi and Mao, Zhendong},
    title     = {Dragin3D: Image Editing by Dragging in 3D Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21502-21512}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "State Key Laboratory of Communication Content Cognition, People\u2019s Daily Online", "city": "Beijing", "country": "China"}]

affiliations:
["University of Science and Technology of China", "State Key Laboratory of Communication Content Cognition, People\u2019s Daily Online"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6864-6874
cities: ["Zhuhai", "Beijing", "Guangzhou", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MMTL-UniAD_A_Unified_Framework_for_Multimodal_and_Multi-Task_Learning_in_CVPR_2025_paper.html

title:
MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception

authors:
Wenzhuo Liu, Wenshuo Wang, Yicheng Qiao, Qiannan Guo, Jiayin Zhu, Pengfei Li, Zilong Chen, Huiming Yang, Zhiwei Li, Lening Wang, Tiao Tan, Huaping Liu

abstract:
Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential benefits of joint learning between these tasks. This paper proposes MMTL-UniAD, a unified multi-modal multi-task learning framework that simultaneously recognizes driver behavior (e.g., looking around, talking), driver emotion (e.g., anxiety, happiness), vehicle behavior (e.g., parking, turning), and traffic context (e.g., traffic jam, traffic smooth). A key challenge is avoiding negative transfer between tasks, which can impair learning performance. To address this, we introduce two key components into the framework: one is the multi-axis region attention network to extract global context-sensitive features, and the other is the dual-branch multimodal embedding to learn multimodal embeddings from both task-shared and task-specific features. The former uses a multi-attention mechanism to extract task-relevant features, mitigating negative transfer caused by task-unrelated features. The latter employs a dual-branch structure to adaptively adjust task-shared and task-specific parameters, enhancing cross-task knowledge transfer while reducing task conflicts. We assess MMTL-UniAD on the AIDE dataset, using a series of ablation studies, and show that it outperforms state-of-the-art methods across all four tasks. The code is available on https://github.com/Wenzhuo-Liu/MMTL-UniAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MMTL-UniAD_A_Unified_Framework_for_Multimodal_and_Multi-Task_Learning_in_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Wenzhuo and Wang, Wenshuo and Qiao, Yicheng and Guo, Qiannan and Zhu, Jiayin and Li, Pengfei and Chen, Zilong and Yang, Huiming and Li, Zhiwei and Wang, Lening and Tan, Tiao and Liu, Huaping},
    title     = {MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6864-6874}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology, Zhuhai", "city": "Zhuhai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Beijing University of Chemical Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Institute of Technology, Zhuhai", "Tsinghua University", "HKUST(GZ)", "Beijing University of Chemical Technology", "Beihang University"]


title: T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
authors: Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, Xihui Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8406-8416
cities: ["Hong Kong", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_T2V-CompBench_A_Comprehensive_Benchmark_for_Compositional_Text-to-video_Generation_CVPR_2025_paper.html

abstract:
Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored. Previous text-to-video benchmarks also neglect this important ability for evaluation. In this work, we conduct the first systematic study on compositional text-to-video generation. We propose T2V-CompBench, the first benchmark tailored for compositional text-to-video generation. T2V-CompBench encompasses diverse aspects of compositionality, including consistent attribute binding, dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy. We further carefully design evaluation metrics of multimodal large language model (MLLM)-based, detection-based, and tracking-based metrics, which can better reflect the compositional text-to-video generation quality of seven proposed categories with 1400 text prompts. The effectiveness of the proposed metrics is verified by correlation with human evaluations. We also benchmark various text-to-video generative models and conduct in-depth analysis across different models and various compositional categories. We find that compositional text-to-video generation is highly challenging for current models, and we hope our attempt could shed light on future research in this direction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_T2V-CompBench_A_Comprehensive_Benchmark_for_Compositional_Text-to-video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_T2V-CompBench_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Kaiyue and Huang, Kaiyi and Liu, Xian and Wu, Yue and Xu, Zihan and Li, Zhenguo and Liu, Xihui},
    title     = {T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8406-8416}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]

affiliations:
["The University of Hong Kong", "The Chinese University of Hong Kong", "Huawei Noah\u2019s Ark Lab"]


title: Self-Evolving Visual Concept Library using Vision-Language Critics
authors: Atharva Sehgal, Patrick Yuan, Ziniu Hu, Yisong Yue, Jennifer J. Sun, Swarat Chaudhuri
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13124-13134
affiliations: ["University of Texas at Austin", "Cornell University", "California Institute of Technology"]
cities: ["Austin", "Ithaca", "Pasadena"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sehgal_Self-Evolving_Visual_Concept_Library_using_Vision-Language_Critics_CVPR_2025_paper.html

abstract:
We study the problem of building a visual concept library for visual recognition. Building effective visual concept libraries is challenging, as manual definition is labor-intensive, while relying solely on LLMs for concept generation can result in concepts that lack discriminative power or fail to account for the complex interactions between them. Our approach, ESCHER, takes a library learning perspective to iteratively discover and improve visual concepts. ESCHER uses a vision-language model (VLM) as a critic to iteratively refine the concept library, including accounting for interactions between concepts and how they affect downstream classifiers. By leveraging the in-context learning abilities of LLMs and the history of performance using various concepts, ESCHER dynamically improves its concept generation strategy based on the VLM critic's feedback. Finally, ESCHER does not require any human annotations, and is thus an automated plug-and-play framework. We empirically demonstrate the ability of ESCHER to learn a concept library for zero-shot, few-shot, and fine-tuning visual classification tasks. This work represents, to our knowledge, the first application of concept library learning to real-world visual tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sehgal_Self-Evolving_Visual_Concept_Library_using_Vision-Language_Critics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sehgal_Self-Evolving_Visual_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sehgal_2025_CVPR,
    author    = {Sehgal, Atharva and Yuan, Patrick and Hu, Ziniu and Yue, Yisong and Sun, Jennifer J. and Chaudhuri, Swarat},
    title     = {Self-Evolving Visual Concept Library using Vision-Language Critics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13124-13134}
}

raw_affiliation:
[{"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "California Institute of Technology", "city": "Pasadena", "country": "USA"}]


title: Multimodal Autoregressive Pre-training of Large Vision Encoders
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9641-9654
raw_affiliation: [{"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]
affiliations: ["Apple"]
cities: ["Cupertino"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fini_Multimodal_Autoregressive_Pre-training_of_Large_Vision_Encoders_CVPR_2025_paper.html

authors:
Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor G. Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua Susskind, Alaaeldin El-Nouby

abstract:
We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Fur- thermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal im- age understanding across diverse settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fini_Multimodal_Autoregressive_Pre-training_of_Large_Vision_Encoders_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Fini_2025_CVPR,
    author    = {Fini, Enrico and Shukor, Mustafa and Li, Xiujun and Dufter, Philipp and Klein, Michal and Haldimann, David and Aitharaju, Sai and da Costa, Victor G. Turrisi and B\'ethune, Louis and Gan, Zhe and Toshev, Alexander and Eichner, Marcin and Nabi, Moin and Yang, Yinfei and Susskind, Joshua and El-Nouby, Alaaeldin},
    title     = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9641-9654}
}


title: AKiRa: Augmentation Kit on Rays for Optical Video Generation
authors: Xi Wang, Robin Courant, Marc Christie, Vicky Kalogeiton
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2609-2619
affiliations: ["LIX, \u00b4Ecole Polytechnique, IP Paris", "Univ Rennes, IRISA, Inria, CNRS"]
cities: ["Palaiseau", "Rennes"]
countries: ["France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AKiRa_Augmentation_Kit_on_Rays_for_Optical_Video_Generation_CVPR_2025_paper.html

abstract:
Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, including dynamic camera motion, zoom, distorted lens and focus shifts. These motion and optical aspects are crucial for adding controllability and cinematic elements to generation frameworks, ultimately resulting in visual content that draws focus, enhances mood, and guides emotions according to filmmakers' controls. In this paper, we aim to close the gap between controllable video generation and camera optics. To achieve this, we propose AKiRa (Augmentation Kit on Rays), a novel augmentation framework that builds and trains a camera adapter with a complex camera model over an existing video generation backbone. It enables fine-tuned control over camera motion as well as complex optical parameters (focal length, distortion, aperture) to achieve cinematic effects such as zoom, fisheye effect, and bokeh. Extensive experiments demonstrate AKiRa's effectiveness in combining and composing camera optics while outperforming all state-of-the-art methods. This work sets a new landmark in controlled and optically enhanced video generation, paving the way for future camera diffusion methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AKiRa_Augmentation_Kit_on_Rays_for_Optical_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AKiRa_Augmentation_Kit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xi and Courant, Robin and Christie, Marc and Kalogeiton, Vicky},
    title     = {AKiRa: Augmentation Kit on Rays for Optical Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2609-2619}
}

raw_affiliation:
[{"affiliation": "LIX, \u00b4Ecole Polytechnique, IP Paris", "city": "Palaiseau", "country": "France"}, {"affiliation": "Univ Rennes, IRISA, Inria, CNRS", "city": "Rennes", "country": "France"}]


title: Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory
authors: Wenliang Zhong, Haoyu Tang, Qinghai Zheng, Mingzhu Xu, Yupeng Hu, Weili Guan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25581-25589
cities: ["Jinan", "Fuzhou", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Towards_Stable_and_Storage-efficient_Dataset_Distillation_Matching_Convexified_Trajectory_CVPR_2025_paper.html

abstract:
The rapid evolution of deep learning and large language models has led to an exponential growth in the demand for training data, prompting the development of Dataset Distillation methods to address the challenges of managing large datasets. Among these, Matching Training Trajectories (MTT) has been a prominent approach, which replicates the training trajectory of an expert network on real data with a synthetic dataset. However, our investigation found that this method suffers from three significant limitations: 1. Instability of expert trajectory generated by Stochastic Gradient Descent (SGD); 2. Low convergence speed of the distillation process; 3. High storage consumption of the expert trajectory. To address these issues, we offer a new perspective on understanding the essence of Dataset Distillation and MTT through a simple transformation of the objective function, and introduce a novel method called Matching Convexified Trajectory (MCT), which aims to provide better guidance for the student trajectory. MCT creates convex combinations of expert trajectories by selecting a few expert models, guiding student networks to converge quickly and stably. This trajectory is not only easier to store, but also enables continuous sampling strategies during the distillation process, ensuring thorough learning and fitting of the entire expert trajectory. The comprehensive experiment of three public datasets verified that MCT is superior to the traditional MTT method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Towards_Stable_and_Storage-efficient_Dataset_Distillation_Matching_Convexified_Trajectory_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Towards_Stable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Wenliang and Tang, Haoyu and Zheng, Qinghai and Xu, Mingzhu and Hu, Yupeng and Guan, Weili},
    title     = {Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25581-25589}
}

raw_affiliation:
[{"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "College of Computer and Data Science, Fuzhou University", "city": "Fuzhou", "country": "China"}, {"affiliation": "School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Software, Shandong University", "College of Computer and Data Science, Fuzhou University", "School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)"]


title: TSAM: Temporal SAM Augmented with Multimodal Prompts for Referring Audio-Visual Segmentation
authors: Abduljalil Radman, Jorma Laaksonen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23947-23956
affiliations: ["Department of Computer Science, Aalto University"]
cities: ["Espoo"]
countries: ["Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Radman_TSAM_Temporal_SAM_Augmented_with_Multimodal_Prompts_for_Referring_Audio-Visual_CVPR_2025_paper.html

abstract:
Referring audio-visual segmentation (Ref-AVS) aims to segment objects within audio-visual scenes using multimodal cues embedded in text expressions. While the Segment Anything Model (SAM) has revolutionized visual segmentation, its applicability to Ref-AVS, where multimodal cues act as novel prompts, remains unexplored. SAM's limitation to single-frame segmentation also hinders its ability to capture essential temporal context needed for multi-frame audio-visual segmentation. To address this gap, we propose TSAM, a novel extension of SAM designed to leverage multimodal cues for precise segmentation in dynamic audio-visual scenes. TSAM enhances SAM's image encoder with a temporal modeling branch, enabling spatio-temporal learning and deep multimodal fusion across video frames, while retaining SAM's pre-trained knowledge. Additionally, TSAM replaces SAM's user-interactive prompting mechanism with sparse and dense data-driven prompts, enabling more effective integration of audio-visual inputs and reference text expressions. Extensive experiments on the Ref-AVS dataset demonstrate TSAM's superiority over state-of-the-art methods. The results illustrate its effectiveness in segmenting objects in dynamic audio-visual scenes using text-based multimodal cues and its strong generalization to unseen objects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Radman_TSAM_Temporal_SAM_Augmented_with_Multimodal_Prompts_for_Referring_Audio-Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Radman_TSAM_Temporal_SAM_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Radman_2025_CVPR,
    author    = {Radman, Abduljalil and Laaksonen, Jorma},
    title     = {TSAM: Temporal SAM Augmented with Multimodal Prompts for Referring Audio-Visual Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23947-23956}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Aalto University", "city": "Espoo", "country": "Finland"}]


title: TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance
authors: Mushui Liu, Dong She, Jingxuan Pang, Qihan Huang, Jiacheng Ying, Wanggui He, Yuanlei Hou, Siming Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2714-2723
cities: ["Hangzhou", "Hefei", "Hangzhou", "Abu Dhabi"]
countries: ["China", "China", "China", "UAE"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_TFCustom_Customized_Image_Generation_with_Time-Aware_Frequency_Feature_Guidance_CVPR_2025_paper.html

abstract:
Subject-driven image personalization has seen notable advancements, especially with the advent of the ReferenceNet paradigm. ReferenceNet excels in integrating image reference features, making it highly applicable in creative and commercial settings. However, current implementations of ReferenceNet primarily operate as latent-level feature extractors, which limit their potential. This constraint hinders the provision of appropriate features to the denoising backbone across different timesteps, leading to suboptimal image consistency. In this paper, we revisit the extraction of reference features and propose TFCustom, a model framework designed to focus on reference image features at different temporal steps and frequency levels. Specifically, we firstly propose synchronized ReferenceNet to extract reference image features while simultaneously optimizing noise injection and denoising for the reference image. We also propose a time-aware frequency feature refinement module that leverages high- and low-frequency filters, combined with time embeddings, to adaptively select the degree of reference feature injection. Additionally, to enhance the similarity between reference objects and the generated image, we introduce a novel reward-based loss that encourages greater alignment between the reference and generated images. Experimental results demonstrate state-of-the-art performance in both multi-object and single-object reference generation, with significant improvements in texture and textual detail generation over existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_TFCustom_Customized_Image_Generation_with_Time-Aware_Frequency_Feature_Guidance_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Mushui and She, Dong and Pang, Jingxuan and Huang, Qihan and Ying, Jiacheng and He, Wanggui and Hou, Yuanlei and Fu, Siming},
    title     = {TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2714-2723}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Middle East Centre, RIPED", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["Zhejiang University", "University of Science and Technology of China", "Alibaba Group", "Middle East Centre, RIPED"]


authors: Mengnan Liu, Le Wang, Sanping Zhou, Kun Xia, Xiaolong Sun, Gang Hua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13865-13875
cities: ["Xi'an", "Xi'an", "Unknown"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Boosting_Point-Supervised_Temporal_Action_Localization_through_Integrating_Query_Reformation_and_CVPR_2025_paper.html

title:
Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport

abstract:
Point-supervised Temporal Action Localization poses significant challenges due to the difficulty of identifying complete actions with a single-point annotation per action.  Existing methods typically employ Multiple Instance Learning, which struggles to capture global temporal context and requires heuristic post-processing. In research on fully-supervised tasks, DETR-based structures have effectively addressed these limitations. However, it is nontrivial to merely adapt DETR to this task, encountering two major bottlenecks. (1) How to integrate point label information into the model and (2) How to select optimal decoder proposals for training in the absence of complete action segment annotations. To address this issue, we introduce an end-to-end framework by integrating Query Reformation and Optimal Transport (QROT). Specifically, we encode point labels through a set of semantic consensus queries, enabling effective focus on action-relevant snippets. Furthermore, we integrate an optimal transport mechanism to generate high-quality pseudo labels. These pseudo-labels facilitate precise proposals selection based on Hungarian algorithm, significantly enhancing localization accuracy in point-supervised settings.  Extensive experiments on the THUMOS14 and ActivityNet-v1.3 datasets demonstrate that our method outperforms existing MIL-based approaches, offering more stable and accurate temporal action localization in point-level supervision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Boosting_Point-Supervised_Temporal_Action_Localization_through_Integrating_Query_Reformation_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Boosting_Point-Supervised_Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Mengnan and Wang, Le and Zhou, Sanping and Xia, Kun and Sun, Xiaolong and Hua, Gang},
    title     = {Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13865-13875}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Multimodal Experiences Research Lab, Dolby Laboratories", "city": "Unknown", "country": "USA"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "Multimodal Experiences Research Lab, Dolby Laboratories"]


title: SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2556-2567
cities: ["Guildford", "Guildford"]
countries: ["United Kingdom", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Koley_SketchFusion_Learning_Universal_Sketch_Features_through_Fusing_Foundation_Models_CVPR_2025_paper.html

authors:
Subhadeep Koley, Tapas Kumar Dutta, Aneeshan Sain, Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Yi-Zhe Song

abstract:
While foundation models have revolutionised computer vision, their effectiveness for sketch understanding remains limited by the unique challenges of abstract, sparse visual inputs. Through systematic analysis, we uncover two fundamental limitations: Stable Diffusion (SD) struggles to extract meaningful features from abstract sketches (unlike its success with photos), and exhibits a pronounced frequency-domain bias that suppresses essential low-frequency components needed for sketch understanding. Rather than costly retraining, we address these limitations by strategically combining SD with CLIP, whose strong semantic understanding naturally compensates for SD's spatial-frequency biases. By dynamically injecting CLIP features into SD's denoising process and adaptively aggregating features across semantic levels, our method achieves state-of-the-art performance in sketch retrieval (+3.35%), recognition (+1.06%), segmentation (+29.42%), and correspondence learning (+21.22%), demonstrating the first truly universal sketch feature representation in the era of foundation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Koley_SketchFusion_Learning_Universal_Sketch_Features_through_Fusing_Foundation_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Koley_2025_CVPR,
    author    = {Koley, Subhadeep and Dutta, Tapas Kumar and Sain, Aneeshan and Chowdhury, Pinaki Nath and Bhunia, Ayan Kumar and Song, Yi-Zhe},
    title     = {SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2556-2567}
}

raw_affiliation:
[{"affiliation": "SketchX, CVSSP, University of Surrey", "city": "Guildford", "country": "United Kingdom"}, {"affiliation": "iFlyTek-Surrey Joint Research Centre on Artificial Intelligence", "city": "Guildford", "country": "United Kingdom"}]

affiliations:
["SketchX, CVSSP, University of Surrey", "iFlyTek-Surrey Joint Research Centre on Artificial Intelligence"]


title: Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior
authors: Haitao Wu, Qing Li, Changqing Zhang, Zhen He, Xiaomin Ying
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2246-2257
cities: ["Tianjin", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Bridging_the_Vision-Brain_Gap_with_an_Uncertainty-Aware_Blur_Prior_CVPR_2025_paper.html

abstract:
Can our brain signals faithfully reflect the original visual stimuli, even including high-frequency details? Although human perceptual and cognitive capacities enable us to process and remember visual information, these abilities are constrained by several factors, such as limited attentional resources and finite capacity of visual memory. When visual stimuli are processed by human visual system into brain signals, some information is inevitably lost, leading to a discrepancy known as the System GAP. Additionally, perceptual and cognitive dynamics, along with technical noise in signal acquisition, degrade the fidelity of brain signals relative to the visual stimuli, known as the Random GAP. When encoded brain representations are directly aligned with the corresponding pretrained image features, the System GAP and Random GAP between paired data challenge the model, requiring it to bridge these gaps. However, due to limited paired data, these gaps are difficult for the model to learn, leading to overfitting and poor generalization to new data. To address these GAPs, we propose a simple yet effective approach called the Uncertainty-aware Blur Prior (UBP). It estimates the uncertainty within the paired data, reflecting the mismatch between brain signals and visual stimuli. Based on uncertainty, UBP dynamically blurs the high-frequency details of the original images, reducing the impact of mismatch and improving alignment. Our method achieves a top-1 accuracy of 50.9% and a top-5 accuracy of 79.7% on the zero-shot brain-to-image retrieval task, surpassing previous state-of-the-art methods. Code is available at https://github.com/HaitaoWuTJU/Uncertainty-aware-Blur-Prior.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Bridging_the_Vision-Brain_Gap_with_an_Uncertainty-Aware_Blur_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Bridging_the_Vision-Brain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Haitao and Li, Qing and Zhang, Changqing and He, Zhen and Ying, Xiaomin},
    title     = {Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2246-2257}
}

raw_affiliation:
[{"affiliation": "College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Beijing Institute of Basic Medical Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["College of Intelligence and Computing, Tianjin University", "Beijing Institute of Basic Medical Sciences"]


title: Invisible Backdoor Attack against Self-supervised Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25790-25801
cities: ["Hangzhou", "New Brunswick", "Singapore", "Nanjing", "Newark", "Amherst"]
countries: ["China", "USA", "Singapore", "China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Invisible_Backdoor_Attack_against_Self-supervised_Learning_CVPR_2025_paper.html

authors:
Hanrong Zhang, Zhenting Wang, Boheng Li, Fulin Lin, Tingxu Han, Mingyu Jin, Chenlu Zhan, Mengnan Du, Hongwei Wang, Shiqing Ma

abstract:
Self-supervised learning (SSL) models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in SSL often involve noticeable triggers, like colored patches or visible noise, which are vulnerable to human inspection. This paper proposes an imperceptible and effective backdoor attack against self-supervised models. We first find that existing imperceptible triggers designed for supervised learning are less effective in compromising self-supervised models. We then identify this ineffectiveness is attributed to the overlap in distributions between the backdoor and augmented samples used in SSL. Building on this insight, we design an attack using optimized triggers disentangled with the augmented transformation in the SSL, while remaining imperceptible to human vision. Experiments on five datasets and six SSL algorithms demonstrate our attack is highly effective and stealthy. It also has strong resistance to existing backdoor defenses. Our code can be found at https://github.com/Zhang-Henry/INACTIVE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Invisible_Backdoor_Attack_against_Self-supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Invisible_Backdoor_Attack_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Hanrong and Wang, Zhenting and Li, Boheng and Lin, Fulin and Han, Tingxu and Jin, Mingyu and Zhan, Chenlu and Du, Mengnan and Wang, Hongwei and Ma, Shiqing},
    title     = {Invisible Backdoor Attack against Self-supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25790-25801}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "New Jersey Institute of Technology", "city": "Newark", "country": "USA"}, {"affiliation": "University of Massachusetts Amherst", "city": "Amherst", "country": "USA"}]

affiliations:
["Zhejiang University", "Rutgers University", "Nanyang Technological University", "Nanjing University", "New Jersey Institute of Technology", "University of Massachusetts Amherst"]


authors: Lee Chae-Yeon, Oh Hyun-Bin, Han EunGi, Kim Sung-Bin, Suekyeong Nam, Tae-Hyun Oh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21065-21074
cities: ["Pohang", "Pohang", "Seongnam-si", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chae-Yeon_Perceptually_Accurate_3D_Talking_Head_Generation_New_Definitions_Speech-Mesh_Representation_CVPR_2025_paper.html

title:
Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics

abstract:
Recent advancements in speech-driven 3D talking head generation have made significant progress in lip synchronization. However, existing models still struggle to capture the perceptual alignment between varying speech characteristics and corresponding lip movements. In this work, we claim that three criteria--Temporal Synchronization, Lip Readability, and Expressiveness--are crucial for achieving perceptually accurate lip movements. Motivated by our hypothesis that a desirable representation space exists to meet these three criteria, we introduce a speech-mesh synchronized representation that captures intricate correspondences between speech signals and 3D face meshes. We found that our learned representation exhibits desirable characteristics, and we plug it into existing models as a perceptual loss to better align lip movements to the given speech. In addition, we utilize this representation as a perceptual metric and introduce two other physically grounded lip synchronization metrics to assess how well the generated 3D talking heads align with these three criteria. Experiments show that training 3D talking head generation models with our perceptual loss significantly improve all three aspects of perceptually accurate lip synchronization. Codes and datasets are available at https://perceptual-3d-talking-head.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chae-Yeon_Perceptually_Accurate_3D_Talking_Head_Generation_New_Definitions_Speech-Mesh_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chae-Yeon_Perceptually_Accurate_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chae-Yeon_2025_CVPR,
    author    = {Chae-Yeon, Lee and Hyun-Bin, Oh and EunGi, Han and Sung-Bin, Kim and Nam, Suekyeong and Oh, Tae-Hyun},
    title     = {Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21065-21074}
}

raw_affiliation:
[{"affiliation": "Grad. School of AI, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Dept. of Electrical Engineering, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "KRAFTON", "city": "Seongnam-si", "country": "South Korea"}, {"affiliation": "School of Computing, KAIST", "city": "Daejeon", "country": "South Korea"}]

affiliations:
["Grad. School of AI, POSTECH", "Dept. of Electrical Engineering, POSTECH", "KRAFTON", "School of Computing, KAIST"]


title: BWFormer: Building Wireframe Reconstruction from Airborne LiDAR Point Cloud with Transformer
authors: Yuzhou Liu, Lingjie Zhu, Hanqiao Ye, Shangfeng Huang, Xiang Gao, Xianwei Zheng, Shuhan Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22215-22224
cities: ["Beijing", "Beijing", "Unknown", "Calgary", "Wuhan"]
countries: ["China", "China", "Unknown", "Canada", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_BWFormer_Building_Wireframe_Reconstruction_from_Airborne_LiDAR_Point_Cloud_with_CVPR_2025_paper.html

abstract:
In this paper, we present BWFormer, a novel Transformer-based model for building wireframe reconstruction from airborne LiDAR point cloud.  The problem is solved in a ground-up manner here by detecting the building corners in 2D, lifting and connecting them in 3D space afterwards with additional data augmentation.Due to the 2.5D  characteristic of the airborne LiDAR point cloud, we simplify the problem by projecting the points on the ground plane to produce a 2D height map. With the height map, a heat map is first generated with pixel-wise corner likelihood to predict the possible 2D corners.Then, 3D corners are predicted by a Transformer-based network with extra height embedding initialization. This 2D-to-3D corner detection strategy reduces the search space significantly. To recover the topological connections among the corners, edges are finally predicted from the height map with the proposed edge attention mechanism, which extracts holistic features and preserves local details simultaneously. In addition, due to the limited datasets in the field and the irregularity of the point clouds, a conditional latent diffusion model for LiDAR scanning simulation is utilized for data augmentation. BWFormer surpasses other state-of-the-art methods, especially in reconstruction completeness.  Our code is available at: https://github.com/3dv-casia/BWformer/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_BWFormer_Building_Wireframe_Reconstruction_from_Airborne_LiDAR_Point_Cloud_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_BWFormer_Building_Wireframe_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuzhou and Zhu, Lingjie and Ye, Hanqiao and Huang, Shangfeng and Gao, Xiang and Zheng, Xianwei and Shen, Shuhan},
    title     = {BWFormer: Building Wireframe Reconstruction from Airborne LiDAR Point Cloud with Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22215-22224}
}

raw_affiliation:
[{"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": "Cenozoic Robotics", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Calgary", "city": "Calgary", "country": "Canada"}, {"affiliation": "The State Key Lab. LIESMARS, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "Cenozoic Robotics", "University of Calgary", "The State Key Lab. LIESMARS, Wuhan University"]


title: Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models
authors: Jinjin Zhang, Qiuyu Huang, Junjie Liu, Xiefan Guo, Di Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23464-23473
cities: ["Beijing", "Beijing", "Unknown"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Diffusion-4K_Ultra-High-Resolution_Image_Synthesis_with_Latent_Diffusion_Models_CVPR_2025_paper.html

abstract:
In this paper, we present Diffusion-4K, a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models. The core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K, a comprehensive benchmark for ultra-high-resolution image generation. We curated a high-quality 4K dataset with carefully selected images and captions generated by GPT-4o. Additionally, we introduce GLCM Score and compression ratio metrics to evaluate fine details, combined with holistic measures such as FID, Aesthetics and CLIPScore for a comprehensive assessment of ultra-high-resolution images. (2) Wavelet-based Fine-tuning: we propose a wavelet-based fine-tuning approach for direct training with photorealistic 4K images, applicable to various latent diffusion models, demonstrating its effectiveness in synthesizing highly detailed 4K images. Consequently, Diffusion-4K achieves impressive performance in high-quality image synthesis and text prompt adherence, especially when powered by modern large-scale diffusion models (e.g., SD3-2B and Flux-12B). Extensive experimental results from our benchmark demonstrate the superiority of Diffusion-4K in ultra-high-resolution image synthesis. Code is available at https://github.com/zhang0jhon/diffusion-4k.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Diffusion-4K_Ultra-High-Resolution_Image_Synthesis_with_Latent_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Diffusion-4K_Ultra-High-Resolution_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jinjin and Huang, Qiuyu and Liu, Junjie and Guo, Xiefan and Huang, Di},
    title     = {Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23464-23473}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Complex and Critical Software Environment, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Meituan", "city": "Unknown", "country": "China"}]

affiliations:
["State Key Laboratory of Complex and Critical Software Environment, Beihang University", "School of Computer Science and Engineering, Beihang University", "Meituan"]


title: AffordDP: Generalizable Diffusion Policy with Transferable Affordance
authors: Shijie Wu, Yihang Zhu, Yunao Huang, Kaizhen Zhu, Jiayuan Gu, Jingyi Yu, Ye Shi, Jingya Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6971-6980
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/Wu_AffordDP_Generalizable_Diffusion_Policy_with_Transferable_Affordance_CVPR_2025_paper.html

abstract:
Diffusion-based policies have shown impressive performance in robotic manipulation tasks while struggling with out-of-domain distributions. Recent efforts attempted to enhance generalization by improving the visual feature encoding for diffusion policy. However, their generalization is typically limited to the same category with similar appearances. Our key insight is that leveraging affordances--manipulation priors that define "where" and "how" an agent interacts with an object--can substantially enhance generalization to entirely unseen object instances and categories. We introduce the Diffusion Policy with transferable Affordance (AffordDP), designed for generalizable manipulation across novel categories. AffordDP models affordances through 3D contact points and post-contact trajectories, capturing the essential static and dynamic information for complex tasks. The transferable affordance from in-domain data to unseen objects is achieved by estimating a 6D transformation matrix using foundational vision models and point cloud registration techniques. More importantly, we incorporate affordance guidance during diffusion sampling that can refine action sequence generation. This guidance directs the generated action to gradually move towards the desired manipulation for unseen objects while keeping the generated action within the manifold of action space. Experimental results from both simulated and real-world environments demonstrate that AffordDP consistently outperforms previous diffusion-based methods, successfully generalizing to unseen instances and categories where others fail.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_AffordDP_Generalizable_Diffusion_Policy_with_Transferable_Affordance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_AffordDP_Generalizable_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Shijie and Zhu, Yihang and Huang, Yunao and Zhu, Kaizhen and Gu, Jiayuan and Yu, Jingyi and Shi, Ye and Wang, Jingya},
    title     = {AffordDP: Generalizable Diffusion Policy with Transferable Affordance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6971-6980}
}


title: HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2535-2544
affiliations: ["Stanford University", "NVIDIA", "CUHK", "HKU", "NTU", "UCSD", "Together AI"]
cities: ["Stanford", "Santa Clara", "Hong Kong", "Hong Kong", "Singapore", "San Diego", "Palo Alto"]
countries: ["USA", "USA", "China", "China", "Singapore", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kumbong_HMAR_Efficient_Hierarchical_Masked_Auto-Regressive_Image_Generation_CVPR_2025_paper.html

authors:
Hermann Kumbong, Xian Liu, Tsung-Yi Lin, Ming-Yu Liu, Xihui Liu, Ziwei Liu, Daniel Y. Fu, Christopher Re, David W. Romero

abstract:
Visual AutoRegressive modeling (VAR) shows promise in bridging the speed and quality gap between autoregressive image models and diffusion models. VAR reformulates autoregressive modeling by decomposing an image into successive resolution scales. During inference, an image is generated by predicting all the tokens in the next (higher-resolution) scale, conditioned on all tokens in all previous (lower-resolutions) scales. However, this formulation suffers from reduced image quality due to parallel generation of all tokens in a resolution scale; has sequence lengths scaling superlinearly in image resolution; and requires retraining to change the resolution sampling schedule.We introduce \underline H ierarchical \underline M asked \underline A uto\underline R egressive modeling (HMAR), a new image generation algorithm that alleviates these issues using next-scale prediction and masked prediction to generate high-quality images with fast sampling. HMAR reformulates next-scale prediction as a Markovian process, wherein prediction of each resolution scale is conditioned only on tokens in its immediate predecessor instead of the tokens in all predecessor resolutions. When predicting a resolution scale, HMAR uses a controllable multi-step masked generation procedure to generate a subset of the tokens in each step. On ImageNet 256  x  256 and 512  x  512 benchmarks, HMAR models match or outperform parameter-matched VAR, diffusion, and autoregressive baselines. We develop efficient IO-aware block-sparse attention kernels that allow HMAR to achieve faster training and inference times over VAR by over 2.5xand 1.75 xrespectively, as well as over 3 xlower inference memory footprint. Finally, the Markovian formulation of HMAR yields additional flexibility over VAR; we show that its sampling schedule can be changed without further training, and it can be applied to image editing tasks in a zero-shot manner.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumbong_HMAR_Efficient_Hierarchical_Masked_Auto-Regressive_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumbong_HMAR_Efficient_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kumbong_2025_CVPR,
    author    = {Kumbong, Hermann and Liu, Xian and Lin, Tsung-Yi and Liu, Ming-Yu and Liu, Xihui and Liu, Ziwei and Fu, Daniel Y. and Re, Christopher and Romero, David W.},
    title     = {HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2535-2544}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "UCSD", "city": "San Diego", "country": "USA"}, {"affiliation": "Together AI", "city": "Palo Alto", "country": "USA"}]


title: OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22442-22452
affiliations: ["NVIDIA", "The Hong Kong Polytechnic University", "Beijing Institute of Technology"]
cities: ["Santa Clara", "Hong Kong", "Beijing"]
countries: ["USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OmniDrive_A_Holistic_Vision-Language_Dataset_for_Autonomous_Driving_with_Counterfactual_CVPR_2025_paper.html

authors:
Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, Jose M. Alvarez

abstract:
The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabilities from 2D to full 3D understanding is crucial for real-world applications. To address this challenge, we propose OmniDrive, a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning. This approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions. Our counterfactual-based synthetic data annotation process generates large-scale, high-quality datasets, providing denser supervision signals that bridge planning trajectories and language-based reasoning. Futher, we explore two advanced OmniDrive-Agent frameworks, namely Omni-L and Omni-Q, to assess the importance of vision-language alignment versus 3D perception, revealing critical insights into designing effective LLM-agents. Significant improvements on the DriveLM Q&A benchmark and nuScenes open-loop planning demonstrate the effectiveness of our dataset and methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OmniDrive_A_Holistic_Vision-Language_Dataset_for_Autonomous_Driving_with_Counterfactual_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shihao and Yu, Zhiding and Jiang, Xiaohui and Lan, Shiyi and Shi, Min and Chang, Nadine and Kautz, Jan and Li, Ying and Alvarez, Jose M.},
    title     = {OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22442-22452}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]


title: DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification
authors: Zhenyu Cui, Jiahuan Zhou, Yuxin Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3573-3582
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_DKC_Differentiated_Knowledge_Consolidation_for_Cloth-Hybrid_Lifelong_Person_Re-identification_CVPR_2025_paper.html

abstract:
Lifelong person re-identification (LReID) aims to match the same person using sequentially collected data. However, due to the long-term nature of lifelong learning, the inevitable changes in human clothes prevent the model from relying on unified discriminative information (e.g., clothing style) to match the same person in the streaming data, demanding differentiated cloth-irrelevant information. Unfortunately, existing LReID methods typically fail to leverage such knowledge resulting in the exacerbation of catastrophic forgetting issues. Therefore, in this paper, we focus on a challenging practical task called Cloth-Hybrid Lifelong Person Re-identification (CH-LReID), which requires matching the same person wearing different clothes using sequentially collected data. A Differentiated Knowledge Consolidation (DKC) framework is designed to unify and balance distinct knowledge across streaming data. The core idea is to adaptively balance differentiated knowledge and compatibly consolidate cloth-relevant and cloth-irrelevant information. To this end, a Differentiated Knowledge Transfer (DKT) module and a Latent Knowledge Consolidation (LKC) module are designed to adaptively discover differentiated new knowledge, while eliminating the derived domain shift of old knowledge via reconstructing the old latent feature space, respectively. Then, to further alleviate the catastrophic conflict between differentiated new and old knowledge, we further propose a Dual-level Distribution Alignment (DDA) module to align the distribution of discriminative knowledge at both the instance level and the fine-grained level. Extensive experiments on multiple benchmarks demonstrate the superiority of our method against existing methods in both CH-LReID and traditional LReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/DKC-CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_DKC_Differentiated_Knowledge_Consolidation_for_Cloth-Hybrid_Lifelong_Person_Re-identification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cui_DKC_Differentiated_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Zhenyu and Zhou, Jiahuan and Peng, Yuxin},
    title     = {DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3573-3582}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}]


title: Enhancing Facial Privacy Protection via Weakening Diffusion Purification
authors: Ali Salar, Qing Liu, Yingli Tian, Guoying Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8235-8244
cities: ["Oulu", "New York"]
countries: ["Finland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Salar_Enhancing_Facial_Privacy_Protection_via_Weakening_Diffusion_Purification_CVPR_2025_paper.html

abstract:
The rapid growth of social media has led to the widespread sharing of individual portrait images, which pose serious privacy risks due to the capabilities of automatic face recognition (AFR) systems for mass surveillance. Hence, protecting facial privacy against unauthorized AFR systems is essential. Inspired by the generation capability of the emerging diffusion models, recent methods employ diffusion models to generate adversarial face images for privacy protection. However, they suffer from the diffusion purification effect, leading to a low protection success rate (PSR). In this paper, we first propose learning unconditional embeddings to increase the learning capacity for adversarial modifications and then use them to guide the modification of the adversarial latent code to weaken the diffusion purification effect. Moreover, we integrate an identity-preserving structure to maintain structural consistency between the original and generated images, allowing human observers to recognize the generated image as having the same identity as the original. Extensive experiments conducted on two public datasets, i.e., CelebA-HQ and LADN, demonstrate the superiority of our approach. The protected faces generated by our method outperform those produced by existing facial privacy protection approaches in terms of transferability and natural appearance. The code is available at https://github.com/parham1998/Facial-Privacy-Protection

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Salar_Enhancing_Facial_Privacy_Protection_via_Weakening_Diffusion_Purification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Salar_Enhancing_Facial_Privacy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Salar_2025_CVPR,
    author    = {Salar, Ali and Liu, Qing and Tian, Yingli and Zhao, Guoying},
    title     = {Enhancing Facial Privacy Protection via Weakening Diffusion Purification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8235-8244}
}

raw_affiliation:
[{"affiliation": "Center for Machine Vision and Signal Analysis (CMVS), University of Oulu", "city": "Oulu", "country": "Finland"}, {"affiliation": "City University of New York (CUNY)", "city": "New York", "country": "USA"}]

affiliations:
["Center for Machine Vision and Signal Analysis (CMVS), University of Oulu", "City University of New York (CUNY)"]


title: ORIDa: Object-centric Real-world Image Composition Dataset
authors: Jinwoo Kim, Sangmin Han, Jinho Jeong, Jiwoo Choi, Dongyeoung Kim, Seon Joo Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3051-3060
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/Kim_ORIDa_Object-centric_Real-world_Image_Composition_Dataset_CVPR_2025_paper.html

abstract:
Object compositing, the task of placing and harmonizing objects in images of diverse visual scenes, has become an important task in computer vision with the rise of generative models.However, existing datasets lack the diversity and scale required to comprehensively explore real-world scenarios comprehensively. We introduce ORIDa (Object-centric Real-world Image Composition Dataset), a large-scale, real-captured dataset containing over 30,000 images featuring 200 unique objects, each of which is presented across varied positions and scenes. ORIDa has two types of data: factual-counterfactual sets and factual-only scenes. The factual-counterfactual sets consist of four factual images showing an object in different positions within a scene and a single counterfactual (or background) image of the scene without the object, resulting in five images per scene. The factual-only scenes include a single image containing an object in a specific context, expanding the variety of environments. To our knowledge, ORIDa is the first publicly available dataset with its scale and complexity for real-world image composition. Extensive analysis and experiments highlight the value of ORIDa as a resource for advancing further research in object compositing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ORIDa_Object-centric_Real-world_Image_Composition_Dataset_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_ORIDa_Object-centric_Real-world_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jinwoo and Han, Sangmin and Jeong, Jinho and Choi, Jiwoo and Kim, Dongyeoung and Kim, Seon Joo},
    title     = {ORIDa: Object-centric Real-world Image Composition Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3051-3060}
}


title: MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing
authors: Cong Wang, Di Kang, Heyi Sun, Shenhan Qian, Zixuan Wang, Linchao Bao, Song-Hai Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26274-26284
affiliations: ["Tsinghua University", "Tencent", "Technical University of Munich", "Carnegie Mellon University"]
cities: ["Beijing", "Shenzhen", "Munich", "Pittsburgh"]
countries: ["China", "China", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MeGA_Hybrid_Mesh-Gaussian_Head_Avatar_for_High-Fidelity_Rendering_and_Head_CVPR_2025_paper.html

abstract:
Creating high-fidelity head avatars from multi-view videos is essential for many AR/VR applications. However, current methods often struggle to achieve high-quality renderings across all head components (e.g., skin vs. hair) due to the limitations of using one single representation for elements with varying characteristics. In this paper, we introduce a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations. Specifically, we employ an enhanced FLAME mesh for the facial representation and predict a UV displacement map to provide per-vertex offsets for improved personalized geometric details. To achieve photorealistic rendering, we use deferred neural rendering to obtain facial colors and decompose neural textures into three meaningful parts. For hair modeling, we first build a static canonical hair using 3D Gaussian Splatting. A rigid transformation and an MLP-based deformation field are further applied to handle complex dynamic expressions. Combined with our occlusion-aware blending, MeGA generates higher-fidelity renderings for the whole head and naturally supports diverse downstream tasks. Experiments on the NeRSemble dataset validate the effectiveness of our designs, outperforming previous state-of-the-art methods and enabling  versatile editing capabilities, including hairstyle alteration and texture editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MeGA_Hybrid_Mesh-Gaussian_Head_Avatar_for_High-Fidelity_Rendering_and_Head_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MeGA_Hybrid_Mesh-Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Cong and Kang, Di and Sun, Heyi and Qian, Shenhan and Wang, Zixuan and Bao, Linchao and Zhang, Song-Hai},
    title     = {MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26274-26284}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: Image Generation Diversity Issues and How to Tame Them
authors: Mischa Dombrowski, Weitong Zhang, Sarah Cechnicka, Hadrien Reynaud, Bernhard Kainz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3029-3039
cities: ["Erlangen", "London"]
countries: ["Germany", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dombrowski_Image_Generation_Diversity_Issues_and_How_to_Tame_Them_CVPR_2025_paper.html

abstract:
Generative methods have reached a level of quality that is almost indistinguishable from real data. However, while individual samples may appear unique, generative models often exhibit limitations in covering the full data distribution. Unlike quality issues, diversity problems within generative models are not easily detected by simply observing single images or generated datasets, which means we need a specific measure to assess the diversity of these models. In this paper, we draw attention to the current lack of diversity in generative models and the inability of common metrics to measure this.  We achieve this by framing diversity as an image retrieval problem, where we measure how many real images can be retrieved using synthetic data as queries. This yields the Image Retrieval Score (IRS), an interpretable, hyperparameter-free metric that quantifies the diversity of a generative model's output. IRS requires only a subset of synthetic samples and provides a statistical measure of confidence. Our experiments indicate that current feature extractors commonly used in generative model assessment are inadequate for evaluating diversity effectively. Consequently, we perform an extensive search for the best feature extractors to assess diversity. Evaluation reveals that current diffusion models converge to limited subsets of the real distribution, with no current state-of-the-art models superpassing 77% of the diversity of the training data. To address this limitation, we introduce Diversity-Aware Diffusion Models (DiADM), a novel approach that improves diversity of unconditional diffusion models without loss of image quality. We do this by disentangling diversity from image quality by using a diversity aware module that uses pseudo-unconditional features as input. We provide a Python package offering unified feature extraction and metric computation to further facilitate the evaluation of generative models https://github.com/MischaD/beyondfid.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dombrowski_Image_Generation_Diversity_Issues_and_How_to_Tame_Them_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dombrowski_Image_Generation_Diversity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dombrowski_2025_CVPR,
    author    = {Dombrowski, Mischa and Zhang, Weitong and Cechnicka, Sarah and Reynaud, Hadrien and Kainz, Bernhard},
    title     = {Image Generation Diversity Issues and How to Tame Them},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3029-3039}
}

raw_affiliation:
[{"affiliation": "Friedrich\u2013Alexander\u2013Universit\u00a8at Erlangen\u2013N\u00a8urnberg", "city": "Erlangen", "country": "Germany"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}]

affiliations:
["Friedrich\u2013Alexander\u2013Universit\u00a8at Erlangen\u2013N\u00a8urnberg", "Imperial College London"]


title: Annotation Ambiguity Aware Semi-Supervised Medical Image Segmentation
authors: Suruchi Kumari, Pravendra Singh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10404-10413
raw_affiliation: [{"affiliation": "Indian Institute of Technology Roorkee", "city": "Roorkee", "country": "India"}]
affiliations: ["Indian Institute of Technology Roorkee"]
cities: ["Roorkee"]
countries: ["India"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kumari_Annotation_Ambiguity_Aware_Semi-Supervised_Medical_Image_Segmentation_CVPR_2025_paper.html

abstract:
Despite the remarkable progress of deep learning-based methods in medical image segmentation, their use in clinical practice remains limited for two main reasons. First, obtaining a large medical dataset with precise annotations to train segmentation models is challenging. Secondly, most current segmentation techniques generate a single deterministic segmentation mask for each image. However, in real-world scenarios, there is often significant uncertainty regarding what defines the "correct" segmentation, and various expert annotators might provide different segmentations for the same image. To tackle both of these problems, we propose Annotation Ambiguity Aware Semi-Supervised Medical Image Segmentation (AmbiSSL). AmbiSSL combines a small amount of multi-annotator labeled data and a large set of unlabeled data to generate diverse and plausible segmentation maps. Our method consists of three key components: (1) The Diverse Pseudo-Label Generation (DPG) module utilizes multiple decoders, created by performing randomized pruning on the original backbone decoder. These pruned decoders enable the generation of a diverse pseudo-label set; (2) a Semi-Supervised Latent Distribution Learning (SSLDL) module constructs a common latent space by utilizing both ground truth annotations and pseudo-label set; and (3) a Cross-Decoder Supervision (CDS) module, which enables pruned decoders to guide each other's learning. We evaluated the proposed method on two publicly available datasets. Extensive experiments demonstrate that AmbiSSL can generate diverse segmentation maps using only a small amount of labeled data and abundant unlabeled data, offering a more practical solution for medical image segmentation by reducing reliance on large labeled datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumari_Annotation_Ambiguity_Aware_Semi-Supervised_Medical_Image_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumari_Annotation_Ambiguity_Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kumari_2025_CVPR,
    author    = {Kumari, Suruchi and Singh, Pravendra},
    title     = {Annotation Ambiguity Aware Semi-Supervised Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10404-10413}
}


authors: Yi Liu, Wengen Li, Jihong Guan, Shuigeng Zhou, Yichao Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17851-17861
affiliations: ["Tongji University", "Fudan University"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Effective_Cloud_Removal_for_Remote_Sensing_Images_by_an_Improved_CVPR_2025_paper.html

title:
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space

abstract:
Cloud removal (CR) remains a challenging task in remote sensing image processing. Although diffusion models (DM) exhibit strong generative capabilities, their direct applications to CR are suboptimal, as they generate cloudless images from random noise, ignoring inherent information in cloudy inputs. To overcome this drawback, we develop a new CR model EMRDM based on mean-reverting diffusion models (MRDMs) to establish a direct diffusion process between cloudy and cloudless images. Compared to current MRDMs, EMRDM offers a modular framework with updatable modules and an elucidated design space, based on a reformulated forward process and a new ordinary differential equation (ODE)-based backward process. Leveraging our framework, we redesign key MRDM modules to boost CR performance, including restructuring the denoiser via a preconditioning technique, reorganizing the training process, and improving the sampling process by introducing deterministic and stochastic samplers. To achieve multi-temporal CR, we further develop a denoising network for simultaneously denoising sequential images. Experiments on mono-temporal and multi-temporal datasets demonstrate the superior performance of EMRDM. Our code is available at https://github.com/Ly403/EMRDM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Effective_Cloud_Removal_for_Remote_Sensing_Images_by_an_Improved_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Effective_Cloud_Removal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yi and Li, Wengen and Guan, Jihong and Zhou, Shuigeng and Zhang, Yichao},
    title     = {Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17851-17861}
}

raw_affiliation:
[{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models
authors: Felix Taubner, Ruihang Zhang, Mathieu Tuli, David B. Lindell
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5318-5330
affiliations: ["University of Toronto", "Vector Institute", "LG Electronics"]
cities: ["Toronto", "Toronto", "Seoul"]
countries: ["Canada", "Canada", "South Korea"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Taubner_CAP4D_Creating_Animatable_4D_Portrait_Avatars_with_Morphable_Multi-View_Diffusion_CVPR_2025_paper.html

abstract:
Reconstructing photorealistic and dynamic portrait avatars from images is essential to many applications including advertising, visual effects, and virtual reality. Depending on the application, avatar reconstruction involves different capture setups and constraints -- for example, visual effects studios use camera arrays to capture hundreds of reference images, while content creators may seek to animate a single portrait image downloaded from the internet. As such, there is a large and heterogeneous ecosystem of methods for avatar reconstruction. Techniques based on multi-view stereo or neural rendering achieve the highest quality results, but require hundreds of reference images. Recent generative models produce convincing avatars from a single reference image, but visual fidelity lags behind multi-view techniques. Here, we present CAP4D: an approach that uses a morphable multi-view diffusion model to reconstruct photoreal 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time. Our approach demonstrates state-of-the-art performance for single-, few-, and multi-image 4D portrait avatar reconstruction, and takes steps to bridge the gap in visual fidelity between single-image and multi-view reconstruction techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Taubner_CAP4D_Creating_Animatable_4D_Portrait_Avatars_with_Morphable_Multi-View_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Taubner_CAP4D_Creating_Animatable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Taubner_2025_CVPR,
    author    = {Taubner, Felix and Zhang, Ruihang and Tuli, Mathieu and Lindell, David B.},
    title     = {CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5318-5330}
}

raw_affiliation:
[{"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "LG Electronics", "city": "Seoul", "country": "South Korea"}]


authors: Jung-Ho Hong, Ho-Joong Kim, Kyu-Sung Jeon, Seong-Whan Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25166-25175
affiliations: ["Dept. of Artificial Intelligence, Korea University"]
cities: ["Seoul"]
countries: ["Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_Comprehensive_Information_Bottleneck_for_Unveiling_Universal_Attribution_to_Interpret_Vision_CVPR_2025_paper.html

title:
Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers

abstract:
The feature attribution method reveals the contribution of input variables to the decision-making process to provide an attribution map for explanation. Existing methods grounded on the information bottleneck principle compute information in a specific layer to obtain attributions, compressing the features by injecting noise via a parametric damping ratio. However, the attribution obtained in a specific layer neglects evidence of the decision-making process distributed across layers. In this paper, we introduce a comprehensive information bottleneck (CoIBA), which discovers the relevant information in each targeted layer to explain the decision-making process. Our core idea is applying information bottleneck in multiple targeted layers to estimate the comprehensive information by sharing a parametric damping ratio across the layers. Leveraging this shared ratio complements the over-compressed information to discover the omitted clues of the decision by sharing the relevant information across the targeted layers. We suggest the variational approach to fairly reflect the relevant information of each layer by upper bounding layer-wise information. Therefore, CoIBA guarantees that the discarded activation is unnecessary in every targeted layer to make a decision. The extensive experimental results demonstrate the enhancement in faithfulness of the feature attributions provided by CoIBA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_Comprehensive_Information_Bottleneck_for_Unveiling_Universal_Attribution_to_Interpret_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_Comprehensive_Information_Bottleneck_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Jung-Ho and Kim, Ho-Joong and Jeon, Kyu-Sung and Lee, Seong-Whan},
    title     = {Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25166-25175}
}

raw_affiliation:
[{"affiliation": "Dept. of Artificial Intelligence, Korea University", "city": "Seoul", "country": "Korea"}]


title: OpticalNet: An Optical Imaging Dataset and Benchmark Beyond the Diffraction Limit
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10900-10912
cities: ["Singapore", "Singapore", "Southampton", "Austin"]
countries: ["Singapore", "Singapore", "United Kingdom", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OpticalNet_An_Optical_Imaging_Dataset_and_Benchmark_Beyond_the_Diffraction_CVPR_2025_paper.html

authors:
Benquan Wang, Ruyi An, Jin-Kyu So, Sergei Kurdiumov, Eng Aik Chan, Giorgio Adamo, Yuhan Peng, Yewen Li, Bo An

abstract:
Optical imaging capable of resolving nanoscale features would revolutionize scientific research and engineering applications across biomedicine, smart manufacturing, and semiconductor quality control. However, due to the physical phenomenon of diffraction, the optical resolution is limited to approximately half the wavelength of light, which impedes the observation of subwavelength objects such as the native state coronavirus, typically smaller than 200 nm. Fortunately, deep learning methods have shown remarkable potential in uncovering underlying patterns within data, promising to overcome the diffraction limit by revealing the mapping pattern between diffraction images and their corresponding ground truth object images. However, the absence of suitable datasets has hindered progress in this field--collecting high-quality optical data of subwavelength objects is highly difficult as these objects are inherently invisible under conventional microscopy, making it impossible to perform standard visual calibration and drift correction. Therefore, we provide the first general optical imaging dataset based on the "building block" concept for challenging the diffraction limit. Drawing an analogy to modular construction principles, we construct a comprehensive optical imaging dataset comprising subwavelength fundamental elements, i.e., small square units that can be assembled into larger and more complex objects. We then frame the task as an image-to-image translation task and evaluate various vision methods. Experimental results validate our "building block" concept, demonstrating that models trained on basic square units can effectively generalize to realistic, more complex unseen objects. Most importantly, by highlighting this underexplored AI-for-science area and its potential, we aspire to advance optical science by fostering collaboration with the vision and machine learning communities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OpticalNet_An_Optical_Imaging_Dataset_and_Benchmark_Beyond_the_Diffraction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OpticalNet_An_Optical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Benquan and An, Ruyi and So, Jin-Kyu and Kurdiumov, Sergei and Chan, Eng Aik and Adamo, Giorgio and Peng, Yuhan and Li, Yewen and An, Bo},
    title     = {OpticalNet: An Optical Imaging Dataset and Benchmark Beyond the Diffraction Limit},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10900-10912}
}

raw_affiliation:
[{"affiliation": "Nanyang T echnological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Skywork AI", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Southampton", "city": "Southampton", "country": "United Kingdom"}, {"affiliation": "The University of T exas at Austin", "city": "Austin", "country": "USA"}]

affiliations:
["Nanyang T echnological University", "Skywork AI", "University of Southampton", "The University of T exas at Austin"]


title: Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
authors: Shaobo Wang, Yicun Yang, Zhiyuan Liu, Chenghao Sun, Xuming Hu, Conghui He, Linfeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25570-25580
affiliations: ["SAI, SJTU", "EPIC Lab, SJTU", "HKUST", "Shanghai AI Laboratory"]
cities: ["Shanghai", "Shanghai", "Guangzhou", "Shanghai"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Dataset_Distillation_with_Neural_Characteristic_Function_A_Minmax_Perspective_CVPR_2025_paper.html

abstract:
Dataset distillation has emerged as a powerful approach for reducing data requirements in deep learning. Among various methods, distribution matching-based approaches stand out for their balance of computational efficiency and strong performance. However, existing distance metrics used in distribution matching often fail to accurately capture distributional differences, leading to unreliable measures of discrepancy. In this paper, we reformulate dataset distillation as a minmax optimization problem and introduce Neural Characteristic Function Discrepancy (NCFD), a comprehensive and theoretically grounded metric for measuring distributional differences. NCFD leverages the Characteristic Function (CF) to encapsulate full distributional information, employing a neural network to optimize the sampling strategy for the CF's frequency arguments, thereby maximizing the discrepancy to enhance distance estimation. Simultaneously, we minimize the difference between real and synthetic data under this optimized NCFD measure. Our approach, termed Neural Characteristic Function Matching (NCFM), inherently aligns the phase and amplitude of neural features in the complex plane for both real and synthetic data, achieving a balance between realism and diversity in synthetic samples. Experiments demonstrate that our method achieves significant performance gains over state-of-the-art methods on both low- and high-resolution datasets. Notably, we achieve a 22.9% accuracy boost on ImageSquawk. Our method also reduces GPU memory usage by over 300x and achieves 20x faster processing speeds compared to state-of-the-art methods. To the best of our knowledge, this is the first work to achieve lossless compression of CIFAR-100 on a single NVIDIA 2080 Ti GPU using only 2.3 GB of memory. The code for this work is publicly available at: https://github.com/gszfwsb/NCFM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Dataset_Distillation_with_Neural_Characteristic_Function_A_Minmax_Perspective_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Dataset_Distillation_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shaobo and Yang, Yicun and Liu, Zhiyuan and Sun, Chenghao and Hu, Xuming and He, Conghui and Zhang, Linfeng},
    title     = {Dataset Distillation with Neural Characteristic Function: A Minmax Perspective},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25570-25580}
}

raw_affiliation:
[{"affiliation": "SAI, SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "EPIC Lab, SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "HKUST", "city": "Guangzhou", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: Free-viewpoint Human Animation with Pose-correlated Reference Selection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26253-26262
affiliations: ["HKUST", "Adobe Research", "Northwestern University"]
cities: ["Hong Kong", "San Jose", "Evanston"]
countries: ["China", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_Free-viewpoint_Human_Animation_with_Pose-correlated_Reference_Selection_CVPR_2025_paper.html

authors:
Fa-Ting Hong, Zhan Xu, Haiyang Liu, Qinjie Lin, Luchuan Song, Zhixin Shu, Yang Zhou, Duygu Ceylan, Dan Xu

abstract:
Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative capacity of diffusion model, existing approaches are able to generate high-fidelity poses, but struggle with significant viewpoint changes, especially in zoom-in/zoom-out scenarios where camera-character distance varies. This limits the applications such as cinematic shot type plan or camera control. We propose a pose-correlated reference selection diffusion network, supporting substantial viewpoint variations in human animation. Our key idea is to enable the network to utilize multiple reference images as input, since significant viewpoint changes often lead to missing appearance details on the human body. To eliminate the computational cost, we first introduce a novel pose correlation module to compute similarities between non-aligned target and source poses, and then propose an adaptive reference selection strategy, utilizing the attention map to identify key regions for animation generation. To train our model, we curated a large dataset from public TED talks featuring varied shots of the same character, helping the model learn synthesis for different perspectives. Our experimental results show that with the same number of reference images, our model performs favorably compared to the current SOTA methods under large viewpoint change. We further show that the adaptive reference selection is able to choose the most relevant reference regions to generate humans under free viewpoints.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_Free-viewpoint_Human_Animation_with_Pose-correlated_Reference_Selection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_Free-viewpoint_Human_Animation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Fa-Ting and Xu, Zhan and Liu, Haiyang and Lin, Qinjie and Song, Luchuan and Shu, Zhixin and Zhou, Yang and Ceylan, Duygu and Xu, Dan},
    title     = {Free-viewpoint Human Animation with Pose-correlated Reference Selection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26253-26262}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}]


title: CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
authors: Yun Liu, Chengwen Zhang, Ruofan Xing, Bingda Tang, Bowen Yang, Li Yi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1769-1782
cities: ["Beijing", "Shanghai", "Shanghai", "Beijing", "Shenyang"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_CORE4D_A_4D_Human-Object-Human_Interaction_Dataset_for_Collaborative_Object_REarrangement_CVPR_2025_paper.html

abstract:
Understanding how humans cooperatively rearrange household objects is critical for VR/AR and human-robot interaction. However, in-depth studies on modeling these behaviors are under-researched due to the lack of relevant datasets. We fill this gap by presenting CORE4D, a novel large-scale 4D human-object-human interaction dataset focusing on collaborative object rearrangement, which encompasses diverse compositions of various object geometries, collaboration modes, and 3D scenes. With 1K human-object-human motion sequences captured in the real world, we enrich CORE4D by contributing an iterative collaboration retargeting strategy to augment motions to a variety of novel objects. Leveraging this approach, CORE4D comprises a total of 11K collaboration sequences spanning 3K real and virtual object shapes. Benefiting from extensive motion patterns provided by CORE4D, we benchmark two tasks aiming at generating human-object interaction: human-object motion forecasting and interaction synthesis. Extensive experiments demonstrate the effectiveness of our collaboration retargeting strategy and indicate that CORE4D has posed new challenges to existing human-object interaction generation methodologies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_CORE4D_A_4D_Human-Object-Human_Interaction_Dataset_for_Collaborative_Object_REarrangement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_CORE4D_A_4D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yun and Zhang, Chengwen and Xing, Ruofan and Tang, Bingda and Yang, Bowen and Yi, Li},
    title     = {CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1769-1782}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}]

affiliations:
["Tsinghua University", "Shanghai Artificial Intelligence Laboratory", "Shanghai Qi Zhi Institute", "Beijing University of Posts and Telecommunications", "Northeastern University"]


title: PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram
authors: Sifan Zhou, Zhihang Yuan, Dawei Yang, Xing Hu, Jian Qian, Ziyu Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27336-27345
affiliations: ["School of Automation, Southeast University", "Houmo AI", "Fudan University"]
cities: ["Nanjing", "Unknown", "Shanghai"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_PillarHist_A_Quantization-aware_Pillar_Feature_Encoder_based_on_Height-aware_Histogram_CVPR_2025_paper.html

abstract:
Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attention due to their compact representation and low computational overhead, making them suitable for onboard deployment and quantization. However, existing pillar-based detectors still suffer from information loss along height dimension and large numerical distribution difference during pillar feature encoding (PFE), which severely limits their performance and quantization potential. To address above issue, we first unveil the importance of different input information during PFE and identify the height dimension as a key factor in enhancing 3D detection performance. Motivated by this observation, we propose a height-aware pillar feature encoder, called PillarHist. Specifically, PillarHist statistics the discrete distribution of points at different heights within one pillar. This simple yet effective design greatly preserves the information along the height dimension while significantly reducing the computation overhead of the PFE. Meanwhile, PillarHist also constrains the arithmetic distribution of PFE input to a stable range, making it quantization-friendly. Notably, PillarHist operates exclusively within the PFE stage to enhance performance, enabling seamless integration into existing pillar-based methods without introducing complex operations. Extensive experiments show the effectiveness of PillarHist in terms of both efficiency and performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_PillarHist_A_Quantization-aware_Pillar_Feature_Encoder_based_on_Height-aware_Histogram_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_PillarHist_A_Quantization-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Sifan and Yuan, Zhihang and Yang, Dawei and Hu, Xing and Qian, Jian and Zhao, Ziyu},
    title     = {PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27336-27345}
}

raw_affiliation:
[{"affiliation": "School of Automation, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Houmo AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3646-3655
countries: ["USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wilson_POp-GS_Next_Best_View_in_3D-Gaussian_Splatting_with_P-Optimality_CVPR_2025_paper.html

authors:
Joey Wilson, Marcelino Almeida, Sachit Mahajan, Martin Labrie, Maani Ghaffari, Omid Ghasemalizadeh, Min Sun, Cheng-Hao Kuo, Arnab Sen

abstract:
In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty or information, posing a challenge for real-world applications such as 3D-GS SLAM. We propose to quantify information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design, which is a classical solution widely used in literature. By restructuring information quantification of 3D-GS through optimal experimental design, we arrive at multiple solutions, of which T-Optimality and D-Optimality perform the best quantitatively and qualitatively as measured on two popular datasets. Additionally, we propose a block diagonal covariance approximation which provides a measure of correlation at the expense of a greater computation cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wilson_POp-GS_Next_Best_View_in_3D-Gaussian_Splatting_with_P-Optimality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wilson_POp-GS_Next_Best_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wilson_2025_CVPR,
    author    = {Wilson, Joey and Almeida, Marcelino and Mahajan, Sachit and Labrie, Martin and Ghaffari, Maani and Ghasemalizadeh, Omid and Sun, Min and Kuo, Cheng-Hao and Sen, Arnab},
    title     = {POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3646-3655}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "Amazon Lab 126", "city": "Sunnyvale", "country": "USA"}]

affiliations:
["University of Michigan", "Amazon Lab 126", "Amazon Lab 126", "Amazon Lab 126", "University of Michigan", "Amazon Lab 126", "Amazon Lab 126", "Amazon Lab 126", "Amazon Lab 126"]

cities:
["Ann Arbor", "Sunnyvale", "Sunnyvale", "Sunnyvale", "Ann Arbor", "Sunnyvale", "Sunnyvale", "Sunnyvale", "Sunnyvale"]


title: Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility
authors: Yidi Li, Jun Xiao, Zhengda Lu, Yiqun Wang, Haiyong Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18531-18540
cities: ["Beijing", "Chongqing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Empowering_Vector_Graphics_with_Consistently_Arbitrary_Viewing_and_View-dependent_Visibility_CVPR_2025_paper.html

abstract:
This work presents a novel text-to-vector graphics generation approach, Dream3DVG, allowing for arbitrary viewpoint viewing, progressive detail optimization, and view-dependent occlusion awareness.  Our approach is a dual-branch optimization framework, consisting of an auxiliary 3D Gaussian Splatting optimization branch and a 3D vector graphics optimization branch.  The introduced 3DGS branch can bridge the domain gaps between text prompts and vector graphics with more consistent guidance. Moreover, 3DGS allows for progressive detail control by scheduling classifier-free guidance, facilitating guiding vector graphics with coarse shapes at the initial stages and finer details at later stages.  We also improve the view-dependent occlusions by devising a visibility-awareness rendering module.  Extensive results on 3D sketches and 3D iconographies, demonstrate the superiority of the method on different abstraction levels of details, cross-view consistency, and occlusion-aware stroke culling.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Empowering_Vector_Graphics_with_Consistently_Arbitrary_Viewing_and_View-dependent_Visibility_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Empowering_Vector_Graphics_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yidi and Xiao, Jun and Lu, Zhengda and Wang, Yiqun and Jiang, Haiyong},
    title     = {Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18531-18540}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, University of Chinese Academy of Sciences", "Chongqing University"]


title: Semantic and Expressive Variations in Image Captions Across Languages
authors: Andre Ye, Sebastin Santy, Jena D. Hwang, Amy X. Zhang, Ranjay Krishna
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29667-29679
affiliations: ["University of Washington", "Allen Institute for AI"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Semantic_and_Expressive_Variations_in_Image_Captions_Across_Languages_CVPR_2025_paper.html

abstract:
Most vision-language models today are primarily trained on English image-text pairs, with non-English pairs often filtered out. Evidence from cross-cultural psychology suggests that this approach will bias models against perceptual modes exhibited by people who speak other (non-English) languages. We investigate semantic and expressive variation in image captions across different languages; we analyze both human-annotated datasets and model-produced captions. By analyzing captions across seven languages (English, French, German, Russian, Chinese, Japanese, Korean) in high-quality image captioning datasets (Crossmodal and Visual Genome), we find that multilingual caption sets tend to provide richer visual descriptions than monolingual (including English-only) ones; multilingual sets contain 46.0% more objects, 66.1% more relationships, and 66.8% more attributes. We observe the same results with multilingual captions produced by LLaVA and the Google Vertex API: for example, compared to monolingual captions, they cover 21.9% more objects,18.8% more relations, and 20.1% more attributes. These suggest that, across a large number of samples, different languages bias people and models to focus on different visual concepts. Finally, we show that models trained on image-text data in one language perform distinctly better on that language's test set. Our work points towards the potential value of training vision models on multilingual data sources to widen the range/variation of descriptive information those models are exposed to.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Semantic_and_Expressive_Variations_in_Image_Captions_Across_Languages_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Semantic_and_Expressive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Andre and Santy, Sebastin and Hwang, Jena D. and Zhang, Amy X. and Krishna, Ranjay},
    title     = {Semantic and Expressive Variations in Image Captions Across Languages},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29667-29679}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}]


title: ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models
authors: Xubing Ye, Yukang Gan, Yixiao Ge, Xiao-Ping Zhang, Yansong Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24972-24982
affiliations: ["Tsinghua Shenzhen International Graduate School, Tsinghua University", "ARC Lab, Tencent PCG"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_ATP-LLaVA_Adaptive_Token_Pruning_for_Large_Vision_Language_Models_CVPR_2025_paper.html

abstract:
Large Vision Language Models (LVLMs) have achieved significant success across multi-modal tasks. However, the computational cost of processing long visual tokens can be prohibitively expensive on resource-limited devices. Previous methods have identified redundancy in visual tokens within the Large Language Model (LLM) decoder layers and have mitigated this by pruning tokens using a pre-defined or fixed ratio, thereby reducing computational overhead. Nonetheless, we observe that the impact of pruning ratio varies across different LLM layers and instances (image-prompt pairs). Therefore, it is essential to develop a layer-wise and instance-wise vision token pruning strategy to balance computational cost and model performance effectively. We propose ATP-LLaVA, a novel approach that adaptively determines instance-specific token pruning ratios for each LLM layer. Specifically, we introduce an Adaptive Token Pruning (ATP) module, which computes the importance score and pruning threshold based on input instance adaptively. The ATP module can be seamlessly integrated between any two LLM layers with negligible computational overhead. Additionally, we develop a Spatial Augmented Pruning (SAP) strategy that prunes visual tokens with both token redundancy and spatial modeling perspectives. Our approach reduces the average token count by 75% while maintaining performance, with only a minimal 1.9% degradation across seven widely used benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_ATP-LLaVA_Adaptive_Token_Pruning_for_Large_Vision_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_ATP-LLaVA_Adaptive_Token_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Xubing and Gan, Yukang and Ge, Yixiao and Zhang, Xiao-Ping and Tang, Yansong},
    title     = {ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24972-24982}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}]


title: ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution
authors: Ze-Yu Mi, Yu-Bin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23101-23110
affiliations: ["State Key Laboratory for Novel Software Technology, Nanjing University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mi_ADD_Attribution-Driven_Data_Augmentation_Framework_for_Boosting_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Data augmentation (DA) stands out as a powerful technique to enhance the generalization capabilities of deep neural networks across diverse tasks. However, in low-level vision tasks, DA remains rudimentary (i.e., vanilla DA), facing a critical bottleneck due to information loss. In this paper, we introduce a novel Calibrated Attribution Maps (CAM) to generate saliency masks, followed by two saliency-based DA methods-- Attribution-Driven Data augmentation (ADD) and ADD+--designed to address this issue. CAM leverages integrated gradients and incorporates two key innovations: a global feature detector and calibrated integrated gradients. Based on CAM and the proposed methods, we have two new insights for low-level vision tasks: (1) increasing pixel diversity, as seen in vanilla DA, can improve performance, and (2) focusing on salient features while minimizing the impact of irrelevant pixels, as seen in saliency-based DA, more effectively enhances model performance. Additionally, we find and highlight the key guiding principle for designing saliency-based DA: a wider spectrum of degradation patterns. Extensive experiments demonstrate the compatibility and consistency of our method, as well as the significant performance improvement across various SR tasks and networks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mi_ADD_Attribution-Driven_Data_Augmentation_Framework_for_Boosting_Image_Super-Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mi_ADD_Attribution-Driven_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mi_2025_CVPR,
    author    = {Mi, Ze-Yu and Yang, Yu-Bin},
    title     = {ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23101-23110}
}

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


title: DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID
authors: Xin Liang, Yogesh S Rawat
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13980-13989
affiliations: ["Center for Research in Computer Vision, University of Central Florida"]
cities: ["Orlando"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_DIFFER_Disentangling_Identity_Features_via_Semantic_Cues_for_Clothes-Changing_Person_CVPR_2025_paper.html

abstract:
Clothes-changing person re-identification (CC-ReID) aims to recognize individuals under different clothing scenarios. Current CC-ReID approaches either concentrate on modeling body shape using additional modalities including silhouette, pose, and body mesh, potentially causing the model to overlook other critical biometric traits such as gender, age, and style, or they incorporate supervision through additional labels that the model tries to disregard or emphasize, such as clothing or personal attributes. However, these annotations are discrete in nature and do not capture comprehensive descriptions. In this work, we propose DIFFER: Disentangle Identity Features From Entangled Representations, a novel adversarial learning method that leverages textual descriptions to disentangle identity features. Recognizing that image features inherently mix inseparable information, DIFFER introduces NBDetach, a mechanism designed for feature disentanglement by leveraging the separable nature of text descriptions as supervision. It partitions the feature space into distinct subspaces and, through gradient reversal layers, effectively separates identity-related features from non-biometric features. We evaluate DIFFER on 4 different benchmark datasets (LTCC, PRCC, CelebreID-Light, and CCVID) to demonstrate its effectiveness and provide state-of-the-art performance across all the benchmarks. DIFFER consistently outperforms the baseline method, with improvements in top-1 accuracy of 3.6% on LTCC, 3.4% on PRCC, 2.5% on CelebReID-Light, and 1% on CCVID. Our code can be found at https://github.com/xliangp/DIFFER.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_DIFFER_Disentangling_Identity_Features_via_Semantic_Cues_for_Clothes-Changing_Person_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_DIFFER_Disentangling_Identity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Xin and Rawat, Yogesh S},
    title     = {DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13980-13989}
}

raw_affiliation:
[{"affiliation": "Center for Research in Computer Vision, University of Central Florida", "city": "Orlando", "country": "USA"}]


authors: Ron Ferens, Yosi Keller
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11547-11557
affiliations: ["Faculty of Engineering, Bar Ilan University"]
cities: ["Ramat-Gan"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ferens_HyperPose_Hypernetwork-Infused_Camera_Pose_Localization_and_an_Extended_Cambridge_Landmarks_CVPR_2025_paper.html

title:
HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset

abstract:
In this work, we propose HyperPose, which utilizes hypernetworks in absolute camera pose regressors. The inherent appearance variations in natural scenes, attributable to environmental conditions, perspective, and lighting, induce a significant domain disparity between the training and test datasets. This disparity degrades the precision of contemporary localization networks. To mitigate this, we advocate for incorporating hypernetworks into single-scene and multiscene camera pose regression models. During inference, the hypernetwork dynamically computes adaptive weights for the localization regression heads based on the particular input image, effectively narrowing the domain gap. Using indoor and outdoor datasets, we evaluate the HyperPose methodology across multiple established absolute pose regression architectures. In particular, we introduce and share the Extended Cambridge Landmarks (ECL), which is a novel localization dataset, based on the Cambridge Landmarks dataset, showing it in multiple seasons with significantly varying appearance conditions. Our empirical experiments demonstrate that HyperPose yields notable performance enhancements for both single- and multi-scene architectures. We have made our source code, pre-trained models, and ECL dataset openly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ferens_HyperPose_Hypernetwork-Infused_Camera_Pose_Localization_and_an_Extended_Cambridge_Landmarks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ferens_HyperPose_Hypernetwork-Infused_Camera_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ferens_2025_CVPR,
    author    = {Ferens, Ron and Keller, Yosi},
    title     = {HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11547-11557}
}

raw_affiliation:
[{"affiliation": "Faculty of Engineering, Bar Ilan University", "city": "Ramat-Gan", "country": "Israel"}]


title: Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9050-9061
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Critic-V_VLM_Critics_Help_Catch_VLM_Errors_in_Multimodal_Reasoning_CVPR_2025_paper.html

authors:
Di Zhang, Jingdi Lei, Junxian Li, Xunzhi Wang, Yujie Liu, Zonglin Yang, Jiatong Li, Weida Wang, Suorong Yang, Jianbo Wu, Peng Ye, Wanli Ouyang, Dongzhan Zhou

abstract:
Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner's capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward (RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Critic-V_VLM_Critics_Help_Catch_VLM_Errors_in_Multimodal_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Critic-V_VLM_Critics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Di and Lei, Jingdi and Li, Junxian and Wang, Xunzhi and Liu, Yujie and Yang, Zonglin and Li, Jiatong and Wang, Weida and Yang, Suorong and Wu, Jianbo and Ye, Peng and Ouyang, Wanli and Zhou, Dongzhan},
    title     = {Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9050-9061}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}, {"affiliation": "Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Artificial Intelligence Laboratory", "Shanghai Jiaotong University", "Nankai University", "Shanghai University", "Nanyang Technological University", "Hong Kong Polytechnic University", "Tongji University", "Nanjing University", "University of California, Merced", "Chinese University of Hong Kong"]

cities:
["Shanghai", "Shanghai", "Shanghai", "Tianjin", "Shanghai", "Singapore", "Hong Kong", "Shanghai", "Nanjing", "Merced", "Hong Kong"]

countries:
["China", "China", "China", "China", "China", "Singapore", "China", "China", "China", "USA", "China"]


title: Mono3DVLT: Monocular-Video-Based 3D Visual Language Tracking
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13886-13896
cities: ["Xi'an", "Xi'an", "Melbourne", "Perth"]
countries: ["China", "China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_Mono3DVLT_Monocular-Video-Based_3D_Visual_Language_Tracking_CVPR_2025_paper.html

authors:
Hongkai Wei, Yang Yang, Shijie Sun, Mingtao Feng, Xiangyu Song, Qi Lei, Hongli Hu, Rong Wang, Huansheng Song, Naveed Akhtar, Ajmal Saeed Mian

abstract:
Visual-Language Tracking (VLT) is emerging as a promising paradigm to bridge the human-machine performance gap. For single objects, VLT broadens the problem scope to text-driven video comprehension. Yet, this direction is still confined to 2D spatial extents, currently lacking the ability to deal with 3D tracking in the confines of monocular video. Unfortunately, advances in 3D tracking mainly rely on expensive sensor inputs, e.g., point clouds, depth measurements, radar. Absence of language counterpart for the outputs of these mildly democratized sensors in the literature also hinders VLT expansion to 3D tracking. Addressing that, we make the first attempt towards extending VLT to 3D tracking based on monocular video. We present a comprehensive framework, introducing (i) the Monocular-Video-based 3D Visual Language Tracking (Mono3DVLT) task, (ii) a large-scale dataset  for the task, called Mono3DVLT-V2X, and (iii) a customized neural model for the task. Our dataset is carefully curated, leveraging a Large Langauge Model (LLM) followed by human verification, composing natural language descriptions for 79,158 video sequences aiming at single object tracking, providing 2D and 3D bounding box annotations. Our neural model, termed Mono3DVLT-MT, is the first targeted  approach for the Mono3DVLT task. Comprising the pipeline of multi-modal feature extractor, visual-language encoder, tracking decoder and a tracking head, our model sets a strong baseline for the task on Mono3DVLT-V2X. Experimental results show that our method significantly outperforms existing techniques on the Mono3DVLT-V2X dataset. Our dataset and code are available in https://github.com/hongkai-wei/Mono3DVLT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_Mono3DVLT_Monocular-Video-Based_3D_Visual_Language_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Hongkai and Yang, Yang and Sun, Shijie and Feng, Mingtao and Song, Xiangyu and Lei, Qi and Hu, Hongli and Wang, Rong and Song, Huansheng and Akhtar, Naveed and Mian, Ajmal Saeed},
    title     = {Mono3DVLT: Monocular-Video-Based 3D Visual Language Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13886-13896}
}

raw_affiliation:
[{"affiliation": "Chang\u2019an University", "city": "Xi'an", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "University of Western Australia", "city": "Perth", "country": "Australia"}]

affiliations:
["Chang\u2019an University", "Xidian University", "The University of Melbourne", "University of Western Australia"]


title: Towards Universal Dataset Distillation via Task-Driven Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10557-10566
cities: ["Shanghai", "Shenzhen", "Hangzhou", "Shanghai", "Nanjing"]
countries: ["China", "China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Towards_Universal_Dataset_Distillation_via_Task-Driven_Diffusion_CVPR_2025_paper.html

authors:
Ding Qi, Jian Li, Junyao Gao, Shuguang Dou, Ying Tai, Jianlong Hu, Bo Zhao, Yabiao Wang, Chengjie Wang, Cairong Zhao

abstract:
Dataset distillation (DD) condenses key information from large-scale datasets into smaller synthetic datasets, reducing storage and computational costs for training networks. However, recent research has primarily focused on image classification tasks, with limited expansion to detection and segmentation. Two key challenges remain: (i) Task Optimization Heterogeneity, where existing methods focus on class-level information and fail to address the diverse needs of detection and segmentation and (ii) Inflexible Image Generation, where current generation methods rely on global updates for single-class targets and lack localized optimization for specific object regions.To address these challenges, we propose a universal dataset distillation framework, named UniDD, a task-driven diffusion model for diverse DD tasks, as illustrated in Fig.1. Our approach operates in two stages: Universal Task Knowledge Mining, which captures task-relevant information through task-specific proxy model training, and Universal Task-Driven Diffusion, where these proxies guide the diffusion process to generate task-specific synthetic images.Extensive experiments across ImageNet-1K, Pascal VOC, and MS COCO demonstrate that UniDD consistently outperforms state-of-the-art methods. In particular, on ImageNet-1K with IPC-10, UniDD surpasses previous diffusion-based methods by 6.1%, while also reducing deployment costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_Towards_Universal_Dataset_Distillation_via_Task-Driven_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_Towards_Universal_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Ding and Li, Jian and Gao, Junyao and Dou, Shuguang and Tai, Ying and Hu, Jianlong and Zhao, Bo and Wang, Yabiao and Wang, Chengjie and Zhao, Cairong},
    title     = {Towards Universal Dataset Distillation via Task-Driven Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10557-10566}
}

raw_affiliation:
[{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent YouTu Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["Tongji University", "Tencent YouTu Lab", "Zhejiang University", "Shanghai Jiao Tong University", "Nanjing University"]


title: Parametric Point Cloud Completion for Polygonal Surface Reconstruction
authors: Zhaiyu Chen, Yuqing Wang, Liangliang Nan, Xiao Xiang Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11749-11758
cities: ["Munich", "Delft", "Munich"]
countries: ["Germany", "Netherlands", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Parametric_Point_Cloud_Completion_for_Polygonal_Surface_Reconstruction_CVPR_2025_paper.html

abstract:
Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked. To address this gap, we introduce parametric completion, a novel paradigm for point cloud completion, which recovers parametric primitives instead of individual points to convey high-level geometric structures. Our presented approach, PaCo, enables high-quality polygonal surface reconstruction by leveraging plane proxies that encapsulate both plane parameters and inlier points, proving particularly effective in challenging scenarios with highly incomplete data. Comprehensive evaluations of our approach on the ABC dataset establish its effectiveness with superior performance and set a new standard for polygonal surface reconstruction from incomplete data. Project page: https://parametric-completion.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Parametric_Point_Cloud_Completion_for_Polygonal_Surface_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Parametric_Point_Cloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhaiyu and Wang, Yuqing and Nan, Liangliang and Zhu, Xiao Xiang},
    title     = {Parametric Point Cloud Completion for Polygonal Surface Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11749-11758}
}

raw_affiliation:
[{"affiliation": "T echnical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Delft University of T echnology", "city": "Delft", "country": "Netherlands"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}]

affiliations:
["T echnical University of Munich", "Delft University of T echnology", "Munich Center for Machine Learning"]


title: SyncSDE: A Probabilistic Framework for Diffusion Synchronization
authors: Hyunjun Lee, Hyunsoo Lee, Sookwan Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17508-17517
affiliations: ["ECE, Seoul National University", "Republic of Korea Air Force"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_SyncSDE_A_Probabilistic_Framework_for_Diffusion_Synchronization_CVPR_2025_paper.html

abstract:
There have been many attempts to leverage multiple diffusion models for collaborative generation, extending beyond the original domain. A prominent approach involves synchronizing multiple diffusion trajectories by mixing the estimated scores to artificially correlate the generation processes. However, existing methods rely on naive heuristics, such as averaging, without considering task specificity. These approaches do not clarify why such methods work and often fail when a heuristic suitable for one task is blindly applied to others. In this paper, we present a probabilistic framework for analyzing why diffusion synchronization works and reveal where heuristics should be focused--modeling correlations between multiple trajectories and adapting them to each specific task. We further identify optimal correlation models per task, achieving better results than previous approaches that apply a single heuristic across all tasks without justification.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_SyncSDE_A_Probabilistic_Framework_for_Diffusion_Synchronization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_SyncSDE_A_Probabilistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Hyunjun and Lee, Hyunsoo and Han, Sookwan},
    title     = {SyncSDE: A Probabilistic Framework for Diffusion Synchronization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17508-17517}
}

raw_affiliation:
[{"affiliation": "ECE, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Republic of Korea Air Force", "city": "Seoul", "country": "South Korea"}]


title: MaRI: Material Retrieval Integration across Domains
authors: Jianhui Wang, Zhifei Yang, Yangfan He, Huixiong Zhang, Yuxuan Chen, Jingwei Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5814-5823
cities: ["Chengdu", "Beijing", "Minneapolis", "Shanghai", "Shenzhen"]
countries: ["China", "China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MaRI_Material_Retrieval_Integration_across_Domains_CVPR_2025_paper.html

abstract:
Accurate material retrieval is critical for creating realistic 3D assets. Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials, which are scarce and face challenges due to limited diversity and inadequate real-world generalization. Most current approaches adopt traditional image search techniques. They fall short in capturing the unique properties of material spaces, leading to suboptimal performance in retrieval tasks. Addressing these challenges, we introduce MaRI, a framework designed to bridge the feature space gap between synthetic and real-world materials. MaRI constructs a shared embedding space that harmonizes visual and material attributes through a contrastive learning strategy by jointly training a image and a material encoder, bringing similar materials and images closer while separating dissimilar pairs within the feature space. To support this, we construct a comprehensive dataset comprising high-quality synthetic materials rendered with controlled shape variations and diverse lighting conditions, along with real-world materials processed and standardized using material transfer techniques. Extensive experiments demonstrate the superior performance, accuracy, and generalization capabilities of MaRI across diverse and complex material retrieval tasks, outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MaRI_Material_Retrieval_Integration_across_Domains_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MaRI_Material_Retrieval_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jianhui and Yang, Zhifei and He, Yangfan and Zhang, Huixiong and Chen, Yuxuan and Huang, Jingwei},
    title     = {MaRI: Material Retrieval Integration across Domains},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5814-5823}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Minnesota-Twin Cities", "city": "Minneapolis", "country": "USA"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent Hunyuan3D", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Electronic Science and Technology of China", "Peking University", "University of Minnesota-Twin Cities", "Fudan University", "Tencent Hunyuan3D"]


title: MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13263-13272
cities: ["Shanghai", "Shanghai", "Suzhou", "Changchun", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MCCD_Multi-Agent_Collaboration-based_Compositional_Diffusion_for_Complex_Text-to-Image_Generation_CVPR_2025_paper.html

authors:
Mingcheng Li, Xiaolu Hou, Ziyang Liu, Dingkang Yang, Ziyun Qian, Jiawei Chen, Jinjie Wei, Yue Jiang, Qingyao Xu, Lihua Zhang

abstract:
Diffusion models have shown excellent performance in text-to-image generation. However, existing methods often suffer from performance bottlenecks when dealing with complex prompts involving multiple objects, characteristics, and relations.  Therefore, we propose a Multi-agent Collaboration-based Compositional Diffusion (MCCD) for text-to-image generation for complex scenes. Specifically, we design a multi-agent collaboration based scene parsing module that generates an agent system containing multiple agents with different tasks using MLLMs to adequately extract multiple scene elements. In addition, Hierarchical Compositional diffusion utilizes Gaussian mask and filtering to achieve the refinement of bounding box regions and highlights objects through region enhancement for accurate and high-fidelity generation of complex scenes. Comprehensive experiments demonstrate that our MCCD significantly improves the performance of the baseline models in a training-free manner, which has a large advantage in complex scene generation. The code will be open-source on github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MCCD_Multi-Agent_Collaboration-based_Compositional_Diffusion_for_Complex_Text-to-Image_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Mingcheng and Hou, Xiaolu and Liu, Ziyang and Yang, Dingkang and Qian, Ziyun and Chen, Jiawei and Wei, Jinjie and Jiang, Yue and Xu, Qingyao and Zhang, Lihua},
    title     = {MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13263-13272}
}

raw_affiliation:
[{"affiliation": "Academy for Engineering and Technology, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Cognition and Intelligent Technology Laboratory (CIT Lab)", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Future Science and Engineering, Soochow University", "city": "Suzhou", "country": "China"}, {"affiliation": "Jilin Provincial Key Laboratory of Intelligence Science and Engineering", "city": "Changchun", "country": "China"}, {"affiliation": "Engineering Research Center of AI and Robotics, Ministry of Education", "city": "Shanghai", "country": "China"}]

affiliations:
["Academy for Engineering and Technology, Fudan University", "Cognition and Intelligent Technology Laboratory (CIT Lab)", "School of Future Science and Engineering, Soochow University", "Jilin Provincial Key Laboratory of Intelligence Science and Engineering", "Engineering Research Center of AI and Robotics, Ministry of Education"]


title: Dual Semantic Guidance for Open Vocabulary Semantic Segmentation
authors: Zhengyang Wang, Tingliang Feng, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20212-20222
cities: ["Tianjin", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Dual_Semantic_Guidance_for_Open_Vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary semantic segmentation aims to enable models to segment arbitrary categories. Currently, though pre-trained Vision-Language Models (VLMs) like CLIP have established a robust foundation for this task by learning to match text and image representations from large-scale data, their lack of pixel-level recognition necessitates further fine-tuning. Most existing methods leverage text as a guide to achieve pixel-level recognition. However, the inherent biases in text semantic descriptions and the lack of pixel-level supervisory information make it challenging to fine-tune CLIP-based models effectively. This paper considers leveraging image-text data to simultaneously capture the semantic information contained in both image and text, thereby constructing Dual Semantic Guidance and corresponding pixel-level pseudo annotations. Particularly, the visual semantic guidance is enhanced via explicitly exploring foreground regions and minimizing the influence of background. The dual semantic guidance is then jointly utilized to fine-tune CLIP-based segmentation models, achieving decent fine-grained recognition capabilities. As the comprehensive evaluation shows, our method outperforms state-of-art results with large margins, on eight commonly used datasets with/without background.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Dual_Semantic_Guidance_for_Open_Vocabulary_Semantic_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhengyang and Feng, Tingliang and Lyu, Fan and Shang, Fanhua and Feng, Wei and Wan, Liang},
    title     = {Dual Semantic Guidance for Open Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20212-20222}
}

raw_affiliation:
[{"affiliation": "College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["College of Intelligence and Computing, Tianjin University", "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"]


title: CroCoDL: Cross-device Collaborative Dataset for Localization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27424-27434
affiliations: ["ETH Zurich", "Microsoft", "Lamarr Institute / Uni Bonn"]
cities: ["Zurich", "Redmond", "Bonn"]
countries: ["Switzerland", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Blum_CroCoDL_Cross-device_Collaborative_Dataset_for_Localization_CVPR_2025_paper.html

authors:
Hermann Blum, Alessandro Mercurio, Joshua O'Reilly, Tim Engelbracht, Mihai Dusmanu, Marc Pollefeys, Zuria Bauer

abstract:
Accurate localization plays a pivotal role in the autonomy of systems operating in unfamiliar environments, particularly when interaction with humans is expected. High-accuracy visual localization systems encompass various components, such as image retrievers, feature extractors, matchers, reconstruction and pose estimation methods. This complexity translates to the necessity of robust evaluation settings and pipelines. However, existing datasets and benchmarks primarily focus on single-agent scenarios, overlooking the critical issue of cross-device localization. Different agents with different sensors will show their own specific strengths and weaknesses, and the data they have available varies substantially. This work addresses this gap by enhancing an existing augmented reality visual localization benchmark with data from legged robots, and evaluating human-robot, cross-device mapping and localization. Our contributions extend beyond device diversity and include high environment variability, spanning ten distinct locations ranging from disaster sites to art exhibitions. Each scene in our dataset features recordings from robot agents, hand-held and head-mounted devices, and high-accuracy ground truth LiDAR scanners, resulting in a comprehensive multi-agent dataset and benchmark. This work represents a significant advancement in the field of visual localization benchmarking, with key insights into the performance of cross-device localization methods across diverse settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Blum_CroCoDL_Cross-device_Collaborative_Dataset_for_Localization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Blum_CroCoDL_Cross-device_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Blum_2025_CVPR,
    author    = {Blum, Hermann and Mercurio, Alessandro and O'Reilly, Joshua and Engelbracht, Tim and Dusmanu, Mihai and Pollefeys, Marc and Bauer, Zuria},
    title     = {CroCoDL: Cross-device Collaborative Dataset for Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27424-27434}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Lamarr Institute / Uni Bonn", "city": "Bonn", "country": "Germany"}]


title: Q-Bench-Video: Benchmark the Video Quality Understanding of LMMs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3229-3239
affiliations: ["Shanghai Jiaotong University", "Nanyang Technological University"]
cities: ["Shanghai", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Q-Bench-Video_Benchmark_the_Video_Quality_Understanding_of_LMMs_CVPR_2025_paper.html

authors:
Zicheng Zhang, Ziheng Jia, Haoning Wu, Chunyi Li, Zijian Chen, Yingjie Zhou, Wei Sun, Xiaohong Liu, Xiongkuo Min, Weisi Lin, Guangtao Zhai

abstract:
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding. To address this oversight, we introduce Q-Bench-Video in this paper, a new benchmark specifically designed to evaluate LMMs' proficiency in discerning video quality. a) To ensure video source diversity, Q-Bench-Video encompasses videos from natural scenes, AI-generated Content (AIGC), and Computer Graphics (CG). b) Building on the traditional multiple-choice questions format with the Yes-or-No and What-How categories, we include Open-ended questions to better evaluate complex scenarios. Additionally, we incorporate the video pair quality comparison question to enhance comprehensiveness. c) Beyond the traditional Technical, Aesthetic, and Temporal distortions, we have expanded our evaluation aspects to include the dimension of AIGC distortions, which addresses the increasing demand for video generation. Finally, we collect a total of 2,378 question-answer pairs and test them on 12 open-source & 5 proprietary LMMs. Our findings indicate that while LMMs have a foundational understanding of video quality, their performance remains incomplete and imprecise, with a notable discrepancy compared to human performance. Through Q-Bench-Video, we seek to catalyze community interest, stimulate further research, and unlock the untapped potential of LMMs to close the gap in video quality understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Q-Bench-Video_Benchmark_the_Video_Quality_Understanding_of_LMMs_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zicheng and Jia, Ziheng and Wu, Haoning and Li, Chunyi and Chen, Zijian and Zhou, Yingjie and Sun, Wei and Liu, Xiaohong and Min, Xiongkuo and Lin, Weisi and Zhai, Guangtao},
    title     = {Q-Bench-Video: Benchmark the Video Quality Understanding of LMMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3229-3239}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: Glossy Object Reconstruction with Cost-effective Polarized Acquisition
authors: Bojian Wu, Yifan Peng, Ruizhen Hu, Xiaowei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 422-431
affiliations: ["Zhejiang University", "The University of Hong Kong", "Shenzhen University"]
cities: ["Hangzhou", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Glossy_Object_Reconstruction_with_Cost-effective_Polarized_Acquisition_CVPR_2025_paper.html

abstract:
The challenge of image-based 3D reconstruction for glossy objects lies in separating diffuse and specular components on glossy surfaces from captured images, a task complicated by the ambiguity in discerning lighting conditions and material properties using RGB data alone. While state-of-the-art methods rely on tailored and/or high-end equipment for data acquisition,  which can be cumbersome and time-consuming, this work introduces a scalable polarization-aided approach that employs cost-effective acquisition tools. By attaching a linear polarizer to readily available RGB cameras, multi-view polarization images can be captured without the need for advance calibration or precise measurements of the polarizer angle, substantially reducing system construction costs. The proposed approach represents polarimetric BRDF, Stokes vectors, and polarization states of object surfaces as neural implicit fields. These fields, combined with the polarizer angle, are retrieved by optimizing the rendering loss of input polarized images. By leveraging fundamental physical principles for the implicit representation of polarization rendering, our method demonstrates superiority over existing techniques through experiments in public datasets and real captured images on both reconstruction and novel view synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Glossy_Object_Reconstruction_with_Cost-effective_Polarized_Acquisition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Glossy_Object_Reconstruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Bojian and Peng, Yifan and Hu, Ruizhen and Zhou, Xiaowei},
    title     = {Glossy Object Reconstruction with Cost-effective Polarized Acquisition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {422-431}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}]


title: Generalizable Object Keypoint Localization from Generative Priors
authors: Dongkai Wang, Jiang Duan, Liangjian Wen, Shiyu Xuan, Hao Chen, Shiliang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20265-20274
cities: ["Chengdu", "Chengdu", "Nanjing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Generalizable_Object_Keypoint_Localization_from_Generative_Priors_CVPR_2025_paper.html

abstract:
Generalizable object keypoint localization is a fundamental computer vision task in understanding the object structure. It is challenging for existing keypoint localization methods because their limited training data cannot provide generalizable shape and semantic cues, leading to inferior performance and generalization capability. Instead of relying on large scale training data, this work tackles this challenge by exploiting the rich priors from large generative models. We propose a data-efficient generalizable localization method named GenLoc. GenLoc extracts the generative priors from a pre-trained image generation model by calculating the correlation map between image latent feature and condition embedding. Those priors are hence optimized with our proposed heatmap expectation loss to perform object keypoint localization. Benefited by the rich knowledge of generative priors in understanding of object semantics and structures, GenLoc achieves superior performance on various object keypoint localization benchmarks. It shows more substantial performance enhancements in cross-domain, few-shot and zero-shot evaluation settings, e.g., getting 20%+ AP enhancement over CLAMP in various zero-shot settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Generalizable_Object_Keypoint_Localization_from_Generative_Priors_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Dongkai and Duan, Jiang and Wen, Liangjian and Xuan, Shiyu and Chen, Hao and Zhang, Shiliang},
    title     = {Generalizable Object Keypoint Localization from Generative Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20265-20274}
}

raw_affiliation:
[{"affiliation": "Southwestern University of Finance and Economics", "city": "Chengdu", "country": "China"}, {"affiliation": "Engineering Research Center of Intelligent Finance, Ministry of Education", "city": "Chengdu", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Southwestern University of Finance and Economics", "Engineering Research Center of Intelligent Finance, Ministry of Education", "School of Computer Science and Engineering, Nanjing University of Science and Technology", "National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "Peng Cheng Laboratory"]


title: CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24873-24883
cities: ["Nanjing", "Beijing", "Los Angeles", "Guangzhou", "Nanjing"]
countries: ["China", "China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_CLIP_is_Almost_All_You_Need_Towards_Parameter-Efficient_Scene_Text_CVPR_2025_paper.html

authors:
Xugong Qin, Peng Zhang, Jun Jie Ou Yang, Gangyan Zeng, Yubo Li, Yuanyuan Wang, Wanqian Zhang, Pengwen Dai

abstract:
Scene Text Retrieval (STR) seeks to identify all images containing a given query string. Existing methods typically rely on an explicit Optical Character Recognition (OCR) process of text spotting or localization, which is susceptible to complex pipelines and accumulated errors. To settle this, we resort to the Contrastive Language-Image Pre-training (CLIP) models, which have demonstrated the capacity to perceive and understand scene text, making it possible to achieve strictly OCR-free STR. From the perspective of parameter-efficient transfer learning, a lightweight visual position adapter is proposed to provide a positional information complement for the visual encoder. Besides, we introduce a visual context dropout technique to improve the alignment of local visual features. A novel, parameter-free cross-attention mechanism transfers the contrastive relationship between images and text to that between visual tokens and text, producing a rich cross-modal representation, which can be utilized for efficient reranking with a linear classifier. The resulting model, CAYN, which proves that CLIP is Almost all You Need for STR with no more than 0.50M additional parameters required, achieves new state-of-the-art performance on the STR task, with 92.46%/89.49%/85.98% mAP on the SVT/IIIT-STR/TTR datasets. Our findings demonstrate that CLIP can serve as a reliable and efficient solution for OCR-free STR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_CLIP_is_Almost_All_You_Need_Towards_Parameter-Efficient_Scene_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_CLIP_is_Almost_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Xugong and Zhang, Peng and Yang, Jun Jie Ou and Zeng, Gangyan and Li, Yubo and Wang, Yuanyuan and Zhang, Wanqian and Dai, Pengwen},
    title     = {CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24873-24883}
}

raw_affiliation:
[{"affiliation": "School of Cyber Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Laboratory for Advanced Computing and Intelligence Engineering", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Cyber Science and Engineering, Nanjing University of Science and Technology", "Institute of Information Engineering, Chinese Academy of Sciences", "University of Southern California", "Sun Yat-sen University", "Laboratory for Advanced Computing and Intelligence Engineering"]


authors: Sofia Casarin, Sergio Escalera, Oswald Lanz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4441-4451
affiliations: ["Free University of Bozen-Bolzano", "Computer Vision Center", "Universitat de Barcelona"]
cities: ["Bolzano", "Barcelona", "Barcelona"]
countries: ["Italy", "Spain", "Spain"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Casarin_L-SWAG_Layer-Sample_Wise_Activation_with_Gradients_Information_for_Zero-Shot_NAS_CVPR_2025_paper.html

title:
L-SWAG: Layer-Sample Wise Activation with Gradients Information for Zero-Shot NAS on Vision Transformers

abstract:
Training-free Neural Architecture Search (NAS) efficiently identifies high-performing neural networks using zero-cost (ZC) proxies. Unlike multi-shot and one-shot NAS approaches, ZC-NAS is both (i) time-efficient, eliminating the need for model training, and (ii) interpretable, with proxy designs often theoretically grounded. Despite rapid developments in the field, current SOTA ZC proxies are typically constrained to well-established convolutional search spaces. With the rise of Large Language Models shaping the future of deep learning, this work extends ZC proxy applicability to Vision Transformers (ViTs). We present a new benchmark using the Autoformer search space evaluated on 6 distinct tasks, and propose Layer-Sample Wise Activation with Gradients information (L-SWAG), a novel, generalizable metric that characterises both convolutional and transformer architectures across 14 tasks. Additionally, previous works highlighted how different proxies contain complementary information, motivating the need for a ML model to identify useful combinations. To further enhance ZC-NAS, we therefore introduce LIBRA-NAS (Low Information gain and Bias Re-Alignment), a method that strategically combines proxies to best represent a specific benchmark. Integrated into the NAS search, LIBRA-NAS outperforms evolution and gradient-based NAS techniques by identifying an architecture with a 17.0% test error on ImageNet1k in just 0.1 GPU days.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Casarin_L-SWAG_Layer-Sample_Wise_Activation_with_Gradients_Information_for_Zero-Shot_NAS_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Casarin_L-SWAG_Layer-Sample_Wise_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Casarin_2025_CVPR,
    author    = {Casarin, Sofia and Escalera, Sergio and Lanz, Oswald},
    title     = {L-SWAG: Layer-Sample Wise Activation with Gradients Information for Zero-Shot NAS on Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4441-4451}
}

raw_affiliation:
[{"affiliation": "Free University of Bozen-Bolzano", "city": "Bolzano", "country": "Italy"}, {"affiliation": "Computer Vision Center", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Universitat de Barcelona", "city": "Barcelona", "country": "Spain"}]


title: Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning
authors: Huabin Liu, Filip Ilievski, Cees G. M. Snoek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3262-3271
affiliations: ["Shanghai Jiao Tong University", "Vrije Universiteit Amsterdam", "University of Amsterdam"]
cities: ["Shanghai", "Amsterdam", "Amsterdam"]
countries: ["China", "Netherlands", "Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Commonsense_Video_Question_Answering_through_Video-Grounded_Entailment_Tree_Reasoning_CVPR_2025_paper.html

abstract:
This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-language models (VLMs), there are growing concerns that they learn spurious correlations between videos and likely answers, reinforced by their black-box nature and remaining benchmarking biases. Our method explicitly grounds VQA tasks to video fragments in four steps: entailment tree construction, video-language entailment verification, tree reasoning, and dynamic tree expansion. A vital benefit of the method is its generalizability to current video- and image-based VLMs across reasoning types.To support fair evaluation, we devise a de-biasing procedure based on large-language models that rewrite VQA benchmark answer sets to enforce model reasoning. Systematic experiments on existing and de-biased benchmarks highlight the impact of our method components across benchmarks, VLMs, and reasoning types.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Commonsense_Video_Question_Answering_through_Video-Grounded_Entailment_Tree_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Commonsense_Video_Question_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Huabin and Ilievski, Filip and Snoek, Cees G. M.},
    title     = {Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3262-3271}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Vrije Universiteit Amsterdam", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}]


title: What Makes a Good Dataset for Knowledge Distillation?
authors: Logan Frank, Jim Davis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23755-23764
raw_affiliation: [{"affiliation": "Ohio State University", "city": "Columbus", "country": "USA"}]
affiliations: ["Ohio State University"]
cities: ["Columbus"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Frank_What_Makes_a_Good_Dataset_for_Knowledge_Distillation_CVPR_2025_paper.html

abstract:
Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the teacher's original dataset will also be available when training the student. However, in situations such as continual learning and distilling large models trained on company-withheld datasets, having access to the original data may not always be possible. This leads practitioners towards utilizing other sources of supplemental data, which could yield mixed results. One must then ask: "what makes a good dataset for transferring knowledge from teacher to student?" Many would assume that only real in-domain imagery is viable, but is that the only option? In this work, we explore multiple possible surrogate distillation datasets and demonstrate that many different datasets, even unnatural synthetic imagery, can serve as a suitable alternative in KD. From examining these alternative datasets, we identify and present various criteria describing what makes a good dataset for distillation. Source code is available at https://github.com/osu-cvl/good-kd-dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Frank_What_Makes_a_Good_Dataset_for_Knowledge_Distillation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Frank_2025_CVPR,
    author    = {Frank, Logan and Davis, Jim},
    title     = {What Makes a Good Dataset for Knowledge Distillation?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23755-23764}
}


title: Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
authors: Qizhou Chen, Chengyu Wang, Dakan Wang, Taolin Zhang, Wangyue Li, Xiaofeng He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9455-9466
cities: ["Shanghai", "Hangzhou", "Hangzhou", "Shanghai", "Hefei"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Lifelong_Knowledge_Editing_for_Vision_Language_Models_with_Low-Rank_Mixture-of-Experts_CVPR_2025_paper.html

abstract:
Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This task poses challenges in lifelong scenarios where edits must be continuously applied for real-world applications. While some editors demonstrate strong robustness for lifelong editing in pure LLMs, Vision LLMs (VLLMs), which incorporate an additional vision modality, are not directly adaptable to existing LLM editors. In this paper, we propose LiveEdit, a lifelong vision language model edit to bridge the gap between lifelong LLM editing and VLLMs. We begin by training an editing expert generator to independently produce low-rank experts for each editing instance, with the goal of correcting the relevant responses of the VLLM. A hard filtering mechanism is developed to utilize visual semantic knowledge, thereby coarsely eliminating visually irrelevant experts for input queries during the inference stage of the post-edited model. Finally, to integrate visually relevant experts, we introduce a soft routing mechanism based on textual semantic relevance to achieve multi-expert fusion. For evaluation, we establish a benchmark for lifelong VLLM editing. Extensive experiments demonstrate that LiveEdit offers significant advantages in lifelong VLLM editing scenarios. Further experiments validate the rationality and effectiveness of each module design in LiveEdit.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Lifelong_Knowledge_Editing_for_Vision_Language_Models_with_Low-Rank_Mixture-of-Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Lifelong_Knowledge_Editing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Qizhou and Wang, Chengyu and Wang, Dakan and Zhang, Taolin and Li, Wangyue and He, Xiaofeng},
    title     = {Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9455-9466}
}

raw_affiliation:
[{"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Exacity Inc.", "city": "Shanghai", "country": "China"}, {"affiliation": "Hefei University of Technology", "city": "Hefei", "country": "China"}]

affiliations:
["East China Normal University", "Alibaba Cloud Computing", "Alibaba Group", "Exacity Inc.", "Hefei University of Technology"]


title: Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification
authors: Rui Gong, Kim-Hui Yap, Weide Liu, Xulei Yang, Jun Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22348-22358
affiliations: ["Nanyang Technological University", "Institute for Infocomm Research, A*STAR"]
cities: ["Singapore", "Singapore"]
countries: ["Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gong_Rectification-specific_Supervision_and_Constrained_Estimator_for_Online_Stereo_Rectification_CVPR_2025_paper.html

abstract:
Online stereo rectification is critical for autonomous vehicles and robots in dynamic environments, where factors such as vibration, temperature fluctuations, and mechanical stress can affect rectification accuracy and severely degrade downstream stereo depth estimation. Current dominant approaches for online stereo rectification involve estimating relative camera poses in real time to derive rectification homographies. However, they do not directly optimize for rectification constraints. Additionally, the general-purpose correspondence matchers used in these methods are not trained for rectification, while training of these matchers typically requires ground-truth correspondences which are not available in stereo rectification datasets. To address these limitations, we propose a matching-based stereo rectification framework that is directly optimized for rectification and does not require ground-truth correspondence annotations for training. We assume intrinsics are known as they are generally available on modern devices and are relatively stable. Our framework incorporates a rectification-constrained estimator and applies multi-level, rectification-specific supervision that trains the matcher network for rectification without relying on ground-truth correspondences. Additionally, we create a new rectification dataset with ground-truth optical flow annotations, eliminating bias from evaluation metrics used in prior work that relied on pretrained keypoint matching or optical flow models. Extensive experiments show that our approach outperforms both state-of-the-art matching-based and matching-free methods in vertical flow metric by 10.7% on the Carla-Flowguided dataset and 21.3% on the Semi-Truck Highway dataset, offering superior rectification accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gong_Rectification-specific_Supervision_and_Constrained_Estimator_for_Online_Stereo_Rectification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gong_2025_CVPR,
    author    = {Gong, Rui and Yap, Kim-Hui and Liu, Weide and Yang, Xulei and Cheng, Jun},
    title     = {Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22348-22358}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute for Infocomm Research, A*STAR", "city": "Singapore", "country": "Singapore"}]


title: Shape and Texture: What Influences Reliable Optical Flow Estimation?
authors: Libo Long, Xiao Hu, Jochen Lang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27894-27903
raw_affiliation: [{"affiliation": "EECS, University of Ottawa", "city": "Ottawa", "country": "Canada"}]
affiliations: ["EECS, University of Ottawa"]
cities: ["Ottawa"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Long_Shape_and_Texture_What_Influences_Reliable_Optical_Flow_Estimation_CVPR_2025_paper.html

abstract:
Recent methods have made significant progress in optical flow estimation. However, the evaluation of these methods focuses mainly on improved accuracy in benchmarks and often overlooks the analysis of network robustness, which may be important in safety-critical scenarios such as autonomous driving. In this paper, we propose a novel method for robustness evaluation by modifying data from original benchmarks. Unlike previous benchmarks that focus on complex scenes, we propose to modify shape and texture of objects from the original images in order to analyze the sensitivity to these changes observed in the output. Our aim is to identify common failure cases of state-of-the-art (SOTA) methods to evaluate their robustness and understand their behaviors. We show that: Optical flow methods are more sensitive to shape changes than to texture changes; and optical flow methods tend to "remember" objects seen during training and may "ignore" the motion of unseen objects. Our experimental results and findings provide a more in-depth understanding of the behavior of recent optical flow methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Long_Shape_and_Texture_What_Influences_Reliable_Optical_Flow_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Long_Shape_and_Texture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Long_2025_CVPR,
    author    = {Long, Libo and Hu, Xiao and Lang, Jochen},
    title     = {Shape and Texture: What Influences Reliable Optical Flow Estimation?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27894-27903}
}


title: PartGen: Part-level 3D Generation and Reconstruction with Multi-view Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5881-5892
affiliations: ["Visual Geometry Group, University of Oxford", "Meta AI"]
cities: ["Oxford", "Menlo Park"]
countries: ["United Kingdom", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_PartGen_Part-level_3D_Generation_and_Reconstruction_with_Multi-view_Diffusion_Models_CVPR_2025_paper.html

authors:
Minghao Chen, Roman Shapovalov, Iro Laina, Tom Monnier, Jianyuan Wang, David Novotny, Andrea Vedaldi

abstract:
Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures, but as single, fused entities lacking meaningful structure. In contrast, most applications and creative workflows require 3D assets to be composed of distinct, meaningful parts that can be independently manipulated. To bridge this gap, we introduce PartGen, a novel approach for generating, from text, images, or unstructured 3D objects, 3D objects composed of meaningful parts. Our method leverages a multi-view diffusion model to extract plausible and view-consistent part segmentations from multiple views of a 3D object, dividing it into meaningful components. A second multi-view diffusion model then processes each part individually, filling in occlusions and generating completed views, which are subsequently passed to a 3D reconstruction network. The completion process ensures that the reconstructed parts integrate cohesively by considering the context of the entire object, compensating for missing information caused by occlusions and, in extreme cases, hallucinating entirely invisible parts based on contextual cues. We evaluate PartGen on both generated and real 3D assets, demonstrating significant improvements over segmentation and part completion baselines. We also showcase downstream applications such as text-guided 3D part editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_PartGen_Part-level_3D_Generation_and_Reconstruction_with_Multi-view_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_PartGen_Part-level_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Minghao and Shapovalov, Roman and Laina, Iro and Monnier, Tom and Wang, Jianyuan and Novotny, David and Vedaldi, Andrea},
    title     = {PartGen: Part-level 3D Generation and Reconstruction with Multi-view Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5881-5892}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group, University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}]


authors: Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Aikun Xu, Boyu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15444-15453
cities: ["Changsha", "London"]
countries: ["China", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.html

title:
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning

abstract:
Server aggregation conflict is a key challenge in personalized federated learning (PFL). While existing PFL methods have achieved significant progress with shallow base models (e.g., four-layer CNNs), they often overlook the negative impacts of deeper base models on personalization mechanisms. In this paper, we identify the phenomenon of deep model degradation in PFL, where as base model depth increases, the model becomes more sensitive to local client data distributions, thereby exacerbating server aggregation conflicts and ultimately reducing overall model performance. Moreover, we show that these conflicts manifest in insufficient global average updates and mutual constraints between clients. Motivated by our analysis, we proposed a two-stage conflict-aware layer-wise mitigation algorithm (FedCALM), which first constructs a conflict-free global update to alleviate negative conflicts, and then maximizes the benefits of all clients through a conflict-aware strategy. Notably, our method naturally leads to a selective mechanism that balances the tradeoff between clients involved in aggregation and the tolerance for conflicts. Consequently, it can boost the positive contribution to the clients even with the greatest conflicts with the global update. Extensive experiments across multiple datasets and deeper base models demonstrate that FedCALM outperforms four state-of-the-art (SOTA) methods by up to 9.88% and seamlessly integrates into existing PFL methods with performance improvements of up to 9.01%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_FedCALM_Conflict-aware_Layer-wise_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Hao and Hu, Zhigang and Yang, Liu and Zheng, Meiguang and Xu, Aikun and Wang, Boyu},
    title     = {FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15444-15453}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Central South University", "city": "Changsha", "country": "China"}, {"affiliation": "Department of Computer Science, University of Western Ontario", "city": "London", "country": "Canada"}]

affiliations:
["School of Computer Science and Engineering, Central South University", "Department of Computer Science, University of Western Ontario"]


title: SINR: Sparsity Driven Compressed Implicit Neural Representations
authors: Dhananjaya Jayasundara, Sudarshan Rajagopalan, Yasiru Ranasinghe, Trac D. Tran, Vishal M. Patel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3061-3070
raw_affiliation: [{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]
affiliations: ["Johns Hopkins University"]
cities: ["Baltimore"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jayasundara_SINR_Sparsity_Driven_Compressed_Implicit_Neural_Representations_CVPR_2025_paper.html

abstract:
Implicit Neural Representations (INRs) are increasingly recognized as a versatile data modality for representing discretized signals, offering benefits such as infinite query resolution and reduced storage requirements. Existing signal compression approaches for INRs typically employ one of two strategies: 1. direct quantization with entropy coding of the trained INR; 2. deriving a latent code on top of the INR through a learnable transformation. Thus, their performance is heavily dependent on the quantization and entropy coding schemes employed. In this paper, we introduce SINR, an innovative compression algorithm that leverages the patterns in the vector spaces formed by weights of INRs. We compress these vector spaces using a high-dimensional sparse code within a dictionary. Further analysis reveals that the atoms of the dictionary used to generate the sparse code do not need to be learned or transmitted to successfully recover the INR weights. We demonstrate that the proposed approach can be integrated with any existing INR-based signal compression technique. Our results indicate that SINR achieves substantial reductions in storage requirements for INRs across various configurations, outperforming conventional INR-based compression baselines. Furthermore, SINR maintains high-quality decoding across diverse data modalities, including images, occupancy fields, and Neural Radiance Fields.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jayasundara_SINR_Sparsity_Driven_Compressed_Implicit_Neural_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jayasundara_SINR_Sparsity_Driven_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jayasundara_2025_CVPR,
    author    = {Jayasundara, Dhananjaya and Rajagopalan, Sudarshan and Ranasinghe, Yasiru and Tran, Trac D. and Patel, Vishal M.},
    title     = {SINR: Sparsity Driven Compressed Implicit Neural Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3061-3070}
}


title: CaricatureBooth: Data-Free Interactive Caricature Generation in a Photo Booth
authors: Zhiyu Qu, Yunqi Miao, Zhensong Zhang, Jifei Song, Jiankang Deng, Yi-Zhe Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10815-10824
cities: ["Guildford", "Coventry", "Unknown", "London"]
countries: ["United Kingdom", "United Kingdom", "Unknown", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_CaricatureBooth_Data-Free_Interactive_Caricature_Generation_in_a_Photo_Booth_CVPR_2025_paper.html

abstract:
We present CaricatureBooth, a system that transforms caricature creation into a simple interactive experience -- as easy as using a photo booth! A key challenge in caricature generation is two-fold: the scarcity of high-quality caricature data and the difficulty in enabling precise creative control over the exaggeration process while maintaining identity. Prior approaches either require large-scale caricature and photo data or lack intuitive mechanisms for users to guide the deformation without losing identity. We address the data scarcity by synthesising training data through Thin Plate Spline (TPS) deformation of standard face images. For creative control, we design a Bezier curve interface where users can easily manipulate facial features, with these edits then driving TPS transformations at inference time. When combined with a pre-trained ID-preserving diffusion model, our system maintains both identity preservation and creative flexibility. Through extensive experiments, we demonstrate that CaricatureBooth achieves state-of-the-art quality while making the joy of caricature creation as accessible as taking a photo -- just walk in and walk out with your personalised caricature! Code is available at https://github.com/WinKawaks/CaricatureBooth.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_CaricatureBooth_Data-Free_Interactive_Caricature_Generation_in_a_Photo_Booth_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_CaricatureBooth_Data-Free_Interactive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Zhiyu and Miao, Yunqi and Zhang, Zhensong and Song, Jifei and Deng, Jiankang and Song, Yi-Zhe},
    title     = {CaricatureBooth: Data-Free Interactive Caricature Generation in a Photo Booth},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10815-10824}
}

raw_affiliation:
[{"affiliation": "SketchX, CVSSP, University of Surrey", "city": "Guildford", "country": "United Kingdom"}, {"affiliation": "University of Warwick", "city": "Coventry", "country": "United Kingdom"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}]

affiliations:
["SketchX, CVSSP, University of Surrey", "University of Warwick", "Independent Researcher", "Imperial College London"]


title: FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16336-16345
affiliations: ["The Chinese University of Hong Kong", "University of Texas at Austin", "Nvidia"]
cities: ["Hong Kong", "Austin", "Santa Clara"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_FlexGS_Train_Once_Deploy_Everywhere_with_Many-in-One_Flexible_3D_Gaussian_CVPR_2025_paper.html

authors:
Hengyu Liu, Yuehao Wang, Chenxin Li, Ruisi Cai, Kevin Wang, Wuyang Li, Pavlo Molchanov, Peihao Wang, Zhangyang Wang

abstract:
3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands significant GPU memory, limiting its use on devices with restricted computational resources. Previous approaches have focused on pruning less important Gaussians, effectively compressing 3DGS but often requiring a fine-tuning stage and lacking adaptability for the specific memory needs of different devices. In this work, we present an elastic inference method for 3DGS. Given an input for the desired model size, our method selects and transforms a subset of Gaussians, achieving substantial rendering performance without additional fine-tuning. We introduce a tiny learnable module that controls Gaussian selection based on the input percentage, along with a transformation module that adjusts the selected Gaussians to complement the performance of the reduced model. Comprehensive experiments on ZipNeRF, MipNeRF and Tanks&Temples scenes demonstrate the effectiveness of our approach. Code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_FlexGS_Train_Once_Deploy_Everywhere_with_Many-in-One_Flexible_3D_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_FlexGS_Train_Once_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hengyu and Wang, Yuehao and Li, Chenxin and Cai, Ruisi and Wang, Kevin and Li, Wuyang and Molchanov, Pavlo and Wang, Peihao and Wang, Zhangyang},
    title     = {FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16336-16345}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Nvidia", "city": "Santa Clara", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12615-12625
affiliations: ["Shenzhen Graduate School, Peking University1", "Tencent Youtu Lab2"]
cities: ["Shenzhen", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Generalizing_Deepfake_Video_Detection_with_Plug-and-Play_Video-Level_Blending_and_Spatiotemporal_CVPR_2025_paper.html

title:
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning

authors:
Zhiyuan Yan, Yandan Zhao, Shen Chen, Mingyi Guo, Xinghe Fu, Taiping Yao, Shouhong Ding, Yunsheng Wu, Li Yuan

abstract:
Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean heavily on one type of artifact and ignore the other: how can we ensure balanced learning from both? (3) Videos are naturally resource-intensive: how can we tackle efficiency without compromising accuracy? This paper attempts to tackle the three challenges jointly. First, inspired by the notable generality of using image-level blending data for image forgery detection, we investigate whether and how video-level blending can be effective in video. We then perform a thorough analysis and identify a previously underexplored temporal forgery artifact: Facial Feature Drift (FFD), which commonly exists across different forgeries. To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts. Second, we carefully design a lightweight Spatiotemporal Adapter (StA) to equip a pre-trained image model with the ability to capture both spatial and temporal features jointly and efficiently. StA is designed with two-stream 3D-Conv with varying kernel sizes, allowing it to process spatial and temporal features separately. This eliminates the need to design a new deepfake-specific video architecture from scratch. Extensive experiments validate the effectiveness of the proposed methods; and show our approach can generalize well to previously unseen forgery videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Generalizing_Deepfake_Video_Detection_with_Plug-and-Play_Video-Level_Blending_and_Spatiotemporal_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Zhiyuan and Zhao, Yandan and Chen, Shen and Guo, Mingyi and Fu, Xinghe and Yao, Taiping and Ding, Shouhong and Wu, Yunsheng and Yuan, Li},
    title     = {Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12615-12625}
}

raw_affiliation:
[{"affiliation": "Shenzhen Graduate School, Peking University1", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent Youtu Lab2", "city": "Unknown", "country": "Unknown"}]


title: ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning
authors: Kailin Li, Puhao Li, Tengyu Liu, Yuyang Li, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6991-7003
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_ManipTrans_Efficient_Dexterous_Bimanual_Manipulation_Transfer_via_Residual_Learning_CVPR_2025_paper.html

abstract:
Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. To address this, we introduce ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator to mimic hand motion, then fine-tunes a specific residual module under interaction constraints, enabling efficient learning and accurate execution of complex bimanual tasks. Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging ManipTrans, we transfer multiple hand-object datasets to robotic hands, creating DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K episodes of robotic manipulation and is easily extensible, facilitating further policy training for dexterous hands and enabling real-world deployments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_ManipTrans_Efficient_Dexterous_Bimanual_Manipulation_Transfer_via_Residual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_ManipTrans_Efficient_Dexterous_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Kailin and Li, Puhao and Liu, Tengyu and Li, Yuyang and Huang, Siyuan},
    title     = {ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6991-7003}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of General Artificial Intelligence, BIGAI", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Automation, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of General Artificial Intelligence, BIGAI", "Department of Automation, Tsinghua University", "Institute for Artificial Intelligence, Peking University"]


title: Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters
authors: Yuan Wang, Ouxiang Li, Tingting Mu, Yanbin Hao, Kuien Liu, Xiang Wang, Xiangnan He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28759-28768
cities: ["Hefei", "Manchester", "Hefei", "Beijing"]
countries: ["China", "UK", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Precise_Fast_and_Low-cost_Concept_Erasure_in_Value_Space__CVPR_2025_paper.html

abstract:
The success of text-to-image generation enabled by diffusion models has imposed an urgent need to erase unwanted concepts, e.g., copyrighted, offensive, and unsafe ones, from the pre-trained models in a precise, timely, and low-cost manner. The twofold demand of concept erasure requires a precise removal of the target concept during generation (i.e., erasure efficacy), while a minimal impact on non-target content generation (i.e., prior preservation). Existing methods are either computationally costly or face challenges in maintaining an effective balance between erasure efficacy and prior preservation. To improve, we propose a precise, fast, and low-cost concept erasure method, called Adaptive Vaule Decomposer (AdaVD), which is training-free. This method is grounded in a classical linear algebraic orthogonal complement operation, implemented in the value space of each cross-attention layer within the UNet of diffusion models. An effective shift factor is designed to adaptively navigate the erasure strength, enhancing prior preservation without sacrificing erasure efficacy. Extensive experimental results show that the proposed AdaVD is effective at both single and multiple concept erasure, showing a 2- to 10-fold improvement in prior preservation as compared to the second best, meanwhile achieving the best or near best erasure efficacy, when comparing with both training-based and training-free state of the arts. AdaVD supports a series of diffusion models and downstream image generation tasks, with code to be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Precise_Fast_and_Low-cost_Concept_Erasure_in_Value_Space__CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Precise_Fast_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuan and Li, Ouxiang and Mu, Tingting and Hao, Yanbin and Liu, Kuien and Wang, Xiang and He, Xiangnan},
    title     = {Precise, Fast, and Low-cost Concept Erasure in Value Space:  Orthogonal Complement Matters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28759-28768}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "The University of Manchester", "city": "Manchester", "country": "UK"}, {"affiliation": "Hefei University of Technology", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Software Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["University of Science and Technology of China", "The University of Manchester", "Hefei University of Technology", "Institute of Software Chinese Academy of Sciences"]


title: HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation
authors: Kun Liu, Qi Liu, Xinchen Liu, Jie Li, Yongdong Zhang, Jiebo Luo, Xiaodong He, Wu Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24001-24010
affiliations: ["JD Explore Academy", "UCAS", "USTC", "University of Rochester"]
cities: ["Beijing", "Beijing", "Hefei", "Rochester"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_HOIGen-1M_A_Large-scale_Dataset_for_Human-Object_Interaction_Video_Generation_CVPR_2025_paper.html

abstract:
Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely generated by current T2V models due to the lack of large-scale videos with accurate captions for HOI. To address this issue, we introduce HOIGen-1M, the first large-scale dataset for HOI Generation, consisting of over one million high-quality videos collected from diverse sources. In particular, to guarantee the high quality of videos, we first design an efficient framework to automatically curate HOI videos using the powerful multimodal large language models (MLLMs), and then the videos are further cleaned by human annotators. Moreover, to obtain accurate textual captions for HOI videos, we design a novel video description method based on a Mixture-of-Multimodal-Experts (MoME) strategy that not only generates expressive captions but also eliminates the hallucination by individual MLLM. Furthermore, due to the lack of an evaluation framework for generated HOI videos, we propose two new metrics to assess the quality of generated videos in a coarse-to-fine manner. Extensive experiments reveal that current T2V models struggle to generate high-quality HOI videos and confirm that our HOIGen-1M dataset is instrumental for improving HOI video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_HOIGen-1M_A_Large-scale_Dataset_for_Human-Object_Interaction_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_HOIGen-1M_A_Large-scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Kun and Liu, Qi and Liu, Xinchen and Li, Jie and Zhang, Yongdong and Luo, Jiebo and He, Xiaodong and Liu, Wu},
    title     = {HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24001-24010}
}

raw_affiliation:
[{"affiliation": "JD Explore Academy", "city": "Beijing", "country": "China"}, {"affiliation": "UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}, {"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}]


title: T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation
authors: Lijun Li, Zhelun Shi, Xuhao Hu, Bowen Dong, Yiran Qin, Xihui Liu, Lu Sheng, Jing Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13381-13392
cities: ["Shanghai", "Beijing", "Shanghai", "Harbin", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_T2ISafety_Benchmark_for_Assessing_Fairness_Toxicity_and_Privacy_in_Image_CVPR_2025_paper.html

abstract:
Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable  safety concerns, including the risk of generating harmful, biased, or private content. Current research on assessing T2I safety remains in its early stages. While some efforts have been made to evaluate models on specific safety dimensions, many critical risks remain unexplored. To address this gap, we introduce T2ISafety, a safety benchmark that evaluates T2I models across three key domains: toxicity, fairness, and bias. We build a detailed hierarchy of 12 tasks and 44 categories based on these three domains, and meticulously collect 70K corresponding prompts. Based on this taxonomy and prompt set, we build a large-scale T2I dataset with 68K manually annotated images and train an evaluator capable of detecting critical risks that previous work has failed to identify, including risks that even ultra-large proprietary models like GPTs cannot correctly detect. We evaluate 15 prominent diffusion models on T2ISafety and reveal several concerns including persistent issues with racial fairness, a tendency to generate toxic content, and significant variation in privacy protection across the models, even with defense methods like concept erasing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_T2ISafety_Benchmark_for_Assessing_Fairness_Toxicity_and_Privacy_in_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_T2ISafety_Benchmark_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Lijun and Shi, Zhelun and Hu, Xuhao and Dong, Bowen and Qin, Yiran and Liu, Xihui and Sheng, Lu and Shao, Jing},
    title     = {T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13381-13392}
}

raw_affiliation:
[{"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Chinese University of Hong Kong (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shanghai Artificial Intelligence Laboratory", "Beihang University", "Fudan University", "Harbin Institute of Technology", "Chinese University of Hong Kong (Shenzhen)", "The University of Hong Kong"]


title: Order-One Rolling Shutter Cameras
authors: Marvin Anas Hahn, Kathlén Kohn, Orlando Marigliano, Tomas Pajdla
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27007-27016
cities: ["Dublin", "Stockholm", "Genoa", "Prague"]
countries: ["Ireland", "Sweden", "Italy", "Czech Republic"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hahn_Order-One_Rolling_Shutter_Cameras_CVPR_2025_paper.html

abstract:
Rolling shutter (RS) cameras dominate consumer and smartphone markets. Several methods for computing the absolute pose of RS cameras have appeared in the last 20 years, but the relative pose problem has not been fully solved yet. We provide a unified theory for the important class of order-one rolling shutter (RS_1) cameras. These cameras generalize the perspective projection to RS cameras, projecting a generic space point to exactly one image point via a rational map. We introduce a new back-projection RS camera model, characterize RS_1 cameras, construct explicit parameterizations of such cameras, and determine the image of a space line. We classify all minimal problems for solving the relative camera pose problem with linear RS_1 cameras and discover new practical cases. Finally, we show how the theory can be used to explain RS models previously used for absolute pose computation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hahn_Order-One_Rolling_Shutter_Cameras_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hahn_Order-One_Rolling_Shutter_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Hahn_2025_CVPR,
    author    = {Hahn, Marvin Anas and Kohn, Kathl\'en and Marigliano, Orlando and Pajdla, Tomas},
    title     = {Order-One Rolling Shutter Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27007-27016}
}

raw_affiliation:
[{"affiliation": "Trinity College Dublin", "city": "Dublin", "country": "Ireland"}, {"affiliation": "KTH Royal Institute of T echnology", "city": "Stockholm", "country": "Sweden"}, {"affiliation": "University of Genoa", "city": "Genoa", "country": "Italy"}, {"affiliation": "CIIRC, Czech T echnical University in Prague", "city": "Prague", "country": "Czech Republic"}]

affiliations:
["Trinity College Dublin", "KTH Royal Institute of T echnology", "University of Genoa", "CIIRC, Czech T echnical University in Prague"]


title: Animate and Sound an Image
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23369-23378
affiliations: ["Gaoling School of Artificial Intelligence, Renmin University of China", "ZHI-TECH GROUP"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Animate_and_Sound_an_Image_CVPR_2025_paper.html

authors:
Xihua Wang, Ruihua Song, Chongxuan Li, Xin Cheng, Boyuan Li, Yihan Wu, Yuyue Wang, Hongteng Xu, Yunfeng Wang

abstract:
This paper addresses a promising yet underexplored task, Image-to-Sounding-Video (I2SV) generation, which animates a static image and generates synchronized sound simultaneously. Despite advances in video and audio generation models, challenges remain to develop a unified model for generating naturally sounding videos. In this work, we propose a novel approach that leverages two separate pretrained diffusion models and makes vision and audio influence each other during generation based on the Diffusion Transformer (DiT) architecture. First, the individual video and audio pretrained generation models are decomposed into input, output, and expert sub-modules. We propose using a unified joint DiT block to integrate the expert sub-modules to effectively model the interaction between the two modalities, resulting in high-quality I2SV generation. Then, we introduce a joint classifier-free guidance technique to boost the performance during joint generation. Finally, we conduct extensive experiments on three popular benchmark datasets, and in both objective and subjective evaluation our method surpass all the baseline methods in almost all metrics. Case studies show our generated sounding videos are high quality and synchronized between video and audio.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Animate_and_Sound_an_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Animate_and_Sound_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xihua and Song, Ruihua and Li, Chongxuan and Cheng, Xin and Li, Boyuan and Wu, Yihan and Wang, Yuyue and Xu, Hongteng and Wang, Yunfeng},
    title     = {Animate and Sound an Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23369-23378}
}

raw_affiliation:
[{"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "ZHI-TECH GROUP", "city": "Beijing", "country": "China"}]


title: Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model
authors: Yingmao Miao, Zhanpeng Huang, Rui Han, Zibin Wang, Chenhao Lin, Chao Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 359-368
affiliations: ["Xi\u2019an Jiaotong University", "SenseTime Research"]
cities: ["Xi'an", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Miao_Shining_Yourself_High-Fidelity_Ornaments_Virtual_Try-on_with_Diffusion_Model_CVPR_2025_paper.html

abstract:
While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consistency under large pose and scale variances between ornaments and models. This paper proposes the task of virtual try-on for ornaments and presents a method to improve the geometric and appearance preservation of ornament virtual try-ons. Specifically, we estimate an accurate wearing mask to improve the alignments between ornaments and models in an iterative scheme alongside the denoising process. To preserve structure details, we further regularize attention layers to map the reference ornament mask to the wearing mask in an implicit way. Experiment results demonstrate that our method successfully wears ornaments from reference images onto target models, handling substantial differences in scale and pose while preserving identity and achieving realistic visual effects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Miao_Shining_Yourself_High-Fidelity_Ornaments_Virtual_Try-on_with_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Miao_Shining_Yourself_High-Fidelity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Miao_2025_CVPR,
    author    = {Miao, Yingmao and Huang, Zhanpeng and Han, Rui and Wang, Zibin and Lin, Chenhao and Shen, Chao},
    title     = {Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {359-368}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}]


title: Foveated Instance Segmentation
authors: Hongyi Zeng, Wenxuan Liu, Tianhua Xia, Jinhui Chen, Ziyun Li, Sai Qian Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24496-24505
affiliations: ["New York University", "Meta Reality Labs", "New York University"]
cities: ["New York", "Menlo Park", "New York"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Foveated_Instance_Segmentation_CVPR_2025_paper.html

abstract:
Instance segmentation is essential for augmented reality and virtual reality (AR/VR) as it enables precise object recognition and interaction, enhancing the integration of virtual and real-world elements for an immersive experience. However, the high computational overhead of segmentation limits its application on resource-constrained AR/VR devices, causing large processing latency and degrading user experience.  In contrast to conventional scenarios, AR/VR users typically focus on only a few regions within their field of view before shifting perspective, allowing segmentation to be concentrated on gaze-specific areas. This insight drives the need for efficient segmentation methods that prioritize processing instance of interest, reducing computational load and enhancing real-time performance. In this paper, we present a foveated instance segmentation(FovealSeg) framework that leverages real-time user gaze data to perform instance segmentation exclusively on instance of interest, resulting in substantial computational savings. Evaluation results show that FSNet achieves an IoU of 0.56 on ADE20K and 0.54 on LVIS, notably outperforming the baseline. The code is available at https://github.com/SAI-Lab-NYU/Foveated-Instance-Segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Foveated_Instance_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Hongyi and Liu, Wenxuan and Xia, Tianhua and Chen, Jinhui and Li, Ziyun and Zhang, Sai Qian},
    title     = {Foveated Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24496-24505}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: Make It Count: Text-to-Image Generation with an Accurate Number of Objects
authors: Lital Binyamin, Yoad Tewel, Hilit Segev, Eran Hirsch, Royi Rassin, Gal Chechik
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13242-13251
affiliations: ["Bar-Ilan University", "NVIDIA", "Tel-Aviv University"]
cities: ["Ramat Gan", "Santa Clara", "Tel Aviv"]
countries: ["Israel", "USA", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Binyamin_Make_It_Count_Text-to-Image_Generation_with_an_Accurate_Number_of_CVPR_2025_paper.html

abstract:
Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various applications from technical documents, to children's books to illustrating cooking recipes. Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation. It is still unknown if such representations exist. To address count-correct generation, we first identify features within the diffusion model that can carry the object identity information. We then use them to separate and count instances of objects during the denoising process and detect over-generation and under-generation. We fix the latter by training a model that predicts both the shape and location of a missing object, based on the layout of existing ones, and show how it can be used to guide denoising with correct object count. Our approach, CountGen, does not depend on external source to determine object layout, but rather uses the prior from the diffusion model itself, creating prompt-dependent and seed-dependent layouts. Evaluated on two benchmark datasets, we find that CountGen strongly outperforms the count-accuracy of existing baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Binyamin_Make_It_Count_Text-to-Image_Generation_with_an_Accurate_Number_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Binyamin_Make_It_Count_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Binyamin_2025_CVPR,
    author    = {Binyamin, Lital and Tewel, Yoad and Segev, Hilit and Hirsch, Eran and Rassin, Royi and Chechik, Gal},
    title     = {Make It Count: Text-to-Image Generation with an Accurate Number of Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13242-13251}
}

raw_affiliation:
[{"affiliation": "Bar-Ilan University", "city": "Ramat Gan", "country": "Israel"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Tel-Aviv University", "city": "Tel Aviv", "country": "Israel"}]


title: Universal Domain Adaptation for Semantic Segmentation
authors: Seun-An Choe, Keon-Hee Park, Jinwoo Choi, Gyeong-Moon Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4607-4617
affiliations: ["Kyung Hee University", "Korea University"]
cities: ["Yongin", "Seoul"]
countries: ["Republic of Korea", "Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choe_Universal_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled synthetic data (source) to unlabeled real-world data (target). Traditional UDA-SS methods work on the assumption that the category settings between the source and target domains are known in advance. However, in real-world scenarios, the category settings of the source and target are unknown due to the lack of labels in the target, resulting in the existence of target-private or source-private classes. Traditional UDA-SS methods struggle with this change, leading to negative transfer and performance degradation. To address these issues, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS) for the first time to achieve good performance even when the category settings of source and target are unknown. We defined the problem in the UniDA-SS scenario as that the confidence score of common Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios. This leads to performance degradation if private private classes exist. To address this limitation, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS), achieving robust adaptation even without prior knowledge of category settings. We define the problem in the UniDA-SS scenario as low confidence scores of common classes in the target domain, which leads to confusion with private classes. To solve this problem, we propose UniMAP: UniDA-SS with Image Ma tching and Prototype-based Distinction, a novel framework composed of two key components. First, Domain-Specific Prototype-based Distinction (DSPD) divides each class into two domain-specific prototypes, enabling finer separation of domain-specific features and enhancing the identification of common classes across domains. Second, Target-based Image Matching (TIM) selects a source image containing the most common-class pixels based on the target pseudo-label and pairs it in a batch to promote effective learning of common classes. We also introduce a new UniDA-SS benchmark and demonstrate through various experiments that UniMAP significantly outperforms baselines. The code is available at https://github.com/KU-VGI/UniDA-SS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choe_Universal_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choe_Universal_Domain_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choe_2025_CVPR,
    author    = {Choe, Seun-An and Park, Keon-Hee and Choi, Jinwoo and Park, Gyeong-Moon},
    title     = {Universal Domain Adaptation for Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4607-4617}
}

raw_affiliation:
[{"affiliation": "Kyung Hee University", "city": "Yongin", "country": "Republic of Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "Republic of Korea"}]


title: HyperGS: Hyperspectral 3D Gaussian Splatting
authors: Christopher Thirgood, Oscar Mendez, Erin Ling, Jon Storey, Simon Hadfield
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5970-5979
affiliations: ["University of Surrey", "I3D Robotics"]
cities: ["Guildford", "Unknown"]
countries: ["UK", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Thirgood_HyperGS_Hyperspectral_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS), based on a new latent 3D Gaussian Splatting (3DGS) technique. Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets. HyperGS reconstructs high-fidelity views from arbitrary perspectives with improved accuracy and speed, outperforming currently existing methods. To address the challenges of high-dimensional data, we perform view synthesis in a learned latent space, incorporating a pixel-wise adaptive density function and a pruning technique for increased training stability and efficiency. Additionally, we introduce the first HNVS benchmark, implementing a number of new baselines based on recent SOTA RGB-NVS techniques, alongside the small number of prior works on HNVS. We demonstrate HyperGS's robustness through extensive evaluation of real and simulated hyperspectral scenes with a 14dB accuracy improvement upon previously published models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Thirgood_HyperGS_Hyperspectral_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Thirgood_HyperGS_Hyperspectral_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Thirgood_2025_CVPR,
    author    = {Thirgood, Christopher and Mendez, Oscar and Ling, Erin and Storey, Jon and Hadfield, Simon},
    title     = {HyperGS: Hyperspectral 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5970-5979}
}

raw_affiliation:
[{"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}, {"affiliation": "I3D Robotics", "city": "Unknown", "country": "Unknown"}]


title: Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30451-30461
affiliations: ["NUS", "CMU", "University of Toronto", "Independent Researcher"]
cities: ["Singapore", "Pittsburgh", "Toronto", "Unknown"]
countries: ["Singapore", "USA", "Canada", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Emphasizing_Discriminative_Features_for_Dataset_Distillation_in_Complex_Scenarios_CVPR_2025_paper.html

authors:
Kai Wang, Zekai Li, Zhi-Qi Cheng, Samir Khaki, Ahmad Sajedi, Ramakrishna Vedantam, Konstantinos N Plataniotis, Alexander Hauptmann, Yang You

abstract:
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features), a dataset distillation method that enhances key discriminative regions in synthetic images using Grad-CAM activation maps. Our approach is inspired by a key observation: in simple datasets, high-activation areas typically occupy most of the image, whereas in complex scenarios, the size of these areas is much smaller. Unlike previous methods that treat all pixels equally when synthesizing images, EDF uses Grad-CAM activation maps to enhance high-activation areas. From a supervision perspective, we downplay supervision signals produced by lower trajectory-matching losses, as they contain common patterns. Additionally, to help the DD community better explore complex scenarios, we build the Complex Dataset Distillation (Comp-DD) benchmark by meticulously selecting sixteen subsets, eight easy and eight hard, from ImageNet-1K. In particular, EDF consistently outperforms SOTA results in complex scenarios, such as ImageNet-1K subsets. Hopefully, more researchers will be inspired and encouraged to improve the practicality and efficacy of DD. Our code and benchmark have been made public at NUS-HPC-AI-Lab/EDF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Emphasizing_Discriminative_Features_for_Dataset_Distillation_in_Complex_Scenarios_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Emphasizing_Discriminative_Features_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Kai and Li, Zekai and Cheng, Zhi-Qi and Khaki, Samir and Sajedi, Ahmad and Vedantam, Ramakrishna and Plataniotis, Konstantinos N and Hauptmann, Alexander and You, Yang},
    title     = {Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30451-30461}
}

raw_affiliation:
[{"affiliation": "NUS", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CMU", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}]


title: LMO: Linear Mamba Operator for MRI Reconstruction
authors: Wei Li, Jiawei Jiang, Jie Wu, Kaihao Yu, Jianwei Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5112-5122
raw_affiliation: [{"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University of Technology"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LMO_Linear_Mamba_Operator_for_MRI_Reconstruction_CVPR_2025_paper.html

abstract:
Interpretability and consistency have long been crucial factors in MRI reconstruction. While interpretability has been significantly innovated with the emerging deep unfolding networks, current solutions still suffer from inconsistency issues and produce inferior anatomical structures. Especially in out-of-distribution cases, e.g., when the acceleration rate (AR) varies, the generalization performance is often catastrophic. To counteract the dilemma, we propose an innovative Linear Mamba Operator (LMO) to ensure consistency and generalization, while still enjoying desirable interpretability. Theoretically, we argue that mapping between function spaces, rather than between signal instances, provides a solid foundation of high generalization. Technically, LMO achieves a good balance between global integration facilitated by a state space model that scans the whole function domain, and local integration engaged with an appealing property of continuous-discrete equivalence. On that basis, learning holistic features can be guaranteed, tapping the potential of maximizing data consistency. Quantitative and qualitative results demonstrate that LMO significantly outperforms other state-of-the-arts. More importantly, LMO is the unique model that, with AR changed, achieves retraining performance without retraining steps. Codes are available at https://github.com/ZhengJianwei2/LMO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LMO_Linear_Mamba_Operator_for_MRI_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LMO_Linear_Mamba_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wei and Jiang, Jiawei and Wu, Jie and Yu, Kaihao and Zheng, Jianwei},
    title     = {LMO: Linear Mamba Operator for MRI Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5112-5122}
}


title: AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
authors: Ziming Huang, Xurui Li, Haotian Liu, Feng Xue, Yuzhe Wang, Yu Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4755-4765
cities: ["Wuhan", "Wuhan", "Wuhan", "Trento"]
countries: ["China", "China", "China", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_AnomalyNCD_Towards_Novel_Anomaly_Class_Discovery_in_Industrial_Scenarios_CVPR_2025_paper.html

abstract:
Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network compatible with different anomaly detection  methods. To address the non-prominence of anomalies, we design main element binarization (MEBin) to obtain anomaly-centered images, ensuring anomalies are learned while avoiding the impact of incorrect detections. Next, to learn anomalies with weak semantics, we design mask-guided representation learning, which focuses on isolated anomalies guided by masks and reduces confusion from erroneous inputs through corrected pseudo labels. Finally, to enable flexible classification at both region and image levels, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% F1 gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, and 12.8% F1 gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. Code is available at https://github.com/HUST-SLOW/AnomalyNCD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_AnomalyNCD_Towards_Novel_Anomaly_Class_Discovery_in_Industrial_Scenarios_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_AnomalyNCD_Towards_Novel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Ziming and Li, Xurui and Liu, Haotian and Xue, Feng and Wang, Yuzhe and Zhou, Yu},
    title     = {AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4755-4765}
}

raw_affiliation:
[{"affiliation": "School of Electronic Information and Communications, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Hubei Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Artificial Intelligence Research Institute, Wuhan JingCe Electronic Group Co., LTD", "city": "Wuhan", "country": "China"}, {"affiliation": "Department of Information Engineering and Computer Science, University of Trento", "city": "Trento", "country": "Italy"}]

affiliations:
["School of Electronic Information and Communications, Huazhong University of Science and Technology", "Hubei Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology", "Artificial Intelligence Research Institute, Wuhan JingCe Electronic Group Co., LTD", "Department of Information Engineering and Computer Science, University of Trento"]


title: Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
authors: Tanner Schmidt, Richard Newcombe
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29428-29437
affiliations: ["Meta Reality Labs", "Meta Reality Labs"]
cities: ["Menlo Park", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Schmidt_Segment_This_Thing_Foveated_Tokenization_for_Efficient_Point-Prompted_Segmentation_CVPR_2025_paper.html

abstract:
This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior work and increasing efficiency by decreasing model size, we gain efficiency by foveating input images. Given an image and a point prompt, we extract a crop centered on the prompt and apply a novel variable-resolution patch tokenization in which patches are downsampled at a rate that increases with increased distance from the prompt. This approach yields far fewer image tokens than uniform patch tokenization. As a result we can drastically reduce the computational cost of segmentation without reducing model size. Furthermore, the foveation focuses the model on the region of interest, a potentially useful inductive bias. We show that our Segment This Thing model is more efficient than prior work while remaining competitive on segmentation benchmarks. It can easily run at interactive frame rates on consumer hardware and is thus a promising tool for augmented reality or robotics applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Schmidt_Segment_This_Thing_Foveated_Tokenization_for_Efficient_Point-Prompted_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Schmidt_Segment_This_Thing_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Schmidt_2025_CVPR,
    author    = {Schmidt, Tanner and Newcombe, Richard},
    title     = {Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29428-29437}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}]


title: Task-Specific Gradient Adaptation for Few-Shot One-Class Classification
authors: Yunlong Li, Xiabi Liu, Liyuan Pan, Yuchen Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30556-30565
raw_affiliation: [{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]
affiliations: ["Beijing Institute of Technology"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Task-Specific_Gradient_Adaptation_for_Few-Shot_One-Class_Classification_CVPR_2025_paper.html

abstract:
Optimization-based meta-learning methods for few-shot one-class classification (FS-OCC) aim to fine-tune a meta-trained model to classify the positive and negative samples using only a few positive samples by adaptation. However, recent approaches primarily focus on adjusting existing meta-learning algorithms for FS-OCC, while overlooking issues stemming from the misalignment between the cross-entropy loss and OCC tasks during adaptation. This misalignment, combined with the limited availability of one-class samples and the restricted diversity of task-specific adaptation, can significantly exacerbate the adverse effects of gradient instability and generalization. To address these challenges, we propose a novel Task-Specific Gradient Adaptation (TSGA) for FS-OCC. Without extra supervision, TSGA learns to generate appropriate, stable gradients by leveraging label prediction and feature representation details of one-class samples and refines the adaptation process by recalibrating task-specific gradients and regularization terms. We evaluate TSGA on three challenging datasets and a real-world CNC Milling Machine application and demonstrate consistent improvements over baseline methods. Furthermore, we illustrate the critical impact of gradient instability and task-agnostic adaptation. Notably, TSGA achieves state-of-the-art results by effectively addressing these issues.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Task-Specific_Gradient_Adaptation_for_Few-Shot_One-Class_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Task-Specific_Gradient_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yunlong and Liu, Xiabi and Pan, Liyuan and Ren, Yuchen},
    title     = {Task-Specific Gradient Adaptation for Few-Shot One-Class Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30556-30565}
}


title: TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception
authors: Zhiying Song, Lei Yang, Fuxi Wen, Jun Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12048-12057
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_TraF-Align_Trajectory-aware_Feature_Alignment_for_Asynchronous_Multi-agent_Perception_CVPR_2025_paper.html

abstract:
Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter-agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real-time observations from the ego vehicle with delayed data from others. To address these issues, we propose TraF-Align, a novel framework that learns the flow path of features by predicting the feature-level trajectory of objects from past observations up to the ego vehicle's current time. By generating temporally ordered sampling points along these paths, TraF-Align directs attention from the current-time query to relevant historical features along each trajectory, supporting the reconstruction of current-time features and promoting semantic interaction across multiple frames. This approach corrects spatial misalignment and ensures semantic consistency across agents, effectively compensating for motion and achieving coherent feature fusion. Experiments on two real-world datasets, V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for asynchronous cooperative perception.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_TraF-Align_Trajectory-aware_Feature_Alignment_for_Asynchronous_Multi-agent_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_TraF-Align_Trajectory-aware_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Zhiying and Yang, Lei and Wen, Fuxi and Li, Jun},
    title     = {TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12048-12057}
}

raw_affiliation:
[{"affiliation": "School of Vehicle and Mobility, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Lab of Intelligent Green Vehicle and Mobility", "city": "Beijing", "country": "China"}]

affiliations:
["School of Vehicle and Mobility, Tsinghua University", "State Key Lab of Intelligent Green Vehicle and Mobility"]


title: DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching
authors: Emanuele Aiello, Umberto Michieli, Diego Valsesia, Mete Ozay, Enrico Magli
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12480-12489
affiliations: ["Politecnico di Torino", "Samsung R&D Institute UK"]
cities: ["Turin", "London"]
countries: ["Italy", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Aiello_DreamCache_Finetuning-Free_Lightweight_Personalized_Image_Generation_via_Feature_Caching_CVPR_2025_paper.html

abstract:
Personalized image generation requires text-to-image generative models that capture the core features of a reference subject to allow for controlled generation across different contexts. Existing methods face challenges due to complex training requirements, high inference costs, limited flexibility, or a combination of these issues. In this paper, we introduce DreamCache, a scalable approach for efficient and high-quality personalized image generation. By caching a small number of reference image features from a subset of layers and a single timestep of the pretrained diffusion denoiser, DreamCache enables dynamic modulation of the generated image features through lightweight, trained conditioning adapters. DreamCache achieves state-of-the-art image and text alignment, utilizing an order of magnitude fewer extra parameters, and is both more computationally effective and versatile than existing models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Aiello_DreamCache_Finetuning-Free_Lightweight_Personalized_Image_Generation_via_Feature_Caching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Aiello_DreamCache_Finetuning-Free_Lightweight_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Aiello_2025_CVPR,
    author    = {Aiello, Emanuele and Michieli, Umberto and Valsesia, Diego and Ozay, Mete and Magli, Enrico},
    title     = {DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12480-12489}
}

raw_affiliation:
[{"affiliation": "Politecnico di Torino", "city": "Turin", "country": "Italy"}, {"affiliation": "Samsung R&D Institute UK", "city": "London", "country": "UK"}]


title: 3D Gaussian Inpainting with Depth-Guided Cross-View Consistency
authors: Sheng-Yu Huang, Zi-Ting Chou, Yu-Chiang Frank Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26704-26713
affiliations: ["Graduate Institute of Communication Engineering, National Taiwan University", "NVIDIA"]
cities: ["Taipei", "Taipei"]
countries: ["Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_3D_Gaussian_Inpainting_with_Depth-Guided_Cross-View_Consistency_CVPR_2025_paper.html

abstract:
When performing 3D inpainting using novel-view rendering methods like Neural Radiance Field (NeRF) or 3D Gussian Splatting (3DGS), how to achieve texture and geometry consistency across camera views has been a challenge. In this paper, we propose a framework of 3D Gaussian Inpainting with Depth-Guided Cross-View Consistency (3DGIC) for cross-view consistent 3D inpainting. Guided by the rendered depth information from each training view, our 3DGIC exploits background pixels visible across different views for updating the inpainting mask, allowing us to refine the 3DGS for inpainting purposes. Through extensive experiments on benchmark datasets, we confirm that our 3DGIC outperforms current state-of-the-art 3D inpainting methods quantitatively and qualitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_3D_Gaussian_Inpainting_with_Depth-Guided_Cross-View_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_3D_Gaussian_Inpainting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Sheng-Yu and Chou, Zi-Ting and Wang, Yu-Chiang Frank},
    title     = {3D Gaussian Inpainting with Depth-Guided Cross-View Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26704-26713}
}

raw_affiliation:
[{"affiliation": "Graduate Institute of Communication Engineering, National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "NVIDIA", "city": "Taipei", "country": "Taiwan"}]


title: Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding
authors: Seil Kang, Jinyeong Kim, Junhyeok Kim, Seong Jae Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9339-9350
raw_affiliation: [{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Yonsei University"]
cities: ["Seoul"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_Your_Large_Vision-Language_Model_Only_Needs_A_Few_Attention_Heads_CVPR_2025_paper.html

abstract:
Visual grounding seeks to localize the image region corresponding to a free-form text description. Recently, the strong multimodal capabilities of Large Vision-Language Models (LVLMs) have driven substantial improvements in visual grounding, though they inevitably require fine-tuning and additional model components to explicitly generate bounding boxes or segmentation masks. However, we discover that a few attention heads in frozen LVLMs demonstrate strong visual grounding capabilities. We refer to these heads, which consistently capture object locations related to text semantics, as localization heads. Using localization heads, we introduce a straightforward and effective training-free visual grounding framework that utilizes text-to-image attention maps from localization heads to identify the target objects. Surprisingly, only three out of thousands of attention heads are sufficient to achieve competitive localization performance compared to existing LVLM-based visual grounding methods that require fine-tuning. Our findings suggest that LVLMs can innately ground objects based on a deep comprehension of the text-image relationship, as they implicitly focus on relevant image regions to generate informative text outputs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_Your_Large_Vision-Language_Model_Only_Needs_A_Few_Attention_Heads_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_Your_Large_Vision-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kang_2025_CVPR,
    author    = {Kang, Seil and Kim, Jinyeong and Kim, Junhyeok and Hwang, Seong Jae},
    title     = {Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9339-9350}
}


title: FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection
authors: Zhonghang Liu, Kun Zhou, Changshuo Wang, Wen-Yan Lin, Jiangbo Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15183-15193
affiliations: ["SMU", "SmartMore Corporation", "NTU", "SMU", "SmartMore Corporation"]
cities: ["Singapore", "Beijing", "Singapore", "Singapore", "Beijing"]
countries: ["Singapore", "China", "Singapore", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_FlexUOD_The_Answer_to_Real-world_Unsupervised_Image_Outlier_Detection_CVPR_2025_paper.html

abstract:
How many outliers are within an unlabeled and contaminated dataset? Despite a series of unsupervised outlier detection (UOD) approaches have been proposed, they cannot correctly answer this critical question, resulting in their performance instability across various real-world (varying contamination factor) scenarios. To address this problem, we propose FlexUOD, with a novel contamination factor estimation perspective. FlexUOD not only achieves its remarkable robustness but also is a general and plug-and-play framework, which can significantly improve the performance of existing UOD methods. Extensive experiments demonstrate that FlexUOD achieves state-of-the-art results as well as high efficacy on diverse evaluation benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_FlexUOD_The_Answer_to_Real-world_Unsupervised_Image_Outlier_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_FlexUOD_The_Answer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhonghang and Zhou, Kun and Wang, Changshuo and Lin, Wen-Yan and Lu, Jiangbo},
    title     = {FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15183-15193}
}

raw_affiliation:
[{"affiliation": "SMU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SmartMore Corporation", "city": "Beijing", "country": "China"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SMU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SmartMore Corporation", "city": "Beijing", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9017-9026
cities: ["Stony Brook", "Goa", "Singapore", "Singapore", "Unknown", "Cambridge", "Singapore"]
countries: ["USA", "India", "Singapore", "Singapore", "India", "USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mane_Ges3ViG__Incorporating_Pointing_Gestures_into_Language-Based_3D_Visual_Grounding_CVPR_2025_paper.html

title:
Ges3ViG : Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding

authors:
Atharv Mahesh Mane, Dulanga Weerakoon, Vigneshwaran Subbaraju, Sougata Sen, Sanjay E. Sarma, Archan Misra

abstract:
3-Dimensional Embodied Reference Understanding (3DERU) combines a language description and an accompanying pointing gesture to identify the most relevant target object in a 3D scene. Although prior work has explored pure language-based 3D grounding, there has been limited exploration of 3D-ERU, which also incorporates human pointing gestures. To address this gap, we introduce a data augmentation framework-Imputer, and use it to curate a new benchmark dataset-ImputeRefer for 3D-ERU, by incorporating human pointing gestures into existing 3D scene datasets that only contain language instructions. We also propose Ges3ViG, a novel model for 3D-ERU that achieves  30% improvement in accuracy as compared to other 3DERU models and  9% compared to other purely language-based 3D grounding models. Our code and dataset are available at https://github.com/AtharvMane/Ges3ViG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mane_Ges3ViG__Incorporating_Pointing_Gestures_into_Language-Based_3D_Visual_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mane_Ges3ViG__Incorporating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mane_2025_CVPR,
    author    = {Mane, Atharv Mahesh and Weerakoon, Dulanga and Subbaraju, Vigneshwaran and Sen, Sougata and Sarma, Sanjay E. and Misra, Archan},
    title     = {Ges3ViG : Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9017-9026}
}

raw_affiliation:
[{"affiliation": "Stony Brook University, USA", "city": "Stony Brook", "country": "USA"}, {"affiliation": "BITS Pilani Goa campus, India", "city": "Goa", "country": "India"}, {"affiliation": "Singapore-MIT Alliance for Research and Technology Centre, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute of High Performance Computing A*STAR, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "APPCAIR, India", "city": "Unknown", "country": "India"}, {"affiliation": "Massachusetts Institute of Technology, USA", "city": "Cambridge", "country": "USA"}, {"affiliation": "Singapore Management University, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Stony Brook University, USA", "BITS Pilani Goa campus, India", "Singapore-MIT Alliance for Research and Technology Centre, Singapore", "Institute of High Performance Computing A*STAR, Singapore", "APPCAIR, India", "Massachusetts Institute of Technology, USA", "Singapore Management University, Singapore"]


title: Focusing on Tracks for Online Multi-Object Tracking
authors: Kyujin Shim, Kangwook Ko, Yujin Yang, Changick Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11687-11696
affiliations: ["Korea Advanced Institute of Science and Technology (KAIST)"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shim_Focusing_on_Tracks_for_Online_Multi-Object_Tracking_CVPR_2025_paper.html

abstract:
Multi-object tracking (MOT) is a critical task in computer vision, requiring the accurate identification and continuous tracking of multiple objects across video frames. However, current state-of-the-art methods mainly rely on a global optimization technique and multi-stage cascade association strategy, and those approaches often overlook the specific characteristics of assignment task in MOT and useful detection results that may represent occluded objects. To address these challenges, we propose a novel Track-Focused Online Multi-Object Tracker (TrackTrack) with two key strategies: Track-Perspective-Based Association (TPA) and Track-Aware Initialization (TAI). The TPA strategy associates each track with the most suitable detection result by choosing the one with the minimum distance from all available detection results in a track-perspective manner. On the other hand, TAI precludes the generation of spurious tracks in the track-aware aspect by suppressing track initialization of detection results that heavily overlap with current active tracks and more confident detection results. Extensive experiments on MOT17, MOT20, and DanceTrack demonstrate that our TrackTrack outperforms current state-of-the-art trackers, offering improved robustness and accuracy across diverse and challenging tracking scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shim_Focusing_on_Tracks_for_Online_Multi-Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shim_Focusing_on_Tracks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shim_2025_CVPR,
    author    = {Shim, Kyujin and Ko, Kangwook and Yang, Yujin and Kim, Changick},
    title     = {Focusing on Tracks for Online Multi-Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11687-11696}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "South Korea"}]


title: Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22328-22337
affiliations: ["Robert Bosch GmbH", "University of Freiburg", "CARIAD SE"]
cities: ["Stuttgart", "Freiburg", "Wolfsburg"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hoffmann_Floxels_Fast_Unsupervised_Voxel_Based_Scene_Flow_Estimation_CVPR_2025_paper.html

authors:
David T. Hoffmann, Syed Haseeb Raza, Hanqiu Jiang, Denis Tananaev, Steffen Klingenhoefer, Martin Meinke

abstract:
Scene flow estimation is a foundational task for many robotic applications, including robust dynamic object detection, automatic labeling, and sensor synchronization. Two types of approaches to the problem have evolved: 1) Supervised and 2) optimization-based methods. Supervised methods are fast during inference and achieve high-quality results, however, they are limited by the need for large amounts of labeled training data and are susceptible to domain gaps. In contrast, unsupervised test-time optimization methods do not face the problem of domain gaps but usually suffer from substantial runtime, exhibit artifacts, or fail to converge to the right solution. In this work, we mitigate several limitations of existing optimization-based methods. To this end, we 1) introduce a simple voxel grid-based model that improves over the standard MLP-based formulation in multiple dimensions and 2) introduce a new multi-frame loss formulation. 3) We combine both contributions in our new method, termed Floxels. On the Argoverse 2 benchmark, Floxels is surpassed only by EulerFlow among unsupervised methods while achieving comparable performance at a fraction of the computational cost. Floxels achieves a massive speedup of more than  60-140x over EulerFlow, reducing the runtime from a day to 10 minutes per sequence. Over the faster but low-quality baseline, NSFP, Floxels achieves a speedup of  14x.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hoffmann_Floxels_Fast_Unsupervised_Voxel_Based_Scene_Flow_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hoffmann_Floxels_Fast_Unsupervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hoffmann_2025_CVPR,
    author    = {Hoffmann, David T. and Raza, Syed Haseeb and Jiang, Hanqiu and Tananaev, Denis and Klingenhoefer, Steffen and Meinke, Martin},
    title     = {Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22328-22337}
}

raw_affiliation:
[{"affiliation": "Robert Bosch GmbH", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "University of Freiburg", "city": "Freiburg", "country": "Germany"}, {"affiliation": "CARIAD SE", "city": "Wolfsburg", "country": "Germany"}]


title: LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale
authors: Joya Chen, Ziyun Zeng, Yiqi Lin, Wei Li, Zejun Ma, Mike Zheng Shou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29083-29095
affiliations: ["Show Lab, National University of Singapore", "Bytedance"]
cities: ["Singapore", "Beijing"]
countries: ["Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_LiveCC_Learning_Video_LLM_with_Streaming_Speech_Transcription_at_Scale_CVPR_2025_paper.html

abstract:
Recent video large language models (Video LLMs) often depend on costly human annotations or proprietary APIs (e.g., GPT-4o) to produce training data, which limits their training at scale. In this paper, we explore large-scale training for Video LLM with cheap automatic speech recognition (ASR) transcripts. Specifically, we propose a novel streaming training approach that densely interleaves the ASR words and video frames according to their timestamps. Compared to previous studies in vision-language representation with ASR, our method naturally fits the streaming characteristics of ASR, thus enabling the model to learn temporally-aligned, fine-grained vision-language modeling. To support the training algorithm, we introduce a data pipeline for YouTube videos and their closed captions (CC), resulting in \texttt Live-CC-10M  pre-training set and \texttt Live-WhisperX-408K  high-quality supervised fine-tuning (SFT) set. Remarkably, even without SFT, the pre-trained model \texttt LiveCC-7B  demonstrates significant improvements in general video QA and exhibits a new capability in real-time video commentary. To evaluate this, we carefully design a new benchmark \texttt LiveSports-3K , using LLM-as-a-judge to measure the free-form commentary. Experiments show our final model \texttt LiveCC-7B  can surpass LLaVA-Video-72B in commentary quality even working in a real-time mode. Meanwhile, it achieves state-of-the-art results at the 7B scale on popular benchmarks such as VideoMME, demonstrating its broad generalizability. All resources of this paper have been released at \href https://showlab.github.io/livecc  showlab.github.io/livecc .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_LiveCC_Learning_Video_LLM_with_Streaming_Speech_Transcription_at_Scale_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_LiveCC_Learning_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Joya and Zeng, Ziyun and Lin, Yiqi and Li, Wei and Ma, Zejun and Shou, Mike Zheng},
    title     = {LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29083-29095}
}

raw_affiliation:
[{"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Bytedance", "city": "Beijing", "country": "China"}]


title: Identity-preserving Distillation Sampling by Fixed-Point Iterator
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11115-11124
affiliations: ["Korea University", "Sookmyung Women\u2019s University", "KAIST"]
cities: ["Seoul", "Seoul", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Identity-preserving_Distillation_Sampling_by_Fixed-Point_Iterator_CVPR_2025_paper.html

authors:
SeonHwa Kim, Jiwon Kim, Soobin Park, Donghoon Ahn, Jiwon Kang, Seungryong Kim, Kyong Hwan Jin, Eunju Cha

abstract:
Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions. However, SDS often suffers from blurriness caused by noisy gradients. When SDS meets the image editing, such degradations can be reduced by adjusting bias shifts using reference pairs, but the de-biasing techniques are still corrupted by erroneous gradients. To this end, we introduce Identity-preserving Distillation Sampling (IDS), which compensates for the gradient leading to undesired changes in the results. Based on the analysis that these errors come from the text-conditioned scores, a new regularization technique, called fixed-point iterative regularization (FPR), is proposed to modify the score itself, driving the preservation of the identity even including poses and structures. Thanks to a self-correction by FPR, the proposed method provides clear and unambiguous representations corresponding to the given prompts in image-to-image editing and editable neural radiance field (NeRF). The structural consistency between the source and the edited data is obviously maintained compared to other state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Identity-preserving_Distillation_Sampling_by_Fixed-Point_Iterator_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Identity-preserving_Distillation_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, SeonHwa and Kim, Jiwon and Park, Soobin and Ahn, Donghoon and Kang, Jiwon and Kim, Seungryong and Jin, Kyong Hwan and Cha, Eunju},
    title     = {Identity-preserving Distillation Sampling by Fixed-Point Iterator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11115-11124}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Sookmyung Women\u2019s University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]


title: Progressive Focused Transformer for Single Image Super-Resolution
authors: Wei Long, Xingyu Zhou, Leheng Zhang, Shuhang Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2279-2288
affiliations: ["University of Electronic Science and Technology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Long_Progressive_Focused_Transformer_for_Single_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Transformer-based methods have achieved remarkable results in image super-resolution tasks because they can capture non-local dependencies in low-quality input images. However, this feature-intensive modeling approach is computationally expensive because it calculates the similarities between numerous features that are irrelevant to the query features when obtaining attention weights. These unnecessary similarity calculations not only degrade the reconstruction performance but also introduce significant computational overhead. How to accurately identify the features that are important to the current query features and avoid similarity calculations between irrelevant features remains an urgent problem. To address this issue, we propose a novel and effective Progressive Focused Transformer (PFT) that links all isolated attention maps in the network through Progressive Focused Attention (PFA) to focus attention on the most important tokens. PFA not only enables the network to capture more critical similar features, but also significantly reduces the computational cost of the overall network by filtering out irrelevant features before calculating similarities. Extensive experiments demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance on various single image super-resolution benchmarks.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Long_Progressive_Focused_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Long_2025_CVPR,
    author    = {Long, Wei and Zhou, Xingyu and Zhang, Leheng and Gu, Shuhang},
    title     = {Progressive Focused Transformer for Single Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2279-2288}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}]


title: VladVA: Discriminative Fine-tuning of LVLMs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4101-4111
affiliations: ["Samsung AI Cambridge", "Technical University of Iasi", "Queen Mary University of London"]
cities: ["Cambridge", "Iasi", "London"]
countries: ["UK", "Romania", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ouali_VladVA_Discriminative_Fine-tuning_of_LVLMs_CVPR_2025_paper.html

authors:
Yassine Ouali, Adrian Bulat, Alexandros Xenos, Anestis Zaganidis, Ioannis Maniadis Metaxas, Brais Martinez, Georgios Tzimiropoulos

abstract:
Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models have limited language understanding, often exhibiting a "bag of words" behavior. At the same time, Large Vision-Language Models (LVLMs), which combine vision encoders with LLMs, have been shown to be capable of detailed vision-language reasoning, yet their autoregressive nature renders them less suitable for discriminative tasks. In this work, we propose to combine "the best of both worlds": a new training approach for discriminative fine-tuning of LVLMs that results in strong discriminative and compositional capabilities. Essentially, our approach converts a generative LVLM into a discriminative one, unlocking its capability for powerful image-text discrimination combined with enhanced language understanding. Our contributions include (1) A carefully designed training/optimization framework that utilizes image-text pairs of variable length and granularity for training the model with both contrastive and next-token prediction losses. This is accompanied by ablation studies that justify the necessity of our framework's components. (2) A parameter-efficient adaptation method using a combination of soft prompting and LoRA adapters. (3) Significant improvements over state-of-the-art CLIP-like models of similar size, including standard image-text retrieval benchmarks and notable gains in compositionality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ouali_VladVA_Discriminative_Fine-tuning_of_LVLMs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ouali_VladVA_Discriminative_Fine-tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ouali_2025_CVPR,
    author    = {Ouali, Yassine and Bulat, Adrian and Xenos, Alexandros and Zaganidis, Anestis and Metaxas, Ioannis Maniadis and Martinez, Brais and Tzimiropoulos, Georgios},
    title     = {VladVA: Discriminative Fine-tuning of LVLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4101-4111}
}

raw_affiliation:
[{"affiliation": "Samsung AI Cambridge", "city": "Cambridge", "country": "UK"}, {"affiliation": "Technical University of Iasi", "city": "Iasi", "country": "Romania"}, {"affiliation": "Queen Mary University of London", "city": "London", "country": "UK"}]


title: FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28316-28326
raw_affiliation: [{"affiliation": "unknown", "city": "unknown", "country": "unknown"}]
affiliations: ["unknown"]
cities: ["unknown"]
countries: ["unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Anagnostidis_FlexiDiT_Your_Diffusion_Transformer_Can_Easily_Generate_High-Quality_Samples_with_CVPR_2025_paper.html

authors:
Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim, Jonas Kohler, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Albert Pumarola, Ali Thabet, Edgar Schönfeld

abstract:
Despite their remarkable performance, modern Diffusion Transformers (DiTs) are hindered by substantial resource requirements during inference, stemming from the fixed and large amount of compute needed for each denoising step. In this work, we revisit the conventional static paradigm that allocates a fixed compute budget per denoising iteration and propose a dynamic strategy instead. Our simple and sample-efficient framework enables pre-trained DiT models to be converted into flexible ones --- dubbed FlexiDiT --- allowing them to process inputs at varying compute budgets. We demonstrate how a single flexible model can generate images without any drop in quality, while reducing the required FLOPs by more than 40% compared to their static counterparts, for both class-conditioned and text-conditioned image generation. Our method is general and agnostic to input and conditioning modalities. We show how our approach can be readily extended for video generation, where FlexiDiT models generate samples with up to 75% less compute without compromising performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Anagnostidis_FlexiDiT_Your_Diffusion_Transformer_Can_Easily_Generate_High-Quality_Samples_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Anagnostidis_FlexiDiT_Your_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Anagnostidis_2025_CVPR,
    author    = {Anagnostidis, Sotiris and Bachmann, Gregor and Kim, Yeongmin and Kohler, Jonas and Georgopoulos, Markos and Sanakoyeu, Artsiom and Du, Yuming and Pumarola, Albert and Thabet, Ali and Sch\"onfeld, Edgar},
    title     = {FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28316-28326}
}


title: WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild
authors: Rolandos Alexandros Potamias, Jinglei Zhang, Jiankang Deng, Stefanos Zafeiriou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12242-12254
affiliations: ["Imperial College London", "Shanghai Jiao Tong University"]
cities: ["London", "Shanghai"]
countries: ["UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Potamias_WiLoR_End-to-end_3D_Hand_Localization_and_Reconstruction_in-the-wild_CVPR_2025_paper.html

abstract:
In recent years, 3D hand pose estimation methods have garnered significant attention due to their extensive applications in human-computer interaction, virtual reality, and robotics. In contrast, there has been a notable gap in hand detection pipelines, posing significant challenges in constructing effective real-world multi-hand reconstruction systems. In this work, we present a data-driven pipeline for efficient multi-hand reconstruction in the wild. The proposed pipeline is composed of two components: a real-time fully convolutional hand localization and a high-fidelity transformer-based 3D hand reconstruction model. To tackle the limitations of previous methods and build a robust and stable detection network, we introduce a large-scale dataset with over than 2M in-the-wild hand images with diverse lighting, illumination, and occlusion conditions. Our approach outperforms previous methods in both efficiency and accuracy on popular 2D and 3D benchmarks. Finally, we showcase the effectiveness of our pipeline to achieve smooth 3D hand tracking from monocular videos, without utilizing any temporal components. Code, models, and dataset are available at https://rolpotamias.github.io/WiLoR

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Potamias_WiLoR_End-to-end_3D_Hand_Localization_and_Reconstruction_in-the-wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Potamias_WiLoR_End-to-end_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Potamias_2025_CVPR,
    author    = {Potamias, Rolandos Alexandros and Zhang, Jinglei and Deng, Jiankang and Zafeiriou, Stefanos},
    title     = {WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12242-12254}
}

raw_affiliation:
[{"affiliation": "Imperial College London", "city": "London", "country": "UK"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: HumanMM: Global Human Motion Recovery from Multi-shot Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1973-1983
cities: ["Beijing", "Unknown", "Baltimore", "Chicago", "Hong Kong", "Hong Kong"]
countries: ["China", "Unknown", "USA", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_HumanMM_Global_Human_Motion_Recovery_from_Multi-shot_Videos_CVPR_2025_paper.html

authors:
Yuhong Zhang, Guanlin Wu, Ling-Hao Chen, Zhuokai Zhao, Jing Lin, Xiaoke Jiang, Jiamin Wu, Zhuoheng Li, Hao Frank Yang, Haoqian Wang, Lei Zhang

abstract:
In this paper, we present a novel framework designed to reconstruct long-sequence 3D human motion in the world coordinates from in-the-wild videos with multiple shot transitions. Such long-sequence in-the-wild motions are highly valuable to applications such as motion generation and motion understanding, but are of great challenge to be recovered due to abrupt shot transitions, partial occlusions, and dynamic backgrounds presented in such videos. Existing methods primarily focus on single-shot videos, where continuity is maintained within a single camera view, or simplify multi-shot alignment in camera space only. In this work, we tackle the challenges by integrating an enhanced camera pose estimation with Human Motion Recovery (HMR) by incorporating a shot transition detector and a robust alignment module for accurate pose and orientation continuity across shots. By leveraging a custom motion integrator, we effectively mitigate the problem of foot sliding and ensure temporal consistency in human pose. Extensive evaluations on our created multi-shot dataset from public 3D human datasets demonstrate the robustness of our method in reconstructing realistic human motion in world coordinates.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_HumanMM_Global_Human_Motion_Recovery_from_Multi-shot_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_HumanMM_Global_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yuhong and Wu, Guanlin and Chen, Ling-Hao and Zhao, Zhuokai and Lin, Jing and Jiang, Xiaoke and Wu, Jiamin and Li, Zhuoheng and Yang, Hao Frank and Wang, Haoqian and Zhang, Lei},
    title     = {HumanMM: Global Human Motion Recovery from Multi-shot Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1973-1983}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "IDEA Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "University of Chicago", "city": "Chicago", "country": "USA"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}]

affiliations:
["Tsinghua University", "IDEA Research", "Johns Hopkins University", "University of Chicago", "HKUST", "HKU"]


title: Removing Reflections from RAW Photos
authors: Eric Kee, Adam Pikielny, Kevin Blackburn-Matzen, Marc Levoy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 161-171
raw_affiliation: [{"affiliation": "Adobe Inc.", "city": "San Jose", "country": "USA"}]
affiliations: ["Adobe Inc."]
cities: ["San Jose"]
countries: ["USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kee_Removing_Reflections_from_RAW_Photos_CVPR_2025_paper.html

abstract:
We describe a system to remove real-world reflections from images for consumer photography. Our system operates on linear (RAW) photos, and accepts an optional contextual photo looking in the opposite direction (e.g., the "selfie" camera on a mobile device). This optional photo disambiguates what should be considered the reflection. The system is trained solely on synthetic mixtures of real RAW photos, which we combine using a reflection simulation that is photometrically and geometrically accurate. Our system comprises a base model that accepts the captured photo and optional context photo as input, and runs at 256p, followed by an up-sampling model that transforms 256p images to full resolution. The system produces preview images at 1K in 4.5-6.5s on a MacBook or iPhone 14 Pro. We show SOTA results on RAW photos that were captured in the field to embody typical consumer photos, and show that training on RAW simulation data improves performance more than the architectural variations among prior works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kee_Removing_Reflections_from_RAW_Photos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kee_Removing_Reflections_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kee_2025_CVPR,
    author    = {Kee, Eric and Pikielny, Adam and Blackburn-Matzen, Kevin and Levoy, Marc},
    title     = {Removing Reflections from RAW Photos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {161-171}
}


title: BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
authors: Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14766-14776
raw_affiliation: [{"affiliation": "Concordia University", "city": "Montreal", "country": "Canada"}]
affiliations: ["Concordia University"]
cities: ["Montreal"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Koleilat_BiomedCoOp_Learning_to_Prompt_for_Biomedical_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream applications remains challenging, as their accuracy often depends on time-intensive and expertise-demanding prompt engineering, while full model fine-tuning is costly. This is particularly true for biomedical images, which, unlike natural images, typically suffer from limited annotated datasets, unintuitive image contrasts, and nuanced visual features. Recent prompt learning techniques, such as Context Optimization (CoOp) intend to tackle these issues, but still fall short in generalizability. Meanwhile, explorations in prompt learning for biomedical image analysis are still highly limited. In this work, we propose BiomedCoOp, a novel prompt learning framework that enables efficient adaptation of BiomedCLIP for accurate and highly generalizable few-shot biomedical image classification. Our approach achieves effective prompt context learning by leveraging semantic consistency with average prompt ensembles from Large Language Models (LLMs) and knowledge distillation with a statistics-based prompt selection strategy. We conducted comprehensive validation of our proposed framework on 11 medical datasets across 9 modalities and 10 organs against existing state-of-the-art methods, demonstrating significant improvements in both accuracy and generalizability. The code is publicly available at https://github.com/HealthX-Lab/BiomedCoOp.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Koleilat_BiomedCoOp_Learning_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Koleilat_2025_CVPR,
    author    = {Koleilat, Taha and Asgariandehkordi, Hojat and Rivaz, Hassan and Xiao, Yiming},
    title     = {BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14766-14776}
}


title: AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation
authors: Zeyi Xu, Jinfan Liu, Kuangxu Chen, Ye Chen, Zhangli Hu, Bingbing Ni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5804-5813
affiliations: ["Shanghai Jiao Tong University", "Shanghai University", "Shenzhen Technology University"]
cities: ["Shanghai", "Shanghai", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_AMR-Transformer_Enabling_Efficient_Long-range_Interaction_for_Complex_Neural_Fluid_Simulation_CVPR_2025_paper.html

abstract:
Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-range dependencies due to limited receptive fields, and Transformer-based models, while providing global context, incur prohibitive computational costs. To tackle these challenges, we propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline that integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module. This design encourages long-range interactions between simulation cells and facilitates the modeling of global fluid wave patterns, such as turbulence and shockwaves. Experiments show that our approach achieves significant gains in efficiency while preserving critical details, making it suitable for high-resolution physical simulations with long-range dependencies. On CFDBench, PDEBench and a new shockwave dataset, our pipeline demonstrates up to an order-of-magnitude improvement in accuracy over baseline models. Additionally, compared to ViT, our approach achieves a reduction in FLOPs of up to 60 times.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_AMR-Transformer_Enabling_Efficient_Long-range_Interaction_for_Complex_Neural_Fluid_Simulation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zeyi and Liu, Jinfan and Chen, Kuangxu and Chen, Ye and Hu, Zhangli and Ni, Bingbing},
    title     = {AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5804-5813}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shenzhen Technology University", "city": "Shenzhen", "country": "China"}]


title: MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation
authors: Aviral Chharia, Wenbo Gou, Haoye Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11590-11599
affiliations: ["Carnegie Mellon University", "National University of Singapore"]
cities: ["Pittsburgh", "Singapore"]
countries: ["USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chharia_MV-SSM_Multi-View_State_Space_Modeling_for_3D_Human_Pose_Estimation_CVPR_2025_paper.html

abstract:
While significant progress has been made in single-view 3D human pose estimation, multi-view 3D human pose estimation remains challenging, particularly in terms of generalizing to new camera configurations. Existing attention-based transformers often struggle to accurately model the spatial arrangement of keypoints, especially in occluded scenarios. Additionally, they tend to overfit specific camera arrangements and visual scenes from training data, resulting in substantial performance drops in new settings. In this study, we introduce a novel Multi-View State Space Modeling framework, named MV-SSM, for robustly estimating 3D human keypoints. We explicitly model the joint spatial sequence at two distinct levels: the feature level from multi-view images and the person keypoint level. We propose a Projective State Space (PSS) block to learn a generalized representation of joint spatial arrangements using state space modeling. Moreover, we modify Mamba's traditional scanning into an effective Grid Token-guided Bidirectional Scanning (GTBS), which is integral to the PSS block. Multiple experiments demonstrate that MV-SSM achieves strong generalization, outperforming state-of-the-art methods: +10.8 on AP25 on the challenging three-camera setting in CMU Panoptic, +7.0 on AP25 on varying camera arrangements, and +15.3 PCP on Campus A1 in cross-dataset evaluations. Project Website: https://aviralchharia.github.io/MV-SSM

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chharia_MV-SSM_Multi-View_State_Space_Modeling_for_3D_Human_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chharia_MV-SSM_Multi-View_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chharia_2025_CVPR,
    author    = {Chharia, Aviral and Gou, Wenbo and Dong, Haoye},
    title     = {MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11590-11599}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation
authors: Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren, Alper Yilmaz, Khoa Luu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29150-29160
affiliations: ["University of Arkansas", "Ohio State University"]
cities: ["Fayetteville", "Columbus"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_HyperGLM_HyperGraph_for_Video_Scene_Graph_Generation_and_Anticipation_CVPR_2025_paper.html

abstract:
Multimodal LLMs have advanced vision-language tasks but still struggle with understanding video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to capture multi-object relationships across video frames. However, prior methods rely on pairwise connections, limiting their ability to handle complex multi-object interactions and reasoning. To this end, we propose Multimodal LLMs on a Scene HyperGraph (HyperGLM), promoting reasoning about multi-way interactions and higher-order relationships. Our approach uniquely integrates entity scene graphs, which capture spatial relationships between objects, with a procedural graph that models their causal transitions, forming a unified HyperGraph. Significantly, HyperGLM enables reasoning by injecting this unified HyperGraph into LLMs. Additionally, we introduce a new Video Scene Graph Reasoning (VSGR) dataset featuring 1.9M frames from third-person, egocentric, and drone views and supports five tasks: Scene Graph Generation, Scene Graph Anticipation, Video Question Answering, Video Captioning, and Relation Reasoning. Empirically, HyperGLM consistently outperforms state-of-the-art methods across five tasks, effectively modeling and reasoning complex relationships in diverse video scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_HyperGLM_HyperGraph_for_Video_Scene_Graph_Generation_and_Anticipation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Trong-Thuan and Nguyen, Pha and Cothren, Jackson and Yilmaz, Alper and Luu, Khoa},
    title     = {HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29150-29160}
}

raw_affiliation:
[{"affiliation": "University of Arkansas", "city": "Fayetteville", "country": "USA"}, {"affiliation": "Ohio State University", "city": "Columbus", "country": "USA"}]


title: AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
authors: Guillaume Astruc, Nicolas Gonthier, Clément Mallet, Loic Landrieu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19530-19540
cities: ["Paris", "Paris", "Paris", "Paris"]
countries: ["France", "France", "France", "France"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Astruc_AnySat_One_Earth_Observation_Model_for_Many_Resolutions_Scales_and_CVPR_2025_paper.html

abstract:
Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and scale-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner. To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of 5 multimodal datasets with varying characteristics and 11 distinct sensors. We then train a single powerful model on these diverse datasets simultaneously. Once fine-tuned or probed, we achieve state-of-the-art results on the test sets of GeoPlex and for 6 external datasets across various environment monitoring tasks: land cover mapping, tree species identification, crop type classification, change detection, climate type classification, and segmentation of flood, burn scar, and deforestation. Our code and models are available at https://github.com/gastruc/AnySat.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Astruc_AnySat_One_Earth_Observation_Model_for_Many_Resolutions_Scales_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Astruc_AnySat_One_Earth_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Astruc_2025_CVPR,
    author    = {Astruc, Guillaume and Gonthier, Nicolas and Mallet, Cl\'ement and Landrieu, Loic},
    title     = {AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19530-19540}
}

raw_affiliation:
[{"affiliation": "LASTIG, Univ Gustave Eiffel, IGN, ENSG, France", "city": "Paris", "country": "France"}, {"affiliation": "IGN, France", "city": "Paris", "country": "France"}, {"affiliation": "CNES, France", "city": "Paris", "country": "France"}, {"affiliation": "LIGM, Ecole Nationale des Ponts et Chauss\u00b4ees, IP Paris, Univ Gustave Eiffel, CNRS, France", "city": "Paris", "country": "France"}]

affiliations:
["LASTIG, Univ Gustave Eiffel, IGN, ENSG, France", "IGN, France", "CNES, France", "LIGM, Ecole Nationale des Ponts et Chauss\u00b4ees, IP Paris, Univ Gustave Eiffel, CNRS, France"]


authors: Gaojian Wang, Feng Lin, Tong Wu, Zhenguang Liu, Zhongjie Ba, Kui Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24364-24376
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_FSFM_A_Generalizable_Face_Security_Foundation_Model_via_Self-Supervised_Facial_CVPR_2025_paper.html

title:
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning

abstract:
This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks with respect to generalization performance? We make the first attempt and propose a self-supervised pretraining framework to learn fundamental representations of real face images, FSFM, that leverages the synergy between masked image modeling (MIM) and instance discrimination (ID). We explore various facial masking strategies for MIM and present a simple yet powerful CRFR-P masking, which explicitly forces the model to capture meaningful intra-region Consistency and challenging inter-region Coherency. Furthermore, we devise an ID network that naturally couples with MIM to establish underlying local-to-global Correspondence through tailored self-distillation. These three learning objectives, namely 3C, empower encoding both local features and global semantics of real faces. After pretraining, a vanilla ViT serves as a universal vision Foundation Model for downstream Face Security tasks: cross-dataset deepfake detection, cross-domain face anti-spoofing, and unseen diffusion facial forgery detection. Extensive experiments on 10 public datasets demonstrate that our model transfers better than supervised pretraining, visual and facial self-supervised learning arts, and even outperforms task-specialized SOTA methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_FSFM_A_Generalizable_Face_Security_Foundation_Model_via_Self-Supervised_Facial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_FSFM_A_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Gaojian and Lin, Feng and Wu, Tong and Liu, Zhenguang and Ba, Zhongjie and Ren, Kui},
    title     = {FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24364-24376}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Blockchain and Data Security, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security", "city": "Hangzhou", "country": "China"}]

affiliations:
["State Key Laboratory of Blockchain and Data Security, Zhejiang University", "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"]


title: OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18902-18913
cities: ["Shanghai", "Beijing", "Beijing", "Beijing", "Hong Kong", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Niu_OVO-Bench_How_Far_is_Your_Video-LLMs_from_Real-World_Online_Video_CVPR_2025_paper.html

authors:
Junbo Niu, Yifei Li, Ziyang Miao, Chunjiang Ge, Yuanhang Zhou, Qihao He, Xiaoyi Dong, Haodong Duan, Shuangrui Ding, Rui Qian, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang

abstract:
Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline models, which rely on complete videos for static, post hoc analysis, online models process video streams incrementally and dynamically adapt their responses based on the timestamp at which the question is posed. Despite its significance, temporal awareness has not been adequately evaluated in existing benchmarks. To fill this gap, we present OVO-Bench (Online-VideO-Benchmark), a novel video benchmark that emphasizes the importance of timestamps for advanced online video understanding capability benchmarking. OVO-Bench evaluates the ability of video LLMs to reason and respond to events occurring at specific timestamps under three distinct scenarios: (1) Backward tracing: trace back to past events to answer the question. (2) Real-time understanding: understand and respond to events as they unfold at the current timestamp. (3) Forward active responding: delay the response until sufficient future information becomes available to answer the question accurately. OVO-Bench comprises 12 tasks, featuring 644 unique videos and approximately human-curated 2,800 fine-grained meta-annotations with precise timestamps. We combine automated generation pipelines with human curation. With these high-quality samples, we further developed an evaluation pipeline to systematically query video LLMs along the video timeline. Evaluations of nine Video-LLMs reveal that, despite advancements on traditional benchmarks, current models struggle with online video understanding, showing a significant gap compared to human agents. We hope OVO-Bench will drive progress in video LLMs and inspire future research in online video reasoning. Our benchmark and code can be accessed at https://joeleelyf.github.io/OVO-Bench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Niu_OVO-Bench_How_Far_is_Your_Video-LLMs_from_Real-World_Online_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Niu_OVO-Bench_How_Far_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Niu_2025_CVPR,
    author    = {Niu, Junbo and Li, Yifei and Miao, Ziyang and Ge, Chunjiang and Zhou, Yuanhang and He, Qihao and Dong, Xiaoyi and Duan, Haodong and Ding, Shuangrui and Qian, Rui and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and He, Conghui and Wang, Jiaqi},
    title     = {OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18902-18913}
}

raw_affiliation:
[{"affiliation": "Shanghai Arti\ufb01cial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Communication University of China", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "SenseTime Group", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Arti\ufb01cial Intelligence Laboratory", "Tsinghua University", "Beihang University", "Communication University of China", "The Chinese University of Hong Kong", "SenseTime Group", "Shanghai Innovation Institute"]


title: AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment
authors: Yan Li, Yifei Xing, Xiangyuan Lan, Xin Li, Haifeng Chen, Dongmei Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24774-24784
cities: ["Shenzhen", "Xi\u2019an", "Xi\u2019an"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_AlignMamba_Enhancing_Multimodal_Mamba_with_Local_and_Global_Cross-modal_Alignment_CVPR_2025_paper.html

abstract:
Cross-modal alignment is crucial for multimodal representation fusion due to the inherent heterogeneity between modalities. While Transformer-based methods have shown promising results in modeling inter-modal relationships, their quadratic computational complexity limits their applicability to long-sequence or large-scale data. Although recent Mamba-based approaches achieve linear complexity, their sequential scanning mechanism poses fundamental challenges in comprehensively modeling cross-modal relationships. To address this limitation, we propose AlignMamba, an efficient and effective method for multimodal fusion. Specifically, grounded in Optimal Transport, we introduce a local cross-modal alignment module that explicitly learns token-level correspondences between different modalities. Moreover, we propose a global cross-modal alignment loss based on Maximum Mean Discrepancy to implicitly enforce the consistency between different modal distributions. Finally, the unimodal representations after local and global alignment are passed to the Mamba backbone for further cross-modal interaction and multimodal fusion. Extensive experiments on complete and incomplete multimodal fusion tasks demonstrate the effectiveness and efficiency of the proposed method. For instance, on the CMU-MOSI dataset, AlignMamba improves classification accuracy by 0.9%, reduces GPU memory usage by 20.3%, and decreases inference time by 83.3%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_AlignMamba_Enhancing_Multimodal_Mamba_with_Local_and_Global_Cross-modal_Alignment_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yan and Xing, Yifei and Lan, Xiangyuan and Li, Xin and Chen, Haifeng and Jiang, Dongmei},
    title     = {AlignMamba: Enhancing Multimodal Mamba with Local and Global Cross-modal Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24774-24784}
}

raw_affiliation:
[{"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi\u2019an", "country": "China"}, {"affiliation": "Shaanxi University of Science and Technology", "city": "Xi\u2019an", "country": "China"}]

affiliations:
["Pengcheng Laboratory", "Northwestern Polytechnical University", "Shaanxi University of Science and Technology"]


title: Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries
authors: Wei Xu, Charles James Wagner, Junjie Luo, Qi Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 432-441
affiliations: ["Elmore Family School of Electrical and Computer Engineering, Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Blurry-Edges_Photon-Limited_Depth_Estimation_from_Defocused_Boundaries_CVPR_2025_paper.html

abstract:
Extracting depth information from photon-limited, defocused images is challenging because depth from defocus (DfD) relies on accurate estimation of defocus blur, which is fundamentally sensitive to image noise. We present a novel approach to robustly measure object depths from photon-limited images along the defocused boundaries. It is based on a new image patch representation, Blurry-Edges, that explicitly stores and visualizes a rich set of low-level patch information, including boundaries, color, and smoothness. We develop a deep neural network architecture that predicts the Blurry-Edges representation from a pair of differently defocused images, from which depth can be calculated using a closed-form DfD relation we derive. The experimental results on synthetic and real data show that our method achieves the highest depth estimation accuracy on photon-limited images compared to a broad range of state-of-the-art DfD methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Blurry-Edges_Photon-Limited_Depth_Estimation_from_Defocused_Boundaries_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Blurry-Edges_Photon-Limited_Depth_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Wei and Wagner, Charles James and Luo, Junjie and Guo, Qi},
    title     = {Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {432-441}
}

raw_affiliation:
[{"affiliation": "Elmore Family School of Electrical and Computer Engineering, Purdue University", "city": "West Lafayette", "country": "USA"}]


