# papers

title: Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)
authors: Tomer Garber, Tom Tirer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2398-2407
affiliations: ["Bar-Ilan University, Israel", "Bar-Ilan University, Israel"]
cities: ["Ramat Gan", "Ramat Gan"]
countries: ["Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Garber_Zero-Shot_Image_Restoration_Using_Few-Step_Guidance_of_Consistency_Models_and_CVPR_2025_paper.html

abstract:
In recent years, it has become popular to tackle image restoration tasks with a single pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated deep neural network per task. However, such "zero-shot" restoration schemes currently require many Neural Function Evaluations (NFEs) for performing well, which may be attributed to the many NFEs needed in the original generative functionality of the DMs. Recently, faster variants of DMs have been explored for image generation. These include Consistency Models (CMs), which can generate samples via a couple of NFEs. However, existing works that use guided CMs for restoration still require tens of NFEs or fine-tuning of the model per task that leads to performance drop if the assumptions during the fine-tuning are not accurate. In this paper, we propose a zero-shot restoration scheme that uses CMs and operates well with as little as 4 NFEs. It is based on a wise combination of several ingredients: better initialization, back-projection guidance, and above all a novel noise injection mechanism. We demonstrate the advantages of our approach for image super-resolution and inpainting. Interestingly, we show that the usefulness of our noise injection technique goes beyond CMs: it can also mitigate the performance degradation of existing guided DM methods when reducing their NFE count.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Garber_Zero-Shot_Image_Restoration_Using_Few-Step_Guidance_of_Consistency_Models_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garber_Zero-Shot_Image_Restoration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Garber_2025_CVPR,
    author    = {Garber, Tomer and Tirer, Tom},
    title     = {Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2398-2407}
}

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


title: Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking
authors: Chaocan Xue, Bineng Zhong, Qihua Liang, Yaozong Zheng, Ning Li, Yuanliang Xue, Shuxiang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6730-6740
cities: ["Guilin", "Xi'an"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Similarity-Guided_Layer-Adaptive_Vision_Transformer_for_UAV_Tracking_CVPR_2025_paper.html

abstract:
Vision transformers (ViTs) have emerged as a popular backbone for visual tracking. However, complete ViT architectures are too cumbersome to deploy for unmanned aerial vehicle (UAV) tracking which extremely emphasizes efficiency. In this study, we discover that many layers within lightweight ViT-based trackers tend to learn relatively redundant and repetitive target representations. Based on this observation, we propose a similarity-guided layer adaptation approach to optimize the structure of ViTs. Our approach dynamically disables a large number of representation-similar layers and selectively retains only a single optimal layer among them, aiming to achieve a better accuracy-speed trade-off. By incorporating this approach into existing ViTs, we tailor previously complete ViT architectures into an efficient similarity-guided layer-adaptive framework, namely SGLATrack, for real-time UAV tracking. Extensive experiments on six tracking benchmarks verify the effectiveness of the proposed approach, and show that our SGLATrack achieves a state-of-the-art real-time speed while maintaining competitive tracking precision. Codes and models are available at https://github.com/GXNU-ZhongLab/SGLATrack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Similarity-Guided_Layer-Adaptive_Vision_Transformer_for_UAV_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Chaocan and Zhong, Bineng and Liang, Qihua and Zheng, Yaozong and Li, Ning and Xue, Yuanliang and Song, Shuxiang},
    title     = {Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6730-6740}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China", "city": "Guilin", "country": "China"}, {"affiliation": "Xi\u2019an Research Institute of High Technology, Xi\u2019an 710025, China", "city": "Xi'an", "country": "China"}]

affiliations:
["Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China", "Xi\u2019an Research Institute of High Technology, Xi\u2019an 710025, China"]


authors: Chuanfu Shen, Rui Wang, Lixin Duan, Shiqi Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6627-6636
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.html

title:
LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition

abstract:
Point clouds have gained growing interest in gait recognition. However, current methods, which typically convert point clouds into 3D voxels, often fail to extract essential gait-specific features.  In this paper, we explore gait recognition within 3D point clouds from the perspectives of architectural designs and gait representation modeling. We indicate the significance of local and body size features in 3D gait recognition and introduce LidarGait++, a novel framework combining advanced local representation learning techniques with a novel size-aware learning mechanism. Specifically, LidarGait++ utilizes Set Abstraction (SA) layer and Pyramid Point Pooling (P^3) layer for learning locally fine-grained gait representations from 3D point clouds directly. Both the SA and P^3 layers can be further enhanced with size-aware learning to make the model aware of the actual size of the subjects. In the end, LidarGait++ not only outperforms current state-of-the-art methods, but it also consistently demonstrates robust performance and great generalizability on two benchmarks. Our extensive experiments validate the effectiveness of size and local features in 3D gait recognition.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_LidarGait_Learning_Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Chuanfu and Wang, Rui and Duan, Lixin and Yu, Shiqi},
    title     = {LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6627-6636}
}

raw_affiliation:
[{"affiliation": "Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China", "Southern University of Science and Technology"]


title: UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation
authors: Yichong Lu, Yichi Cai, Shangzhan Zhang, Hongyu Zhou, Haoji Hu, Huimin Yu, Andreas Geiger, Yiyi Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27519-27530
affiliations: ["Zhejiang University", "University of T \u00a8ubingen", "T \u00a8ubingen AI Center"]
cities: ["Hangzhou", "T\u00fcbingen", "T\u00fcbingen"]
countries: ["China", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_UrbanCAD_Towards_Highly_Controllable_and_Photorealistic_3D_Vehicles_for_Urban_CVPR_2025_paper.html

abstract:
Photorealistic 3D vehicle models with high controllability are essential for autonomous driving simulation and data augmentation. While handcrafted CAD models provide flexible controllability, free CAD libraries often lack the high-quality materials necessary for photorealistic rendering. Conversely, reconstructed 3D models offer high-fidelity rendering but lack controllability. In this work, we introduce UrbanCAD, a framework that generates highly controllable and photorealistic 3D vehicle digital twins from a single urban image, leveraging a large collection of free 3D CAD models and handcrafted materials. To achieve this, we propose a novel pipeline that follows a retrieval-optimization manner, adapting to observational data while preserving fine-grained expert-designed priors for both geometry and material. This enables vehicles' realistic 360-degree rendering, background insertion, material transfer, relighting, and component manipulation. Furthermore, given multi-view background perspective and fisheye images, we approximate environment lighting using fisheye images and reconstruct the background with 3DGS, enabling the photorealistic insertion of optimized CAD models into rendered novel view backgrounds. Experimental results demonstrate that UrbanCAD outperforms baselines in terms of photorealism. Additionally, we show that various perception models maintain their accuracy when evaluated on UrbanCAD with in-distribution configurations but degrade when applied to realistic out-of-distribution data generated by our method. This suggests that UrbanCAD is a significant advancement in creating photorealistic, safety-critical driving scenarios for downstream applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_UrbanCAD_Towards_Highly_Controllable_and_Photorealistic_3D_Vehicles_for_Urban_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_UrbanCAD_Towards_Highly_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Yichong and Cai, Yichi and Zhang, Shangzhan and Zhou, Hongyu and Hu, Haoji and Yu, Huimin and Geiger, Andreas and Liao, Yiyi},
    title     = {UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27519-27530}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of T \u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "T \u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}]


title: Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3783-3792
cities: ["Seattle", "Beijing", "Shenzhen", "Seattle", "Ithaca"]
countries: ["USA", "China", "China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Coarse_Correspondences_Boost_Spatial-Temporal_Reasoning_in_Multimodal_Language_Model_CVPR_2025_paper.html

authors:
Benlin Liu, Yuhao Dong, Yiqin Wang, Zixian Ma, Yansong Tang, Luming Tang, Yongming Rao, Wei-Chiu Ma, Ranjay Krishna

abstract:
Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. Current methods often rely on specialized architectural designs or task-specific fine-tuning to achieve this. We introduce Coarse Correspondences, a simple lightweight method that enhances MLLMs' spatial-temporal reasoning with 2D images as input, without modifying the architecture or requiring task-specific fine-tuning. Our method uses a lightweight tracking model to identify primary object correspondences between frames in a video or across different image viewpoints, and then conveys this information to MLLMs through visual prompting. We demonstrate that this simple training-free approach brings substantial gains to GPT4-V/O consistently on four benchmarks that require spatial-temporal reasoning, including +20.5% improvement on ScanQA, +9.7% on OpenEQA's episodic memory subset, +6.0% on the long-form video benchmark EgoSchema, and +11% on the R2R navigation benchmark. Additionally, we show that Coarse Correspondences  can also enhance open-source MLLMs' spatial reasoning (by +6.9% on ScanQA) when applied in both training and inference and that the improvement can generalize to unseen datasets such as SQA3D (+3.1%). Taken together, we show that Coarse Correspondences  effectively and efficiently boosts models' performance on downstream tasks requiring spatial-temporal reasoning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Coarse_Correspondences_Boost_Spatial-Temporal_Reasoning_in_Multimodal_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Coarse_Correspondences_Boost_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Benlin and Dong, Yuhao and Wang, Yiqin and Ma, Zixian and Tang, Yansong and Tang, Luming and Rao, Yongming and Ma, Wei-Chiu and Krishna, Ranjay},
    title     = {Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3783-3792}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]

affiliations:
["University of Washington", "Tsinghua University", "Tencent", "Allen Institute for AI", "Cornell University"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26367-26376
affiliations: ["Hefei University of Technology", "Tencent Youtu Lab", "Tencent WeChat Pay Lab"]
cities: ["Hefei", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_Diff-Palm_Realistic_Palmprint_Generation_with_Polynomial_Creases_and_Intra-Class_Variation_CVPR_2025_paper.html

title:
Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Models

authors:
Jianlong Jin, Chenglong Zhao, Ruixin Zhang, Sheng Shang, Jianqing Xu, Jingyun Zhang, ShaoMing Wang, Yang Zhao, Shouhong Ding, Wei Jia, Yunsheng Wu

abstract:
Palmprint recognition is significantly limited by the lack of large-scale publicly available datasets. Previous methods have adopted Bezier curves to simulate the palm creases, which then serve as input for conditional GANs to generate realistic palmprints.However, without employing real data fine-tuning, the performance of the recognition model trained on these synthetic datasets would drastically decline, indicating a large gap between generated and real palmprints.This is primarily due to the utilization of an inaccurate palm crease representation and challenges in balancing intra-class variation with identity consistency.To address this, we introduce a polynomial-based palm crease representation that provides a new palm crease generation mechanism more closely aligned with the real distribution. We also propose the palm creases conditioned diffusion model with a novel intra-class variation control method.By applying our proposed K-step noise-sharing sampling, we are able to synthesize palmprint datasets with large intra-class variation and high identity consistency.Experimental results show that, for the first time, recognition models trained solely on our synthetic datasets, without any fine-tuning, outperform those trained on real datasets.Furthermore, our approach achieves superior recognition performance as the number of generated identities increases.Our code and pre-trained models will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_Diff-Palm_Realistic_Palmprint_Generation_with_Polynomial_Creases_and_Intra-Class_Variation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_Diff-Palm_Realistic_Palmprint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Jianlong and Zhao, Chenglong and Zhang, Ruixin and Shang, Sheng and Xu, Jianqing and Zhang, Jingyun and Wang, ShaoMing and Zhao, Yang and Ding, Shouhong and Jia, Wei and Wu, Yunsheng},
    title     = {Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26367-26376}
}

raw_affiliation:
[{"affiliation": "Hefei University of Technology", "city": "Hefei", "country": "China"}, {"affiliation": "Tencent Youtu Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent WeChat Pay Lab", "city": "Shenzhen", "country": "China"}]


title: FoundationStereo: Zero-Shot Stereo Matching
authors: Bowen Wen, Matthew Trepte, Joseph Aribido, Jan Kautz, Orazio Gallo, Stan Birchfield
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5249-5260
raw_affiliation: [{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]
affiliations: ["NVIDIA"]
cities: ["Santa Clara"]
countries: ["USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_FoundationStereo_Zero-Shot_Stereo_Matching_CVPR_2025_paper.html

abstract:
Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching. We introduce FoundationStereo, a foundation model for stereo depth estimation designed to achieve strong zero shot generalization. To this end, we first construct a large scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism, followed by an automatic self-curation pipeline to remove ambiguous samples. We then design a number of network architecture components to enhance scalability, including a side-tuning feature backbone that adapts rich monocular priors from vision foundation models to mitigate the sim-to-real gap, and long-range context reasoning for effective cost volume filtering. Together, these components lead to strong robustness and accuracy across domains, establishing a new standard in zero-shot stereo depth estimation. Project page: https://nvlabs.github.io/FoundationStereo

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_FoundationStereo_Zero-Shot_Stereo_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wen_FoundationStereo_Zero-Shot_Stereo_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wen_2025_CVPR,
    author    = {Wen, Bowen and Trepte, Matthew and Aribido, Joseph and Kautz, Jan and Gallo, Orazio and Birchfield, Stan},
    title     = {FoundationStereo: Zero-Shot Stereo Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5249-5260}
}


title: Z-Magic: Zero-shot Multiple Attributes Guided Image Creator
authors: Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18390-18400
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Z-Magic_Zero-shot_Multiple_Attributes_Guided_Image_Creator_CVPR_2025_paper.html

abstract:
The customization of multiple attributes has gained increasing popularity with the rising demand for personalized content creation. Despite promising empirical results, the contextual coherence between different attributes has been largely overlooked. In this paper, we argue that subsequent attributes should follow the multivariable conditional distribution introduced by former attributes creation. In light of this, we reformulate multi-attribute creation from a conditional probability theory perspective and tackle the challenging zero-shot setting. By explicitly modeling the dependencies between attributes, we further enhance the coherence of generated images across diverse attribute combinations. Furthermore, we identify connections between multi-attribute customization and multi-task learning, effectively addressing the high computing cost encountered in multi-attribute synthesis. Extensive experiments demonstrate that Z-Magic outperforms existing models in zero-shot image generation, with broad implications for AI-driven design and creative applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Z-Magic_Zero-shot_Multiple_Attributes_Guided_Image_Creator_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Z-Magic_Zero-shot_Multiple_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Yingying and He, Xiangyu and Tang, Fan and Dong, Weiming},
    title     = {Z-Magic: Zero-shot Multiple Attributes Guided Image Creator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18390-18400}
}

raw_affiliation:
[{"affiliation": "MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["MAIS, Institute of Automation, Chinese Academy of Sciences", "Institute of Computing Technology, Chinese Academy of Sciences"]


authors: Shun Wei, Jielin Jiang, Xiaolong Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9994-10003
cities: ["Nanjing", "Nanjing", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_UniNet_A_Contrastive_Learning-guided_Unified_Framework_with_Feature_Selection_for_CVPR_2025_paper.html

title:
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection

abstract:
Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples. However, most existing AD methods suffer from limited generalizability, as they are primarily designed for domain-specific applications, such as industrial scenarios, and often perform poorly when applied to other domains. This challenge largely stems from the inherent discrepancies in features across domains. To bridge this domain gap, we introduce UniNet, a generic unified framework that incorporates effective feature selection and contrastive learning-guided anomaly discrimination. UniNet comprises student-teacher models and a bottleneck, featuring several vital innovations: First, we propose domain-related feature selection, where the student is guided to select and focus on representative features from the teacher with domain-relevant priors, while restoring them effectively.  Second, a similarity contrastive loss function is  developed to strengthen the correlations among homogeneous features. Meanwhile, a margin loss function is proposed to enforce the separation between the similarities of abnormality and normality, effectively improving the model's ability to discriminate anomalies. Third, we propose a weighted decision mechanism for dynamically evaluating the anomaly score to achieve robust AD. Large-scale experiments on 12 datasets from various domains show that UniNet surpasses existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_UniNet_A_Contrastive_Learning-guided_Unified_Framework_with_Feature_Selection_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_UniNet_A_Contrastive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Shun and Jiang, Jielin and Xu, Xiaolong},
    title     = {UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9994-10003}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Information Science and Technology, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing University, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services, China", "city": "Nanjing", "country": "China"}]

affiliations:
["Nanjing University of Information Science and Technology, China", "Nanjing University, China", "Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services, China"]


title: Chain of Semantics Programming in 3D Gaussian Splatting Representation for 3D Vision Grounding
authors: Jiaxin Shi, Mingyue Xiang, Hao Sun, Yixuan Huang, Zhi Weng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24560-24569
raw_affiliation: [{"affiliation": "Inner Mongolia University", "city": "Hohhot", "country": "China"}]
affiliations: ["Inner Mongolia University"]
cities: ["Hohhot"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Chain_of_Semantics_Programming_in_3D_Gaussian_Splatting_Representation_for_CVPR_2025_paper.html

abstract:
3D Vision Grounding (3DVG) is a fundamental research area that enables agents to perceive and interact with the 3D world. The challenge of the 3DVG task lies in understanding fine-grained semantics and spatial relationships within both the utterance and 3D scene. To address this challenge, we propose a zero-shot neuro-symbolic framework that utilizes a large language model (LLM) as neuro-symbolic functions to ground the object within the 3D Gaussian Splatting (3DGS) representation. By utilizing 3DGS representation, we can dynamically render high-quality 2D images from various viewpoints to enrich the semantic information. Given the complexity of spatial relationships, we construct a relationship graph and chain of semantics that decouple spatial relationships and facilitate step-by-step reasoning within 3DGS representation. Additionally, we employ a grounded-aware self-check mechanism to enable the LLM to reflect on its responses and mitigate the effects of ambiguity in spatial reasoning. We evaluate our method using two publicly available datasets, Nr3D and Sr3D, achieving accuracies of 60.8% and 91.4%, respectively. Notably, our method surpasses current state-of-the-art zero-shot methods on the Nr3D dataset. In addition, it outperforms the recent supervised models on the Sr3D dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Chain_of_Semantics_Programming_in_3D_Gaussian_Splatting_Representation_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Chain_of_Semantics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Jiaxin and Xiang, Mingyue and Sun, Hao and Huang, Yixuan and Weng, Zhi},
    title     = {Chain of Semantics Programming in 3D Gaussian Splatting Representation for 3D Vision Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24560-24569}
}


authors: Baoshun Tong, Hanjiang Lai, Yan Pan, Jian Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19921-19930
raw_affiliation: [{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]
affiliations: ["Sun Yat-sen University"]
cities: ["Guangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tong_On_the_Zero-shot_Adversarial_Robustness_of_Vision-Language_Models_A_Truly_CVPR_2025_paper.html

title:
On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach

abstract:
Pre-trained Vision-Language Models (VLMs) like CLIP, have demonstrated strong zero-shot generalization capabilities. Despite their effectiveness on various downstream tasks, they remain vulnerable to adversarial samples. Existing methods fine-tune VLMs to improve their performance via performing adversarial training on a certain dataset. However, this can lead to model overfitting and is not a true zero-shot scenario. In this paper, we propose a truly zero-shot and training-free approach that can significantly improve the VLM's zero-shot adversarial robustness. Specifically, we first discover that simply adding Gaussian noise greatly enhances the VLM's zero-shot performance. Then, we treat the adversarial examples with added Gaussian noise as anchors and strive to find a path in the embedding space that leads from the adversarial examples to the cleaner samples. We improve the VLMs' generalization abilities in a truly zero-shot and training-free manner compared to previous methods. Extensive experiments on 16 datasets demonstrate that our method can achieve state-of-the-art zero-shot robust performance, improving the top-1 robust accuracy by an average of 9.77%. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tong_On_the_Zero-shot_Adversarial_Robustness_of_Vision-Language_Models_A_Truly_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tong_2025_CVPR,
    author    = {Tong, Baoshun and Lai, Hanjiang and Pan, Yan and Yin, Jian},
    title     = {On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19921-19930}
}


title: Towards General Visual-Linguistic Face Forgery Detection
authors: Ke Sun, Shen Chen, Taiping Yao, Ziyin Zhou, Jiayi Ji, Xiaoshuai Sun, Chia-Wen Lin, Rongrong Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19576-19586
cities: ["Xiamen", "Unknown", "Hsinchu"]
countries: ["China", "China", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Towards_General_Visual-Linguistic_Face_Forgery_Detection_CVPR_2025_paper.html

abstract:
Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust. Recent works demonstrate that leveraging multimodal models can enhance the generalization and interpretability of face forgery detection. However, existing annotation approaches, whether through human labeling or direct Multimodal Large Language Model (MLLM) generation, often suffer from hallucination issues, leading to inaccurate text descriptions, especially for high-quality forgeries. To address this, we propose Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification, followed by a comprehensive prompting strategy to guide MLLMs in reducing hallucination. We validate our approach through fine-tuning both CLIP with a three-branch training framework combining unimodal and multimodal objectives, and MLLMs with our structured annotations. Experimental results demonstrate that our method not only achieves more accurate annotations with higher region identification accuracy, but also leads to improvements in model performance across various forgery detection benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Towards_General_Visual-Linguistic_Face_Forgery_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Towards_General_Visual-Linguistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Ke and Chen, Shen and Yao, Taiping and Zhou, Ziyin and Ji, Jiayi and Sun, Xiaoshuai and Lin, Chia-Wen and Ji, Rongrong},
    title     = {Towards General Visual-Linguistic Face Forgery Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19576-19586}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Youtu Lab, Tecent, China", "city": "Unknown", "country": "China"}, {"affiliation": "National Tsing Hua University", "city": "Hsinchu", "country": "Taiwan"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "Youtu Lab, Tecent, China", "National Tsing Hua University"]


title: Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13146-13156
cities: ["Austin", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Movie_Weaver_Tuning-Free_Multi-Concept_Video_Personalization_with_Anchored_Prompts_CVPR_2025_paper.html

authors:
Feng Liang, Haoyu Ma, Zecheng He, Tingbo Hou, Ji Hou, Kunpeng Li, Xiaoliang Dai, Felix Juefei-Xu, Samaneh Azadi, Animesh Sinha, Peizhao Zhang, Peter Vajda, Diana Marculescu

abstract:
Video personalization, which generates customized videos using reference images, has gained significant attention.However, prior methods typically focus on single-concept personalization, limiting broader applications that require multi-concept integration.Attempts to extend these models to multiple concepts often lead to identity blending, which results in composite characters with fused attributes from multiple sources.This challenge arises due to the lack of a mechanism to link each concept with its specific reference image.We address this with anchored prompts, which embed image anchors as unique tokens within text prompts, guiding accurate referencing during generation.Additionally, we introduce concept embeddings to encode the order of reference images.Our approach, Movie Weaver, seamlessly weaves multiple concepts--including face, body, and animal images--into one video, allowing flexible combinations in a single model.The evaluation shows that Movie Weaver outperforms existing methods for multi-concept video personalization in identity preservation and overall quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Movie_Weaver_Tuning-Free_Multi-Concept_Video_Personalization_with_Anchored_Prompts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Movie_Weaver_Tuning-Free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Feng and Ma, Haoyu and He, Zecheng and Hou, Tingbo and Hou, Ji and Li, Kunpeng and Dai, Xiaoliang and Juefei-Xu, Felix and Azadi, Samaneh and Sinha, Animesh and Zhang, Peizhao and Vajda, Peter and Marculescu, Diana},
    title     = {Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13146-13156}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin, Chandra Family Department of Electrical and Computer Engineering", "city": "Austin", "country": "USA"}, {"affiliation": "Meta GenAI", "city": "Menlo Park", "country": "USA"}]

affiliations:
["The University of Texas at Austin, Chandra Family Department of Electrical and Computer Engineering", "Meta GenAI"]


authors: Tiantian Geng, Jinrui Zhang, Qingni Wang, Teng Wang, Jinming Duan, Feng Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18959-18969
cities: ["Shenzhen", "Birmingham", "Chengdu", "Hong Kong", "Manchester"]
countries: ["China", "United Kingdom", "China", "China", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.html

title:
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos

abstract:
Despite impressive advancements in video understanding, most efforts remain limited to coarse-grained or visual-only video tasks. However, real-world videos encompass omni-modal information (vision, audio, and speech) with a series of events forming a cohesive storyline. The lack of multi-modal video data with fine-grained event annotations and the high cost of manual labeling are major obstacles to comprehensive omni-modality video perception. To address this gap, we propose an automatic pipeline consisting of high-quality multi-modal video filtering, semantically coherent omni-modal event boundary detection, and cross-modal correlation-aware event captioning. In this way, we present LongVALE, the first-ever Vision-Audio-Language Event understanding benchmark comprising 105K omni-modal events with precise temporal boundaries and detailed relation-aware captions within 8.4K high-quality long videos. Further, we build a baseline that leverages LongVALE to enable video large language models (LLMs) for omni-modality fine-grained temporal video understanding for the first time. Extensive experiments demonstrate the effectiveness and great potential of LongVALE in advancing comprehensive multi-modal video understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Geng_2025_CVPR,
    author    = {Geng, Tiantian and Zhang, Jinrui and Wang, Qingni and Wang, Teng and Duan, Jinming and Zheng, Feng},
    title     = {LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18959-18969}
}

raw_affiliation:
[{"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "United Kingdom"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Manchester", "city": "Manchester", "country": "United Kingdom"}]

affiliations:
["Southern University of Science and Technology", "University of Birmingham", "University of Electronic Science and Technology of China", "The University of Hong Kong", "University of Manchester"]


title: MVPortrait: Text-Guided Motion and Emotion Control for Multi-view Vivid Portrait Animation
authors: Yukang Lin, Hokit Fung, Jianjin Xu, Zeping Ren, Adela S.M. Lau, Guosheng Yin, Xiu Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26242-26252
affiliations: ["Tsinghua University", "The University of Hong Kong", "Carnegie Mellon University"]
cities: ["Beijing", "Hong Kong", "Pittsburgh"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_MVPortrait_Text-Guided_Motion_and_Emotion_Control_for_Multi-view_Vivid_Portrait_CVPR_2025_paper.html

abstract:
Recent portrait animation methods have made significant strides in generating realistic lip synchronization. However, they often lack explicit control over head movements and facial expressions, and cannot produce videos from multiple viewpoints, resulting in less controllable and expressive animations. Moreover, text-guided portrait animation remains underexplored, despite its user-friendly nature. In this paper, we present a novel two-stage text-guided framework, MVPortrait, to generate expressive multi-view portrait animations that faithfully capture the described motion and emotion. MVPortrait is the first to introduce FLAME as an intermediate representation, effectively embedding facial movements, expressions, and view transformations within its parameter space. In the first stage, we separately train the FLAME motion and emotion diffusion models based on text input. In the second stage, we train a multi-view video generation model conditioned on a reference portrait image and multi-view FLAME rendering sequences from the first stage. Experimental results exhibit that MVPortrait outperforms existing methods in terms of motion and emotion control, as well as view consistency. Furthermore, by leveraging FLAME as a bridge, MVPortrait becomes the first controllable portrait animation framework that is compatible with text, speech, and video as driving signals.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_MVPortrait_Text-Guided_Motion_and_Emotion_Control_for_Multi-view_Vivid_Portrait_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_MVPortrait_Text-Guided_Motion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Yukang and Fung, Hokit and Xu, Jianjin and Ren, Zeping and Lau, Adela S.M. and Yin, Guosheng and Li, Xiu},
    title     = {MVPortrait: Text-Guided Motion and Emotion Control for Multi-view Vivid Portrait Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26242-26252}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: MoEdit: On Learning Quantity Perception for Multi-object Image Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2683-2693
cities: ["Macau", "Fuzhou", "Dongguan", "Chengdu", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MoEdit_On_Learning_Quantity_Perception_for_Multi-object_Image_Editing_CVPR_2025_paper.html

authors:
Yanfeng Li, Kahou Chan, Yue Sun, Chantong Lam, Tong Tong, Zitong Yu, Keren Fu, Xiaohong Liu, Tao Tan

abstract:
Multi-object images are widely present in the real world, spanning various areas of daily life. Efficient and accurate editing of these images is crucial for applications such as augmented reality, advertisement design, and medical imaging. Stable Diffusion (SD) has ushered in a new era of high-quality image generation and editing. However, existing methods struggle to analyze abstract relationships between multiple objects, often yielding suboptimal performance. To address this, we propose MoEdit, an auxiliary-free method for multi-object image editing. This method enables high-quality editing of attributes in multi-object images, such as style, object features, and background, by maintaining quantity consistency between the input and output images. To preserve the concept of quantity, we introduce a Quantity Attention (QTTN) module to control the editing process. Additionally, we present a Feature Compensation (FeCom) module to enhance the robustness of object preservation. We also employ a null-text training strategy to retain the guiding capability of text prompts, making them plug-and-play modules. This method leverages the powerful image perception and generation capabilities of the SD model, enabling specific concepts to be preserved and altered between input and output images. Experimental results demonstrate that MoEdit achieves the state-of-the-art performance in high-quality multi-object image editing. Data and codes will be available at https://github.com/Tear-kitty/MoEdit.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MoEdit_On_Learning_Quantity_Perception_for_Multi-object_Image_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MoEdit_On_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yanfeng and Chan, Kahou and Sun, Yue and Lam, Chantong and Tong, Tong and Yu, Zitong and Fu, Keren and Liu, Xiaohong and Tan, Tao},
    title     = {MoEdit: On Learning Quantity Perception for Multi-object Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2683-2693}
}

raw_affiliation:
[{"affiliation": "Macao Polytechnic University", "city": "Macau", "country": "China"}, {"affiliation": "Fuzhou University", "city": "Fuzhou", "country": "China"}, {"affiliation": "Great Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Macao Polytechnic University", "Fuzhou University", "Great Bay University", "Sichuan University", "Shanghai Jiao Tong University"]


title: Seeing More with Less: Human-like Representations in Vision Models
authors: Andrey Gizdov, Shimon Ullman, Daniel Harari
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4408-4417
affiliations: ["Weizmann Institute of Science", "Harvard University", "Massachusetts Institute of Technology"]
cities: ["Rehovot", "Cambridge", "Cambridge"]
countries: ["Israel", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gizdov_Seeing_More_with_Less_Human-like_Representations_in_Vision_Models_CVPR_2025_paper.html

abstract:
Large multimodal models (LMMs) typically process visual inputs with uniform resolution across the entire field of view, leading to inefficiencies when non-critical image regions are processed as precisely as key areas. Inspired by the human visual system's foveated approach, we apply a sampling method to leading architectures such as MDETR, BLIP2, InstructBLIP, LLaVA, and ViLT, and evaluate their performance with variable (foveated) resolution inputs. Results show that foveated sampling boosts accuracy in visual tasks like question answering and object detection under tight pixel budgets, improving performance by up to 2.7% on the GQA dataset, 2.1% on SEED-Bench, and 2.0% on VQAv2 compared to uniform sampling. Furthermore, we show that indiscriminate resolution increases yield diminishing returns, with models achieving up to 80% of their full capability using just 3% of the pixels, even on complex tasks. Foveated sampling prompts more human-like processing within models, such as neuronal selectivity and globally acting self-attention in vision transformers. This paper provides a foundational analysis of foveated sampling's impact on existing models, suggesting that more efficient architectural adaptations, mimicking human visual processing, are a promising research venue for the community. Potential applications of our findings center low power minimal bandwidth devices (such as UAVs and edge devices), where compact and efficient vision is critical.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gizdov_Seeing_More_with_Less_Human-like_Representations_in_Vision_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gizdov_Seeing_More_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gizdov_2025_CVPR,
    author    = {Gizdov, Andrey and Ullman, Shimon and Harari, Daniel},
    title     = {Seeing More with Less: Human-like Representations in Vision Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4408-4417}
}

raw_affiliation:
[{"affiliation": "Weizmann Institute of Science", "city": "Rehovot", "country": "Israel"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}]


title: Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification
authors: Jiayu Jiang, Changxing Ding, Wentao Tan, Junhong Wang, Jin Tao, Xiangmin Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9220-9230
affiliations: ["South China University of Technology", "Pazhou Lab"]
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Modeling_Thousands_of_Human_Annotators_for_Generalizable_Text-to-Image_Person_Re-identification_CVPR_2025_paper.html

abstract:
Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost in manually annotating large-scale databases, which affects the generalization ability of ReID models. Recent works handle this problem by leveraging Multi-modal Large Language Models (MLLMs) to describe pedestrian images automatically. However, the captions produced by MLLMs lack diversity in description styles. To address this issue, we propose a Human Annotator Modeling (HAM) approach to enable MLLMs to mimic the description styles of thousands of human annotators. Specifically, we first extract style features from human textual descriptions and perform clustering on them. This allows us to group textual descriptions with similar styles into the same cluster. Then, we employ a prompt to represent each of these clusters and apply prompt learning to mimic the description styles of different human annotators. Furthermore, we define a style feature space and perform uniform sampling in this space to obtain more diverse clustering prototypes, which further enriches the diversity of the MLLM-generated captions. Finally, we adopt HAM to automatically annotate a massive-scale database for text-to-image ReID. Extensive experiments on this database demonstrate that it significantly improves the generalization ability of ReID models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Modeling_Thousands_of_Human_Annotators_for_Generalizable_Text-to-Image_Person_Re-identification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Modeling_Thousands_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Jiayu and Ding, Changxing and Tan, Wentao and Wang, Junhong and Tao, Jin and Xu, Xiangmin},
    title     = {Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9220-9230}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Pazhou Lab", "city": "Guangzhou", "country": "China"}]


title: Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29869-29879
affiliations: ["Rutgers University", "Meta"]
cities: ["New Brunswick", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Accelerating_Multimodal_Large_Language_Models_by_Searching_Optimal_Vision_Token_CVPR_2025_paper.html

authors:
Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu, Xide Xia, Miao Liu, Xiaofang Wang, Mingfu Liang, Ning Zhang, Dimitris N. Metaxas, Licheng Yu

abstract:
Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs) process the text tokens. However, the number of vision tokens increases quadratically as the image resolutions, leading to huge computational costs.In this paper, we consider improving MLLM's efficiency from two scenarios, (I) Reducing computational cost without degrading the performance. (II) Improving the performance with given budgets. We start with our main finding that the ranking of each vision token sorted by attention scores is similar in each layer except the first layer. Based on it, we assume that the number of essential top vision tokens does not increase along layers. Accordingly, for Scenario I, we propose a greedy search algorithm (G-Search) to find the least number of vision tokens to keep at each layer from the shallow to the deep. Interestingly, G-Search is able to reach the optimal reduction strategy based on our assumption. For Scenario II, based on the reduction strategy from G-Search, we design a parametric sigmoid function (P-Sigmoid) to guide the reduction at each layer of the MLLM, whose parameters are optimized by Bayesian Optimization. Extensive experiments demonstrate that our approach can significantly accelerate those popular MLLMs, e.g. LLaVA, and InternVL2 models, by more than 2 xwithout performance drops. Our approach also far outperforms other token reduction methods when budgets are limited, achieving a better trade-off between efficiency and effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Accelerating_Multimodal_Large_Language_Models_by_Searching_Optimal_Vision_Token_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Accelerating_Multimodal_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Shiyu and Wang, Zhenting and Juefei-Xu, Felix and Xia, Xide and Liu, Miao and Wang, Xiaofang and Liang, Mingfu and Zhang, Ning and Metaxas, Dimitris N. and Yu, Licheng},
    title     = {Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29869-29879}
}

raw_affiliation:
[{"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


title: Matrix-Free Shared Intrinsics Bundle Adjustment
authors: Daniel Safari
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27017-27026
raw_affiliation: [{"affiliation": "Sony Semiconductor Solutions", "city": "Tokyo", "country": "Japan"}]
affiliations: ["Sony Semiconductor Solutions"]
cities: ["Tokyo"]
countries: ["Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Safari_Matrix-Free_Shared_Intrinsics_Bundle_Adjustment_CVPR_2025_paper.html

abstract:
Research on accelerating bundle adjustment has focused on photo collections where each image is accompanied by its own set of camera parameters. However, real-world applications overwhelmingly call for shared intrinsics bundle adjustment (SI-BA) where camera parameters are shared across multiple images. Utilizing overlooked optimization opportunities specific to SI-BA, most notably matrix-free computation, we present a solver that is eight times faster than alternatives while consuming a tenth of the memory. Additionally, we examine factors contributing to BA instability under single-precision computation and propose mitigations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Safari_Matrix-Free_Shared_Intrinsics_Bundle_Adjustment_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Safari_2025_CVPR,
    author    = {Safari, Daniel},
    title     = {Matrix-Free Shared Intrinsics Bundle Adjustment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27017-27026}
}


title: AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3614-3624
cities: ["Xi'an", "Xi'an", "Xi'an", "Shenzhen", "Beijing", "Xi'an", "Shenzhen"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_AeroGen_Enhancing_Remote_Sensing_Object_Detection_with_Diffusion-Driven_Data_Generation_CVPR_2025_paper.html

authors:
Datao Tang, Xiangyong Cao, Xuan Wu, Jialin Li, Jing Yao, Xueru Bai, Dongsheng Jiang, Yin Li, Deyu Meng

abstract:
Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, can mitigate this scarcity issue to some extent, they are heavily dependent on high-quality labeled data and perform worse in rare object classes. To address this issue, this paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD. To our knowledge, AeroGen is the first model to simultaneously support horizontal and rotated bounding box condition generation, thus enabling the generation of high-quality synthetic images that meet specific layout and object category requirements. Additionally, we propose an end-to-end data augmentation framework that integrates a diversity-conditioned generator and a filtering mechanism to enhance both the diversity and quality of generated data. Experimental results demonstrate that the synthetic data produced by our method are of high quality and diversity. Furthermore, the synthetic RSIOD data can significantly improve the detection performance of existing RSIOD models, i.e., the mAP metrics on DIOR, DIOR-R, and HRSC datasets are improved by 3.7%, 4.3%, and 2.43%, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_AeroGen_Enhancing_Remote_Sensing_Object_Detection_with_Diffusion-Driven_Data_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_AeroGen_Enhancing_Remote_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Datao and Cao, Xiangyong and Wu, Xuan and Li, Jialin and Yao, Jing and Bai, Xueru and Jiang, Dongsheng and Li, Yin and Meng, Deyu},
    title     = {AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3614-3624}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Huawei Technologies Ltd", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Xi\u2019an Jiaotong University", "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "Pengcheng Laboratory", "Chinese Academy of Sciences", "Xidian University", "Huawei Technologies Ltd"]


authors: Jiange Yang, Haoyi Zhu, Yating Wang, Gangshan Wu, Tong He, Limin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6960-6970
cities: ["Nanjing", "Shanghai", "Hefei", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Tra-MoE_Learning_Trajectory_Prediction_Model_from_Multiple_Domains_for_Adaptive_CVPR_2025_paper.html

title:
Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

abstract:
Learning from multiple domains is a primary factor that influences the generalization of a single unified robot system. In this paper, we aim to learn the trajectory prediction model by using broad out-of-domain data to improve its performance and generalization ability. Trajectory model is designed to predict any-point trajectories in the current frame given an instruction and can provide detailed control guidance for robotic policy learning. To handle the diverse out-of-domain data distribution, we propose a sparsely-gated MoE (Top-1 gating strategy) architecture for trajectory model, coined as Tra-MoE. The sparse activation design enables good balance between parameter cooperation and specialization, effectively benefiting from large-scale out-of-domain data while maintaining constant FLOPs per token. In addition, we further introduce an adaptive policy conditioning technique by learning 2D mask representations for predicted trajectories, which is explicitly aligned with image observations to guide action prediction more flexibly. We perform extensive experiments on both simulation and real-world scenarios to verify the effectiveness of Tra-MoE and adaptive policy conditioning technique. We also conduct a comprehensive empirical study to train Tra-MoE, demonstrating that our Tra-MoE consistently exhibits superior performance compared to the dense baseline model, even when the latter is scaled to match Tra-MoE's parameter count.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Tra-MoE_Learning_Trajectory_Prediction_Model_from_Multiple_Domains_for_Adaptive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Tra-MoE_Learning_Trajectory_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jiange and Zhu, Haoyi and Wang, Yating and Wu, Gangshan and He, Tong and Wang, Limin},
    title     = {Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6960-6970}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "Shanghai AI Laboratory", "University of Science and Technology of China", "Tongji University"]


title: Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key
authors: Zhihe Yang, Xufang Luo, Dongqi Han, Yunjian Xu, Dongsheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10610-10620
cities: ["Hong Kong", "Beijing", "Shenzhen"]
countries: ["China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Mitigating_Hallucinations_in_Large_Vision-Language_Models_via_DPO_On-Policy_Data_CVPR_2025_paper.html

abstract:
Hallucination remains a major challenge for Large Vision-Language Models (LVLMs). Direct Preference Optimization (DPO) has gained increasing attention as a simple solution to hallucination issues. It directly learns from constructed preference pairs that reflect the severity of hallucinations in responses to the same prompt and image. Nonetheless, different data construction methods in existing works bring notable performance variations. We identify a crucial factor here: outcomes are largely contingent on whether the constructed data aligns on-policy w.r.t the initial (reference) policy of DPO. Theoretical analysis suggests that learning from off-policy data is impeded by the presence of KL-divergence between the updated policy and the reference policy. From the perspective of dataset distribution, we systematically summarize the inherent flaws in existing algorithms that employ DPO to address hallucination issues. To alleviate the problems, we propose On-Policy Alignment (OPA)-DPO framework, which uniquely leverages expert feedback to correct hallucinated responses and aligns both the original and expert-revised responses in an on-policy manner. Notably, with only 4.8k data, OPA-DPO achieves an additional reduction in the hallucination rate of  LLaVA-1.5-7B: 13.26% on the AMBER benchmark and 5.39% on the Object-Hal benchmark, compared to the previous SOTA algorithm trained with 16k samples.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Mitigating_Hallucinations_in_Large_Vision-Language_Models_via_DPO_On-Policy_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Mitigating_Hallucinations_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhihe and Luo, Xufang and Han, Dongqi and Xu, Yunjian and Li, Dongsheng},
    title     = {Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10610-10620}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen Research Institute (SZRI)", "city": "Shenzhen", "country": "China"}]

affiliations:
["The Chinese University of Hong Kong", "Microsoft Research Asia", "The Chinese University of Hong Kong, Shenzhen Research Institute (SZRI)"]


title: Style Quantization for Data-Efficient GAN Training
authors: Jian Wang, Xin Lan, Jizhe Zhou, Yuxin Tian, Jiancheng Lv
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7696-7706
cities: ["Chengdu", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Style_Quantization_for_Data-Efficient_GAN_Training_CVPR_2025_paper.html

abstract:
Under limited data setting, GANs often struggle to navigate and effectively exploit the input latent space. Consequently, images generated from adjacent variables in a sparse input latent space may exhibit significant discrepancies in realism, leading to suboptimal consistency regularization (CR) outcomes. To address this, we propose SQ-GAN, a novel approach that enhances CR by introducing a style space quantization scheme. This method transforms the sparse, continuous input latent space into a compact, structured discrete proxy space, allowing each element to correspond to a specific data point, thereby improving CR performance. Instead of direct quantization, we first map the input latent variables into a less entangled "style" space and apply quantization using a learnable codebook. This enables each quantized code to control distinct factors of variation. Additionally, we optimize the optimal transport distance to align the codebook codes with features extracted from the training data by a foundation model, embedding external knowledge into the codebook and establishing a semantically rich vocabulary that properly describes the training dataset. Extensive experiments demonstrate significant improvements in both discriminator robustness and generation quality with our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Style_Quantization_for_Data-Efficient_GAN_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Style_Quantization_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jian and Lan, Xin and Zhou, Jizhe and Tian, Yuxin and Lv, Jiancheng},
    title     = {Style Quantization for Data-Efficient GAN Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7696-7706}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education", "city": "Beijing", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University", "Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education"]


title: Localizing Events in Videos with Multimodal Queries
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3339-3351
affiliations: ["LMU Munich", "TU Munich", "University of Oxford", "Munich Center for Machine Learning (MCML)"]
cities: ["Munich", "Munich", "Oxford", "Munich"]
countries: ["Germany", "Germany", "UK", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Localizing_Events_in_Videos_with_Multimodal_Queries_CVPR_2025_paper.html

authors:
Gengyuan Zhang, Mang Ling Ada Fok, Jialu Ma, Yan Xia, Daniel Cremers, Philip Torr, Volker Tresp, Jindong Gu

abstract:
Localizing events in videos based on semantic queries is a pivotal task in video understanding research and user-oriented applications like video search. Yet, current research predominantly relies on natural language queries (NLQs), overlooking the potential of using multimodal queries (MQs) that incorporate images to flexibly represent semantic queries, particularly when it is difficult to express non-verbal or unfamiliar concepts in words. To bridge this gap, we introduce ICQ, a new benchmark designed for localizing events in videos with MQs, alongside an evaluation dataset ICQ-Highlight. To adapt and reevaluate existing video localization models for this new task, we propose 3 Multimodal Query Adaptation methods and a novel Surrogate Fine-tuning strategy, serving as strong baseline methods. ICQ systematically benchmarks 12 state-of-the-art backbone models, spanning from specialized video localization models to Video Large Language Models. Our extensive experiments highlight the high potential of using MQs in real-world applications. We believe this is a first step toward video event localization with MQs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Localizing_Events_in_Videos_with_Multimodal_Queries_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Localizing_Events_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Gengyuan and Fok, Mang Ling Ada and Ma, Jialu and Xia, Yan and Cremers, Daniel and Torr, Philip and Tresp, Volker and Gu, Jindong},
    title     = {Localizing Events in Videos with Multimodal Queries},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3339-3351}
}

raw_affiliation:
[{"affiliation": "LMU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "Munich Center for Machine Learning (MCML)", "city": "Munich", "country": "Germany"}]


title: PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability
authors: Weijie Zhou, Manli Tao, Chaoyang Zhao, Haiyun Guo, Honghui Dong, Ming Tang, Jinqiao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6940-6949
cities: ["Beijing", "Beijing", "Beijing", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_PhysVLM_Enabling_Visual_Language_Models_to_Understand_Robotic_Physical_Reachability_CVPR_2025_paper.html

abstract:
Understanding the environment and a robot's physical reachability is crucial for task execution. While state-of-the-art vision-language models (VLMs) excel in environmental perception, they often generate inaccurate or impractical responses in embodied visual reasoning tasks due to a lack of understanding of robotic physical reachability. To address this issue, we propose a unified representation of physical reachability across diverse robots, i.e., Space-Physical Reachability Map (S-P Map), and PhysVLM, a vision-language model that integrates this reachability information into visual reasoning. Specifically, the S-P Map abstracts a robot's physical reachability into a generalized spatial representation, independent of specific robot configurations, allowing the model to focus on reachability features rather than robot-specific parameters. Subsequently, PhysVLM extends traditional VLM architectures by incorporating an additional feature encoder to process the S-P Map, enabling the model to reason about physical reachability without compromising its general vision-language capabilities. To train and evaluate PhysVLM, we constructed a large-scale multi-robot dataset, Phys100K, and a challenging benchmark, EQA-phys, which includes tasks for six different robots in both simulated and real-world environments. Experimental results demonstrate that PhysVLM outperforms existing models, achieving a 14% improvement over GPT-4o on EQA-phys and surpassing advanced embodied VLMs such as RoboMamba and SpatialVLM on the RoboVQA-val and OpenEQA benchmarks. Additionally, the S-P Map shows strong compatibility with various VLMs, and its integration into GPT-4o-mini yields a 7.1% performance improvement.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_PhysVLM_Enabling_Visual_Language_Models_to_Understand_Robotic_Physical_Reachability_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Weijie and Tao, Manli and Zhao, Chaoyang and Guo, Haiyun and Dong, Honghui and Tang, Ming and Wang, Jinqiao},
    title     = {PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6940-6949}
}

raw_affiliation:
[{"affiliation": "School of Traffic and Transportation, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "ObjectEye Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Traffic and Transportation, Beijing Jiaotong University", "Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences", "ObjectEye Inc.", "Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University"]


title: CleanDIFT: Diffusion Features without Noise
authors: Nick Stracke, Stefan Andreas Baumann, Kolja Bauer, Frank Fundel, Björn Ommer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 117-127
raw_affiliation: [{"affiliation": "CompVis @ LMU Munich, MCML", "city": "Munich", "country": "Germany"}]
affiliations: ["CompVis @ LMU Munich, MCML"]
cities: ["Munich"]
countries: ["Germany"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stracke_CleanDIFT_Diffusion_Features_without_Noise_CVPR_2025_paper.html

abstract:
Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of downstream tasks. Works that use these features generally need to add noise to images before passing them through the model to obtain the semantic features, as the models do not offer the most useful features when given images with little to no noise. We show that this noise has a critical impact on the usefulness of these features that cannot be remedied by ensembling with different random noises. We address this issue by introducing a lightweight, unsupervised fine-tuning method that enables diffusion backbones to provide high-quality, noise-free semantic features. We show that these features readily outperform previous diffusion features by a wide margin in a wide variety of extraction setups and downstream tasks, offering better performance than even ensemble-based methods at a fraction of the cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stracke_CleanDIFT_Diffusion_Features_without_Noise_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stracke_CleanDIFT_Diffusion_Features_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Stracke_2025_CVPR,
    author    = {Stracke, Nick and Baumann, Stefan Andreas and Bauer, Kolja and Fundel, Frank and Ommer, Bj\"orn},
    title     = {CleanDIFT: Diffusion Features without Noise},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {117-127}
}


title: Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion
authors: Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18062-18071
countries: ["USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hoogeboom_Simpler_Diffusion_1.5_FID_on_ImageNet512_with_Pixel-space_Diffusion_CVPR_2025_paper.html

abstract:
Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-end, latent models are perceived to be more efficient and to produce higher image quality at high resolution. Here we challenge these notions, and show that pixel-space models can be very competitive to latent models both in quality and efficiency, achieving 1.5 FID on ImageNet512 and new SOTA results on ImageNet128, ImageNet256 and Kinetics600. We present a simple recipe for scaling end-to-end pixel-space diffusion models to high resolutions. 1: Use the sigmoid loss-weighting (Kingma & Gao, 2023) with our prescribed hyper-parameters. 2: Use our simplified memory-efficient architecture with fewer skip-connections. 3: Scale the model to favor processing the image at a high resolution with fewer parameters, rather than using more parameters at a lower resolution. Combining these with guidance intervals, we obtain a family of pixel-space diffusion models we call Simpler Diffusion (SiD2).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hoogeboom_Simpler_Diffusion_1.5_FID_on_ImageNet512_with_Pixel-space_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hoogeboom_Simpler_Diffusion_1.5_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hoogeboom_2025_CVPR,
    author    = {Hoogeboom, Emiel and Mensink, Thomas and Heek, Jonathan and Lamerigts, Kay and Gao, Ruiqi and Salimans, Tim},
    title     = {Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18062-18071}
}

raw_affiliation:
[{"affiliation": "Google Deepmind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Deepmind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Deepmind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Deepmind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Deepmind", "city": "Mountain View", "country": "USA"}]

affiliations:
["Google Deepmind", "Google Research", "Google Deepmind", "Google Deepmind", "Google Deepmind", "Google Deepmind"]

cities:
["Mountain View", "Mountain View", "Mountain View", "Mountain View", "Mountain View", "Mountain View"]


title: Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction
authors: Xiaolu Liu, Ruizi Yang, Song Wang, Wentong Li, Junbo Chen, Jianke Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22359-22368
affiliations: ["Zhejiang University", "NUAA", "Udeer.ai"]
cities: ["Hangzhou", "Nanjing", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Uncertainty-Instructed_Structure_Injection_for_Generalizable_HD_Map_Construction_CVPR_2025_paper.html

abstract:
Reliable high-definition (HD) map construction is crucial for the driving safety of autonomous vehicles. While recent studies demonstrate improved performance, their generalization capability across unfamiliar driving scenes remains unexplored. To tackle this issue, we propose UIGenMap, an uncertainty-instructed structure injection approach for generalizable HD map vectorization, which concerns the uncertainty resampling in statistical distribution and employs explicit instance features to reduce the excessive reliance on training data. Specifically, we introduce the perspective-view (PV) detection branch to obtain explicit structural features, in which the uncertainty-aware decoder is designed to dynamically sample probability distributions considering the difference in scenes. With probabilistic embedding and selection, UI2DPrompt is proposed to construct PV learnable prompts. These PV prompts are integrated into the map decoder by designed hybrid injection to compensate for neglected instance structures. To ensure real-time inference, a lightweight Mimic Query Distillation is designed to learn from PV prompts, which can serve as an efficient alternative to the flow of PV branches. Extensive experiments on challenging geographically disjoint (geo-based) data splits demonstrate that our UIGenMap achieves superior performance, with +5.7 mAP improvement on nuScenes dataset. Source code is available at \href https://github.com/xiaolul2/UIGenMap  https://github.com/xiaolul2/UIGenMap .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Uncertainty-Instructed_Structure_Injection_for_Generalizable_HD_Map_Construction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Uncertainty-Instructed_Structure_Injection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xiaolu and Yang, Ruizi and Wang, Song and Li, Wentong and Chen, Junbo and Zhu, Jianke},
    title     = {Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22359-22368}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "NUAA", "city": "Nanjing", "country": "China"}, {"affiliation": "Udeer.ai", "city": "Unknown", "country": "Unknown"}]


title: STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20767-20777
cities: ["Abu Dhabi", "Abu Dhabi", "Bonn", "Bonn", "Perth"]
countries: ["United Arab Emirates", "United Arab Emirates", "Germany", "Germany", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Velayudhan_STING-BEE_Towards_Vision-Language_Model_for_Real-World_X-ray_Baggage_Security_Inspection_CVPR_2025_paper.html

authors:
Divya Velayudhan, Abdelfatah Ahmed, Mohamad Alansari, Neha Gour, Abderaouf Behouch, Taimur Hassan, Syed Talal Wasim, Nabil Maalej, Muzammal Naseer, Juergen Gall, Mohammed Bennamoun, Ernesto Damiani, Naoufel Werghi

abstract:
Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However, current datasets are limited in representing real-world, sophisticated threats and concealment tactics, and existing approaches are constrained by a closed-set paradigm with predefined labels. To address these challenges, we introduce STCray, the first multimodal X-ray baggage security dataset, comprising 46,642 image-caption paired scans across 21 threat categories, generated using an X-ray scanner for airport security. STCray is meticulously developed with our specialized protocol that ensures domain-aware, coherent captions, that lead to the multi-modal instruction following data in X-ray baggage security. This allows us to train a domain-aware visual AI assistant named STING-BEE that supports a range of vision-language tasks, including scene comprehension, referring threat localization, visual grounding, and visual question answering (VQA), establishing novel baselines for multi-modal learning in X-ray baggage security. Further, STING-BEE shows state-of-the-art generalization in cross-domain settings. Code, data, and models are available at https://divs1159.github.io/STING-BEE/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Velayudhan_STING-BEE_Towards_Vision-Language_Model_for_Real-World_X-ray_Baggage_Security_Inspection_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Velayudhan_2025_CVPR,
    author    = {Velayudhan, Divya and Ahmed, Abdelfatah and Alansari, Mohamad and Gour, Neha and Behouch, Abderaouf and Hassan, Taimur and Wasim, Syed Talal and Maalej, Nabil and Naseer, Muzammal and Gall, Juergen and Bennamoun, Mohammed and Damiani, Ernesto and Werghi, Naoufel},
    title     = {STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20767-20777}
}

raw_affiliation:
[{"affiliation": "Khalifa University of Science and Technology", "city": "Abu Dhabi", "country": "United Arab Emirates"}, {"affiliation": "Abu Dhabi University", "city": "Abu Dhabi", "country": "United Arab Emirates"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Lamarr Institute for ML and AI", "city": "Bonn", "country": "Germany"}, {"affiliation": "The University of Western Australia", "city": "Perth", "country": "Australia"}]

affiliations:
["Khalifa University of Science and Technology", "Abu Dhabi University", "University of Bonn", "Lamarr Institute for ML and AI", "The University of Western Australia"]


title: Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
authors: Lei Wang, Senmao Li, Fei Yang, Jianye Wang, Ziheng Zhang, Yuhan Liu, Yaxing Wang, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12880-12890
affiliations: ["PCA Lab, VCIP, College of Computer Science, Nankai University", "Shenzhen Futian, NKIARI"]
cities: ["Tianjin", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Not_All_Parameters_Matter_Masking_Diffusion_Models_for_Enhancing_Generation_CVPR_2025_paper.html

abstract:
The diffusion models, in early stages focus on constructing basic image structures, while the refined details, including local features and textures, are generated in later stages.  Thus the same network layers are forced to learn both structural and textural information simultaneously,  significantly differing from the traditional deep learning architectures (e.g., ResNet or GANs) which  captures or generates the image semantic information at different layers.  This difference inspires us to explore the time-wise diffusion models.  We initially investigate the key contributions of the U-Net parameters to the denoising process and identify that properly zeroing out certain parameters (including large parameters) contributes to denoising, substantially improving the generation quality on the fly. Capitalizing on this discovery, we propose a simple yet effective method--termed "MaskUNet"-- that enhances generation quality with   negligible parameter numbers. Our method fully leverages timestep- and sample-dependent effective U-Net parameters. To optimize MaskUNet,  we offer two fine-tuning strategies: a training-based approach and a training-free approach, including tailored networks and optimization functions.  In zero-shot inference on the COCO dataset, MaskUNet achieves the best FID score and further demonstrates its effectiveness in downstream task evaluations. Project page: https://gudaochangsheng.github.io/MaskUnet-Page/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Not_All_Parameters_Matter_Masking_Diffusion_Models_for_Enhancing_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Not_All_Parameters_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lei and Li, Senmao and Yang, Fei and Wang, Jianye and Zhang, Ziheng and Liu, Yuhan and Wang, Yaxing and Yang, Jian},
    title     = {Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12880-12890}
}

raw_affiliation:
[{"affiliation": "PCA Lab, VCIP, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Shenzhen Futian, NKIARI", "city": "Shenzhen", "country": "China"}]


title: MAD: Memory-Augmented Detection of 3D Objects
authors: Ben Agro, Sergio Casas, Patrick Wang, Thomas Gilles, Raquel Urtasun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1449-1460
raw_affiliation: [{"affiliation": "waabi.ai", "city": "Toronto", "country": "Canada"}]
affiliations: ["waabi.ai"]
cities: ["Toronto"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Agro_MAD_Memory-Augmented_Detection_of_3D_Objects_CVPR_2025_paper.html

abstract:
To perceive, humans use memory to fill in gaps caused by our limited visibility, whether due to occlusion or our narrow field of view. However, most 3D object detectors are limited to using sensor evidence from a short temporal window (0.1s-0.3s). In this work, we present a simple and effective add-on for enhancing any existing 3D object detector with long-term memory regardless of its sensor modality (e.g., LiDAR, camera) and network architecture. We propose a model to effectively align and fuse object proposals from a detector with object proposals from a memory bank of past predictions, exploiting trajectory forecasts to align proposals across time. We propose a novel schedule to train our model on temporal data that balances data diversity and the gap between training and inference. By applying our method to existing LiDAR and camera-based detectors on the Waymo Open Dataset (WOD) and Argoverse 2 Sensor (AV2) dataset, we demonstrate significant improvements in detection performance (+2.5 to +7.6 AP points). Our method attains the best performance on the WOD 3D detection leaderboard among online methods (excluding ensembles or test-time augmentation).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Agro_MAD_Memory-Augmented_Detection_of_3D_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Agro_MAD_Memory-Augmented_Detection_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Agro_2025_CVPR,
    author    = {Agro, Ben and Casas, Sergio and Wang, Patrick and Gilles, Thomas and Urtasun, Raquel},
    title     = {MAD: Memory-Augmented Detection of 3D Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1449-1460}
}


title: Doppelgangers and Adversarial Vulnerability
authors: George Kamberov
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10244-10254
raw_affiliation: [{"affiliation": "University of Alaska Anchorage", "city": "Anchorage", "country": "USA"}]
affiliations: ["University of Alaska Anchorage"]
cities: ["Anchorage"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kamberov_Doppelgangers_and_Adversarial_Vulnerability_CVPR_2025_paper.html

abstract:
Many machine learning (ML) classifiers are claimed to outperform humans, but they still make mistakes that humans do not. The most notorious examples of such mistakes are adversarial visual metamers. This paper aims to define and investigate the phenomenon of adversarial Doppelgangers (AD), which includes adversarial visual metamers, and to compare the performance and robustness of ML classifiers to human performance.We find that AD are inputs that are close to each other with respect to a perceptual metric defined in this paper, and show that AD are qualitatively different from the usual adversarial examples. The vast majority of classifiers are vulnerable to AD and robustness-accuracy trade-offs may not improve them. Some classification problems may not admit any AD robust classifiers because the underlying classes are ambiguous.  We provide criteria that can be used to determine whether a classification problem is well defined or not; describe of an AD robust classifiers' structure and attributes; introduce and explore the notions of conceptual entropy and regions of conceptual ambiguity for classifiers that are vulnerable to AD attacks, along with methods to bound the AD fooling rate of an attack. We define the notion of classifiers that exhibit hyper-sensitive behavior, that is, classifiers whose only mistakes are adversarial Doppelgangers. Improving the AD robustness of hyper-sensitive classifiers proving accuracy.  We identify conditions guaranteeing that all classifiers with sufficiently high accuracy are hyper-sensitive.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kamberov_Doppelgangers_and_Adversarial_Vulnerability_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kamberov_Doppelgangers_and_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kamberov_2025_CVPR,
    author    = {Kamberov, George},
    title     = {Doppelgangers and Adversarial Vulnerability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10244-10254}
}


title: Complexity Experts are Task-Discriminative Learners for Any Image Restoration
authors: Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, Radu Timofte
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12753-12763
cities: ["Wurzburg", "Sofia", "Shanghai"]
countries: ["Germany", "Bulgaria", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zamfir_Complexity_Experts_are_Task-Discriminative_Learners_for_Any_Image_Restoration_CVPR_2025_paper.html

abstract:
Recent advancements in all-in-one image restoration models have revolutionized the ability to address diverse degradations through a unified framework. However, parameters tied to specific tasks often remain inactive for other tasks, making mixture-of-experts (MoE) architectures a natural extension. Despite this, MoEs often show inconsistent behavior, with some experts unexpectedly generalizing across tasks while others struggle within their intended scope. This hinders leveraging MoEs' computational benefits by bypassing irrelevant experts during inference.We attribute this undesired behavior to the uniform and rigid architecture of traditional MoEs. To address this, we introduce "complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields. A key challenge is assigning tasks to each expert, as degradation complexity is unknown in advance. Thus, we execute tasks with a simple bias toward lower complexity.To our surprise, this preference effectively drives task-specific allocation, assigning tasks to experts with the appropriate complexity. Extensive experiments validate our approach, demonstrating the ability to bypass irrelevant experts during inference while maintaining superior performance. The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability. The source code and models are publicly available at \href https://eduardzamfir.github.io/moceir/  \texttt eduardzamfir.github.io/MoCE-IR/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zamfir_Complexity_Experts_are_Task-Discriminative_Learners_for_Any_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zamfir_Complexity_Experts_are_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zamfir_2025_CVPR,
    author    = {Zamfir, Eduard and Wu, Zongwei and Mehta, Nancy and Tan, Yuedong and Paudel, Danda Pani and Zhang, Yulun and Timofte, Radu},
    title     = {Complexity Experts are Task-Discriminative Learners for Any Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12753-12763}
}

raw_affiliation:
[{"affiliation": "Computer V ision Lab, CAIDAS & IFI, University of W \u00a8urzburg", "city": "Wurzburg", "country": "Germany"}, {"affiliation": "INSAIT , So\ufb01a University \u201cSt. Kliment Ohridski\u201d", "city": "Sofia", "country": "Bulgaria"}, {"affiliation": "Shanghai Jiao T ong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Computer V ision Lab, CAIDAS & IFI, University of W \u00a8urzburg", "INSAIT , So\ufb01a University \u201cSt. Kliment Ohridski\u201d", "Shanghai Jiao T ong University"]


title: Generative Omnimatte: Learning to Decompose Video into Layers
authors: Yao-Chih Lee, Erika Lu, Sarah Rumbley, Michal Geyer, Jia-Bin Huang, Tali Dekel, Forrester Cole
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12522-12532
affiliations: ["Google DeepMind", "University of Maryland College Park", "Weizmann Institute of Science"]
cities: ["Mountain View", "College Park", "Rehovot"]
countries: ["USA", "USA", "Israel"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Generative_Omnimatte_Learning_to_Decompose_Video_into_Layers_CVPR_2025_paper.html

abstract:
Given a video and a set of input object masks, an omnimatte method aims to decompose the video into semantically meaningful layers containing individual objects along with their associated effects, such as shadows and reflections.Existing omnimatte methods assume a static background or accurate pose and depth estimation and produce poor decompositions when these assumptions are violated. Furthermore, due to the lack of generative prior on natural videos, existing methods cannot complete dynamic occluded regions.We present a novel generative layered video decomposition framework to address the omnimatte problem. Our method does not assume a stationary scene or require camera pose or depth information and produces clean, complete layers, including convincing completions of occluded dynamic regions. Our core idea is to train a video diffusion model to identify and remove scene effects caused by a specific object. We show that this model can be finetuned from an existing video inpainting model with a small, carefully curated dataset, anddemonstrate high-quality decompositions and editing results for a wide range of casually captured videos containing soft shadows, glossy reflections, splashing water, and more.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Generative_Omnimatte_Learning_to_Decompose_Video_into_Layers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Generative_Omnimatte_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Yao-Chih and Lu, Erika and Rumbley, Sarah and Geyer, Michal and Huang, Jia-Bin and Dekel, Tali and Cole, Forrester},
    title     = {Generative Omnimatte: Learning to Decompose Video into Layers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12522-12532}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Maryland College Park", "city": "College Park", "country": "USA"}, {"affiliation": "Weizmann Institute of Science", "city": "Rehovot", "country": "Israel"}]


title: 5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
authors: Dongshuo Yin, Leiyi Hu, Bin Li, Youqun Zhang, Xue Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20071-20081
cities: ["Shanghai", "Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_5100_Breaking_Performance_Shackles_of_Full_Fine-Tuning_on_Visual_Recognition_CVPR_2025_paper.html

abstract:
Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks. Despite their success, existing visual delta-tuning art fails to exceed the upper limit of full fine-tuning on challenging tasks. To find a competitive alternative to full fine-tuning, we propose the Multi-cognitive Visual Adapter (Mona) tuning, a novel adapter-based tuning method. First, we introduce multiple vision-friendly filters into the adapter to enhance its ability for processing visual signals, while previous methods mainly rely on language-friendly linear filters. Second, we add the scaled layernorm in the adapter to regulate the distribution of input features for visual filters. To fully demonstrate the practicality and generality of Mona, we conduct experiments on representative visual tasks, including instance segmentation on COCO, semantic segmentation on ADE20K, object detection on Pascal VOC, oriented object detection on DOTA/STAR, and image classification on three common datasets. Exciting results illustrate that Mona surpasses full fine-tuning on all these tasks by tuning less than 5% params of the backbone, and is the only delta-tuning method outperforming full fine-tuning on all tasks. For example, Mona achieves 1% performance gain on the COCO compared to full fine-tuning. Comprehensive results suggest that Mona-tuning is more suitable for retaining and utilizing the capabilities of pre-trained models than full fine-tuning. The code is publicly available on https://github.com/Leiyi-Hu/mona.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_5100_Breaking_Performance_Shackles_of_Full_Fine-Tuning_on_Visual_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Dongshuo and Hu, Leiyi and Li, Bin and Zhang, Youqun and Yang, Xue},
    title     = {5\%\ensuremath{>}100\%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20071-20081}
}

raw_affiliation:
[{"affiliation": "Department of Automation, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "BNRist, Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["Department of Automation, Shanghai Jiao Tong University", "BNRist, Department of Computer Science and Technology, Tsinghua University", "University of Chinese Academy of Sciences", "Alibaba Group"]


title: Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance
authors: Sanchayan Santra, Vishal Chudasama, Pankaj Wasnik, Vineeth N Balasubramanian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3163-3172
affiliations: ["Sony Research India", "Indian Institute of Technology Hyderabad"]
cities: ["Bangalore", "Hyderabad"]
countries: ["India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Santra_Precise_Event_Spotting_in_Sports_Videos_Solving_Long-Range_Dependency_and_CVPR_2025_paper.html

abstract:
Precise Event Spotting (PES) aims to identify events and their class from long, untrimmed videos, particularly in sports. The main objective of PES is to detect the event at the exact moment it occurs. Existing methods mainly rely on features from a large pre-trained network, which may not be ideal for the task. Furthermore, these methods overlook the issue of imbalanced event class distribution present in the data, negatively impacting performance in challenging scenarios. This paper demonstrates that an appropriately designed network, trained end-to-end, can outperform state-of-the-art (SOTA) methods. Particularly, we propose a network with a convolutional spatial-temporal feature extractor enhanced with our proposed Adaptive Spatio-Temporal Refinement Module (ASTRM) and a long-range temporal module. The ASTRM enhances the features with spatio-temporal information. Meanwhile, the long-range temporal module helps extract global context from the data by modeling long-range dependencies. To address the class imbalance issue, we introduce the Soft Instance Contrastive (Soft-IC) loss that promotes feature compactness and class separation. Extensive experiments show that the proposed method is efficient and outperforms the SOTA methods, specifically in more challenging settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Santra_Precise_Event_Spotting_in_Sports_Videos_Solving_Long-Range_Dependency_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Santra_Precise_Event_Spotting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Santra_2025_CVPR,
    author    = {Santra, Sanchayan and Chudasama, Vishal and Wasnik, Pankaj and Balasubramanian, Vineeth N},
    title     = {Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3163-3172}
}

raw_affiliation:
[{"affiliation": "Sony Research India", "city": "Bangalore", "country": "India"}, {"affiliation": "Indian Institute of Technology Hyderabad", "city": "Hyderabad", "country": "India"}]


title: Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15214-15223
cities: ["Shanghai", "Shanghai", "Unknown", "Unknown", "Shanghai", "Suzhou"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Real-IAD_D3_A_Real-World_2DPseudo-3D3D_Dataset_for_Industrial_Anomaly_Detection_CVPR_2025_paper.html

authors:
Wenbing Zhu, Lidong Wang, Ziqing Zhou, Chengjie Wang, Yurui Pan, Ruoyi Zhang, Zhuhao Chen, Linjie Cheng, Bin-Bin Gao, Jiangning Zhang, Zhenye Gan, Yuxie Wang, Yulong Chen, Shuguang Qian, Mingmin Chi, Bo Peng, Lizhuang Ma

abstract:
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimodal datasets specifically tailored for IAD remain limited. Pioneering datasets like MVTec 3D have laid essential groundwork in multimodal IAD by incorporating RGB+3D data, but still face challenges in bridging the gap with real industrial environments due to limitations in scale and resolution. To address these challenges, we introduce Real-IAD D3, a high-precision multimodal dataset that uniquely incorporates an additional pseudo-3D modality generated through photometric stereo, alongside high-resolution RGB images and micrometer-level 3D point clouds. Real-IAD D3 comprises industrial components with smaller dimensions and finer defects than existing datasets, offering diverse anomalies across modalities and presenting a more challenging benchmark for multimodal IAD research. With 20 product categories, the dataset offers significantly greater scale and diversity compared to current alternatives. Additionally, we introduce an effective approach that integrates RGB, point cloud, and pseudo-3D depth information to leverage the complementary strengths of each modality, enhancing detection performance. Our experiments highlight the importance of these modalities in boosting detection robustness and overall IAD performance. The Real-IAD D3 dataset will be publicly available to advance research and innovation in multimodal IAD.The dataset and code are publicly accessible for research purposes at https://realiad4ad.github.io/Real-IAD_D3.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Real-IAD_D3_A_Real-World_2DPseudo-3D3D_Dataset_for_Industrial_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Real-IAD_D3_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Wenbing and Wang, Lidong and Zhou, Ziqing and Wang, Chengjie and Pan, Yurui and Zhang, Ruoyi and Chen, Zhuhao and Cheng, Linjie and Gao, Bin-Bin and Zhang, Jiangning and Gan, Zhenye and Wang, Yuxie and Chen, Yulong and Qian, Shuguang and Chi, Mingmin and Peng, Bo and Ma, Lizhuang},
    title     = {Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15214-15223}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "YouTu Lab, Tencent", "city": "Unknown", "country": "China"}, {"affiliation": "Rongcheer Co., Ltd.", "city": "Unknown", "country": "China"}, {"affiliation": "Shanghai Ocean University", "city": "Shanghai", "country": "China"}, {"affiliation": "Suzhou University", "city": "Suzhou", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Jiao Tong University", "YouTu Lab, Tencent", "Rongcheer Co., Ltd.", "Shanghai Ocean University", "Suzhou University"]


authors: Qinghe Ma, Jian Zhang, Zekun Li, Lei Qi, Qian Yu, Yinghuan Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5175-5185
affiliations: ["Nanjing University", "Southeast University", "Shandong Women\u2019s University"]
cities: ["Nanjing", "Nanjing", "Jinan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Steady_Progress_Beats_Stagnation_Mutual_Aid_of_Foundation_and_Conventional_CVPR_2025_paper.html

title:
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation

abstract:
Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged sword when adapting them to downstream tasks in specific domains.In the context of semi-supervised medical image segmentation with domain shift, foundation models like MedSAM tend to make overconfident predictions, some of which are incorrect. The error accumulation hinders the effective utilization of unlabeled data and limits further improvements.In this paper, we introduce a Synergistic training framework for Foundation and Conventional models (SynFoC) to address the issue. We observe that a conventional model trained from scratch has the ability to correct the high-confidence mispredictions of the foundation model, while the foundation model can supervise it with high-quality pseudo-labels in the early training stages. Furthermore, to enhance the collaborative training effectiveness of both models and promote reliable convergence towards optimization, the consensus-divergence consistency regularization is proposed. We demonstrate the superiority of our method across four public multi-domain datasets. In particular, our method improves the Dice score by 10.31% on the Prostate dataset. Our code is available in the supplementary material.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Steady_Progress_Beats_Stagnation_Mutual_Aid_of_Foundation_and_Conventional_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Steady_Progress_Beats_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Qinghe and Zhang, Jian and Li, Zekun and Qi, Lei and Yu, Qian and Shi, Yinghuan},
    title     = {Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5175-5185}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shandong Women\u2019s University", "city": "Jinan", "country": "China"}]


title: ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks
authors: Mohamed Afane, Gabrielle Ebbrecht, Ying Wang, Juntao Chen, Junaid Farooq
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20427-20436
affiliations: ["Fordham University", "Stevens Institute of Technology", "University of Michigan-Dearborn"]
cities: ["New York", "Hoboken", "Dearborn"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Afane_ATP_Adaptive_Threshold_Pruning_for_Efficient_Data_Encoding_in_Quantum_CVPR_2025_paper.html

abstract:
Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient computations in QNNs. ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance. Extensive experiments across multiple datasets demonstrate that ATP reduces entanglement entropy and improves adversarial robustness when combined with adversarial training methods like FGSM. Our results highlight ATP's ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources, which will help make QNNs more feasible in practical, resource-constrained settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Afane_ATP_Adaptive_Threshold_Pruning_for_Efficient_Data_Encoding_in_Quantum_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Afane_2025_CVPR,
    author    = {Afane, Mohamed and Ebbrecht, Gabrielle and Wang, Ying and Chen, Juntao and Farooq, Junaid},
    title     = {ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20427-20436}
}

raw_affiliation:
[{"affiliation": "Fordham University", "city": "New York", "country": "USA"}, {"affiliation": "Stevens Institute of Technology", "city": "Hoboken", "country": "USA"}, {"affiliation": "University of Michigan-Dearborn", "city": "Dearborn", "country": "USA"}]


title: Color Alignment in Diffusion
authors: Ka Chun Shum, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28446-28455
affiliations: ["Hong Kong University of Science and Technology", "Trinity College Dublin", "Deakin University"]
cities: ["Hong Kong", "Dublin", "Geelong"]
countries: ["China", "Ireland", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shum_Color_Alignment_in_Diffusion_CVPR_2025_paper.html

abstract:
Diffusion models have shown great promise in synthesizing visually appealing images. However, it remains challenging to condition the synthesis at a fine-grained level, for instance, synthesizing image pixels following some generic color pattern. Existing image synthesis methods often produce contents that fall outside the desired pixel conditions. To address this, we introduce a novel color alignment algorithm that confines the generative process in diffusion models within a given color pattern. Specifically, we project diffusion terms, either imagery samples or latent representations, into a conditional color space to align with the input color distribution. This strategy simplifies the prediction in diffusion models within a color manifold while still allowing plausible structures in generated contents, thus enabling the generation of diverse contents that comply with the target color pattern. Experimental results demonstrate our state-of-the-art performance in conditioning and controlling of color pixels, while maintaining on-par generation quality and diversity in comparison with regular diffusion models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shum_Color_Alignment_in_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shum_Color_Alignment_in_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shum_2025_CVPR,
    author    = {Shum, Ka Chun and Hua, Binh-Son and Nguyen, Duc Thanh and Yeung, Sai-Kit},
    title     = {Color Alignment in Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28446-28455}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Trinity College Dublin", "city": "Dublin", "country": "Ireland"}, {"affiliation": "Deakin University", "city": "Geelong", "country": "Australia"}]


title: LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24297-24308
affiliations: ["UNC Charlotte", "Salesforce AI Research", "Inria", "Universit\u00b4e C\u02c6ote d\u2019Azur"]
cities: ["Charlotte", "Unknown", "Paris", "Nice"]
countries: ["USA", "USA", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Reilly_LLAVIDAL_A_Large_LAnguage_VIsion_Model_for_Daily_Activities_of_CVPR_2025_paper.html

authors:
Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha, Manish Kumar Govind, Pu Wang, Francois Bremond, Le Xue, Srijan Das

abstract:
Current Large Language Vision Models (LLVMs) trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interactions (HOI), and view-invariant representation learning essential for Activities of Daily Living (ADL). This limitation stems from a lack of specialized ADL video instruction-tuning datasets and insufficient modality integration to capture discriminative action representations. To address this, we propose a semi-automated framework for curating ADL datasets, creating ADL-X, a multi-view, multi-modal RGBS instruction-tuning dataset. Additionally, we introduce LLAVIDAL, an LLVM integrating videos, 3D skeletons, and HOIs to model ADL's complex spatiotemporal relationships. For training LLAVIDAL, a simple joint alignment of all modalities yields suboptimal results; thus, we propose a Multimodal Progressive (MMPro) training strategy, incorporating modalities in stages following a curriculum. We also establish ADL MCQ and video description benchmarks to assess LLVM performance in ADL tasks. Trained on ADL-X, LLAVIDAL achieves state-of-the-art performance across ADL benchmarks. Code and data will be made publicly available at https://adl-x.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Reilly_LLAVIDAL_A_Large_LAnguage_VIsion_Model_for_Daily_Activities_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reilly_LLAVIDAL_A_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Reilly_2025_CVPR,
    author    = {Reilly, Dominick and Chakraborty, Rajatsubhra and Sinha, Arkaprava and Govind, Manish Kumar and Wang, Pu and Bremond, Francois and Xue, Le and Das, Srijan},
    title     = {LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24297-24308}
}

raw_affiliation:
[{"affiliation": "UNC Charlotte", "city": "Charlotte", "country": "USA"}, {"affiliation": "Salesforce AI Research", "city": "Unknown", "country": "USA"}, {"affiliation": "Inria", "city": "Paris", "country": "France"}, {"affiliation": "Universit\u00b4e C\u02c6ote d\u2019Azur", "city": "Nice", "country": "France"}]


title: Language-Guided Salient Object Ranking
authors: Fang Liu, Yuhao Liu, Ke Xu, Shuquan Ye, Gerhard Petrus Hancke, Rynson W. H. Lau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29803-29813
affiliations: ["Department of Computer Science, City University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Language-Guided_Salient_Object_Ranking_CVPR_2025_paper.html

abstract:
Salient Object Ranking (SOR) aims to study human attention shifts across different objects in the scene. It is a challenging task, as it requires comprehension of the relations among the salient objects in the scene. However, existing works often overlook such relations or model them implicitly. In this work, we observe that when Large Vision-Language Models (LVLMs) describe a scene, they usually focus on the most salient object first, and then discuss the relations as they move on to the next (less salient) one. Based on this observation, we propose a novel Language-Guided Salient Object Ranking approach (named LG-SOR), which utilizes the internal knowledge within the LVLM-generated language descriptions, i.e., semantic relation cues and the implicit entity order cues, to facilitate saliency ranking. Specifically, we first propose a novel Text-Guided Visual Modulation (TGVM) module to incorporate semantic information in the description for saliency ranking. TGVM controls the flow of linguistic information to the visual features, suppresses noisy background image features, and enables propagation of useful textual features. We then propose a novel Text-Aware Visual Reasoning (TAVR) module to enhance model reasoning in object ranking, by explicitly learning a multimodal graph based on the entity and relation cues derived from the description. Extensive experiments demonstrate superior performances of our model on two SOR benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Language-Guided_Salient_Object_Ranking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Language-Guided_Salient_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Fang and Liu, Yuhao and Xu, Ke and Ye, Shuquan and Hancke, Gerhard Petrus and Lau, Rynson W. H.},
    title     = {Language-Guided Salient Object Ranking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29803-29813}
}

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


title: Decoupled Motion Expression Video Segmentation
authors: Hao Fang, Runmin Cong, Xiankai Lu, Xiaofei Zhou, Sam Kwong, Wei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13821-13831
cities: ["Jinan", "Jinan", "Jinan", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Decoupled_Motion_Expression_Video_Segmentation_CVPR_2025_paper.html

abstract:
Motion expression video segmentation aims to segment objects based on input motion descriptions. Compared with traditional referring video object segmentation, it focuses on motion and multi-object expressions and is more challenging. Previous works achieved it by simply injecting text information into the video instance segmentation (VIS) model. However, this requires retraining the entire model and optimization is difficult. In this work, we propose DMVS, a simple framework constructed on the existing query-based VIS model, emphasizing decoupling the task into video instance segmentation and motion expression understanding. Firstly, we use a frozen video instance segmenter to extract object-specific contexts and convert them into frame-level and video-level queries. Secondly, we interact two levels of queries with static and motion cues, respectively, to further encode visually enhanced motion expressions. Furthermore, we propose a novel query initialization strategy that uses video queries guided by classification priors to initialize motion queries, greatly reducing the difficulty of optimization. Without bells and whistles, DMVS achieves state-of-the-art performance on the MeViS dataset at a lower training cost. Extensive experiments verify the effectiveness and efficiency of our framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Decoupled_Motion_Expression_Video_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Decoupled_Motion_Expression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Hao and Cong, Runmin and Lu, Xiankai and Zhou, Xiaofei and Kwong, Sam and Zhang, Wei},
    title     = {Decoupled Motion Expression Video Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13821-13831}
}

raw_affiliation:
[{"affiliation": "School of Control Science and Engineering, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and System Control, Ministry of Education", "city": "Jinan", "country": "China"}, {"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "School of Automation, Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "School of Data Science, Lingnan University", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Control Science and Engineering, Shandong University", "Key Laboratory of Machine Intelligence and System Control, Ministry of Education", "School of Software, Shandong University", "School of Automation, Hangzhou Dianzi University", "School of Data Science, Lingnan University"]


title: K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs
authors: Ziheng Ouyang, Zhen Li, Qibin Hou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13041-13050
affiliations: ["School of Computer Science, Nankai University"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ouyang_K-LoRA_Unlocking_Training-Free_Fusion_of_Any_Subject_and_Style_LoRAs_CVPR_2025_paper.html

abstract:
Recent studies have explored combining different LoRAs to jointly generate learned style and content. However, existing methods either fail to effectively preserve both the original subject and style simultaneously or require additional training. In this paper, we argue that the intrinsic properties of LoRA can effectively guide diffusion models in merging learned subject and style. Building on this insight, we propose K-LoRA, a simple yet effective training-free LoRA fusion approach. In each attention layer, K-LoRA compares the Top-K elements in each LoRA to be fused, determining which LoRA to select for optimal fusion. This selection mechanism ensures that the most representative features of both subject and style are retained during the fusion process, effectively balancing their contributions. Experimental results demonstrate that the proposed method effectively integrates the subject and style information learned by the original LoRAs, outperforming state-of-the-art training-based approaches in both qualitative and quantitative results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ouyang_K-LoRA_Unlocking_Training-Free_Fusion_of_Any_Subject_and_Style_LoRAs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ouyang_K-LoRA_Unlocking_Training-Free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ouyang_2025_CVPR,
    author    = {Ouyang, Ziheng and Li, Zhen and Hou, Qibin},
    title     = {K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13041-13050}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}]


authors: Yue-Hua Han, Tai-Ming Huang, Kai-Lung Hua, Jun-Cheng Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22995-23005
cities: ["Taipei", "Redmond", "Taipei", "Taipei"]
countries: ["Taiwan", "USA", "Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Towards_More_General_Video-based_Deepfake_Detection_through_Facial_Component_Guided_CVPR_2025_paper.html

title:
Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Model

abstract:
The current deep generative models have enabled the creation of synthetic facial images with remarkable photorealism, raising significant societal concerns over their potential misuse. Despite rapid advancements in the field of deepfake detection, developing an efficient and effective approach for the generalized deepfake detection of unseen forgery samples remains challenging. To address this challenge, we leverage the rich semantic priors of foundation models and propose a novel side-network-based decoder that extracts spatial and temporal cues using the CLIP image encoder for generalized video-based Deepfake detection. Additionally, we introduce Facial Component Guidance (FCG) to enhance spatial learning generalizability by encouraging the model to focus on key facial regions. By leveraging the generic features of a vision-language foundation model, our approach demonstrates promising generalizability on challenging Deepfake datasets while also exhibiting superiority in training data efficiency, parameter efficiency, and model robustness. The source code is available at: https://github.com/aiiu-lab/DFD-FCG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Towards_More_General_Video-based_Deepfake_Detection_through_Facial_Component_Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Towards_More_General_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Yue-Hua and Huang, Tai-Ming and Hua, Kai-Lung and Chen, Jun-Cheng},
    title     = {Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22995-23005}
}

raw_affiliation:
[{"affiliation": "Academia Sinica", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "National Taiwan University of Science and Technology", "city": "Taipei", "country": "Taiwan"}]

affiliations:
["Academia Sinica", "Microsoft", "National Taiwan University", "National Taiwan University of Science and Technology"]


title: WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
authors: Zongjian Li, Bin Lin, Yang Ye, Liuhan Chen, Xinhua Cheng, Shenghai Yuan, Li Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17778-17788
affiliations: ["Peking University", "Peng Cheng Laboratory", "Rabbitpre Intelligence"]
cities: ["Beijing", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper.html

abstract:
Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottleneck in training LVDMs. Moreover, the block-wise inference method adopted by most LVDMs can lead to discontinuities of latent space when processing long-duration videos. The key to addressing the computational bottleneck lies in decomposing videos into distinct components and efficiently encoding the critical information. Wavelet transform can decompose videos into multiple frequency-domain components and improve the efficiency significantly, we thus propose Wavelet Flow VAE (WF-VAE), an autoencoder that leverages multi-level wavelet transform to facilitate low-frequency energy flow into latent representation. Furthermore, we introduce a method called Causal Cache, which maintains the integrity of latent space during block-wise inference. Compared to state-of-the-art video VAEs, WF-VAE demonstrates superior performance in both PSNR and LPIPS metrics, achieving 2x higher throughput and 4x lower memory consumption while maintaining competitive reconstruction quality. Our code and models are available at https://github.com/PKU-YuanGroup/WF-VAE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_WF-VAE_Enhancing_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zongjian and Lin, Bin and Ye, Yang and Chen, Liuhan and Cheng, Xinhua and Yuan, Shenghai and Yuan, Li},
    title     = {WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17778-17788}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Rabbitpre Intelligence", "city": "Beijing", "country": "China"}]


title: SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
authors: Shijia Zhao, Qiming Xia, Xusheng Guo, Pufan Zou, Maoji Zheng, Hai Wu, Chenglu Wen, Cheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29374-29384
cities: ["Xiamen", "Xiamen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_SP3D_Boosting_Sparsely-Supervised_3D_Object_Detection_via_Accurate_Cross-Modal_Semantic_CVPR_2025_paper.html

abstract:
Recently, sparsely-supervised 3D object detection has gained great attention, achieving performance close to fully-supervised 3D detectors while requiring only a few annotated instances. Nevertheless, these methods suffer challenges when accurate labels are extremely absent. In this paper, we propose a boosting strategy, termed SP3D, explicitly utilizing the cross-modal semantic prompts generated from Large Multimodal Models (LMMs) to boost the 3D detector with robust feature discrimination capability under sparse annotation settings. Specifically, we first develop a Confident Points Semantic Transfer (CPST) module that generates accurate cross-modal semantic prompts through boundary-constrained center cluster selection. Based on these accurate semantic prompts, which we treat as seed points, we introduce a Dynamic Cluster Pseudo-label Generation (DCPG) module to yield pseudo-supervision signals from the geometry shape of multi-scale neighbor points. Additionally, we design a Distribution Shape score (DS score) that chooses high-quality supervision signals for the initial training of the 3D detector. Experiments on the KITTI dataset and Waymo Open Dataset (WOD) have validated that SP3D can enhance the performance of sparsely supervised detectors by a large margin under meager labeling conditions. Moreover, we verified SP3D in the zero-shot setting, where its performance exceeded that of the state-of-the-art methods. The code is available at https://github.com/xmuqimingxia/SP3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_SP3D_Boosting_Sparsely-Supervised_3D_Object_Detection_via_Accurate_Cross-Modal_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_SP3D_Boosting_Sparsely-Supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Shijia and Xia, Qiming and Guo, Xusheng and Zou, Pufan and Zheng, Maoji and Wu, Hai and Wen, Chenglu and Wang, Cheng},
    title     = {SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29374-29384}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University, China", "city": "Xiamen", "country": "China"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, China", "Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University, China"]


title: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
authors: Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4398-4407
affiliations: ["ETH Z\u00a8urich", "Stanford University", "University of Bonn & Lamarr Institute"]
cities: ["Zurich", "Stanford", "Bonn"]
countries: ["Switzerland", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_ARKit_LabelMaker_A_New_Scale_for_Indoor_3D_Scene_Understanding_CVPR_2025_paper.html

abstract:
Neural network performance scales with both model size and data volume, as shown in both language and image processing. This requires scaling-friendly architectures and large datasets. While transformers have been adapted for 3D vision, a `GPT-moment' remains elusive due to limited training data. We introduce ARKit LabelMaker, a large-scale real-world 3D dataset with dense semantic annotation that is more than three times larger than prior largest dataset. Specifically, we extend ARKitScenes with automatically generated dense 3D labels using an extended LabelMaker pipeline, tailored for large-scale pre-training. Training on our dataset improves accuracy across architectures, achieving state-of-the-art 3D semantic segmentation scores on ScanNet and ScanNet200, with notable gains on tail classes. Our code is available at https://labelmaker.org and our dataset at https://huggingface.co/datasets/labelmaker/arkit_labelmaker.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_ARKit_LabelMaker_A_New_Scale_for_Indoor_3D_Scene_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_ARKit_LabelMaker_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Guangda and Weder, Silvan and Engelmann, Francis and Pollefeys, Marc and Blum, Hermann},
    title     = {ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4398-4407}
}

raw_affiliation:
[{"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "University of Bonn & Lamarr Institute", "city": "Bonn", "country": "Germany"}]


title: VoCo-LLaMA: Towards Vision Compression with Large Language Models
authors: Xubing Ye, Yukang Gan, Xiaoke Huang, Yixiao Ge, Yansong Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29836-29846
cities: ["Shenzhen", "Unknown", "Santa Cruz"]
countries: ["China", "Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_VoCo-LLaMA_Towards_Vision_Compression_with_Large_Language_Models_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational cost of processing high-resolution image inputs and videos. Vision compression can alleviate this problem by reducing the vision token count. Previous approaches compress vision tokens with external modules and force LLMs to understand the compressed ones, leading to visual information loss. However, the LLMs' understanding paradigm of vision tokens is not fully utilised in the compression learning process. We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs. By introducing Vision Compression tokens during the vision instruction tuning phase and leveraging attention distillation, our method distill how LLMs comprehend vision tokens into their processing of VoCo tokens. VoCo-LLaMA facilitates effective vision compression and improves the computational efficiency during the inference stage. Specifically, our method can achieve a 576x compression rate while maintaining 83.7% performance. Furthermore, through continuous training using time-series compressed token sequences of video frames, VoCo-LLaMA demonstrates the ability to understand temporal correlations, outperforming previous methods on popular video question-answering benchmarks. Our approach presents a promising way to unlock the full potential of VLMs' contextual window, enabling more scalable multi-modal applications. The project page can be accessed via https://yxxxb.github.io/VoCo-LLaMA-page.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_VoCo-LLaMA_Towards_Vision_Compression_with_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_VoCo-LLaMA_Towards_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Xubing and Gan, Yukang and Huang, Xiaoke and Ge, Yixiao and Tang, Yansong},
    title     = {VoCo-LLaMA: Towards Vision Compression with Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29836-29846}
}

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

affiliations:
["Tsinghua Shenzhen International Graduate School, Tsinghua University", "ARC Lab, Tencent PCG", "UC Santa Cruz"]


title: StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2568-2577
affiliations: ["Picsart AI Resarch (P AIR)", "UT Austin", "Georgia T ech"]
cities: ["Los Angeles", "Austin", "Atlanta"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Henschel_StreamingT2V_Consistent_Dynamic_and_Extendable_Long_Video_Generation_from_Text_CVPR_2025_paper.html

authors:
Roberto Henschel, Levon Khachatryan, Hayk Poghosyan, Daniil Hayrapetyan, Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi

abstract:
Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, simplifying the process of producing diverse and individual content. Current methods excel in generating short videos (up to 16s), but produce hard-cuts when naively extended to long video synthesis. To overcome these limitations, we present StreamingT2V, an autoregressive method that generates long videos of up to 2 minutes or longer with seamless transitions. The key components are: (i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the preceding chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a long-term memory block called appearance preservation module (APM), which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene,  and (iii) a randomized blending approach that allows for the autoregressive application of a video enhancer on videos of indefinite length, ensuring consistency across chunks. Experiments show that StreamingT2V produces more motion, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion.    Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator, surpassing competitors in both consistency and motion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Henschel_StreamingT2V_Consistent_Dynamic_and_Extendable_Long_Video_Generation_from_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Henschel_StreamingT2V_Consistent_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Henschel_2025_CVPR,
    author    = {Henschel, Roberto and Khachatryan, Levon and Poghosyan, Hayk and Hayrapetyan, Daniil and Tadevosyan, Vahram and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey},
    title     = {StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2568-2577}
}

raw_affiliation:
[{"affiliation": "Picsart AI Resarch (P AIR)", "city": "Los Angeles", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Georgia T ech", "city": "Atlanta", "country": "USA"}]


title: Focal Split: Untethered Snapshot Depth from Differential Defocus
authors: Junjie Luo, John Mamish, Alan Fu, Thomas Concannon, Josiah Hester, Emma Alexander, Qi Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26965-26974
cities: ["West Lafayette", "Atlanta", "Evanston"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Focal_Split_Untethered_Snapshot_Depth_from_Differential_Defocus_CVPR_2025_paper.html

abstract:
We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480 x 360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Focal_Split_Untethered_Snapshot_Depth_from_Differential_Defocus_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Focal_Split_Untethered_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Junjie and Mamish, John and Fu, Alan and Concannon, Thomas and Hester, Josiah and Alexander, Emma and Guo, Qi},
    title     = {Focal Split: Untethered Snapshot Depth from Differential Defocus},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26965-26974}
}

raw_affiliation:
[{"affiliation": "Elmore Family School of Electrical and Computer Engineering, Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "College of Computing, Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "McCormick School of Engineering, Northwestern University", "city": "Evanston", "country": "USA"}]

affiliations:
["Elmore Family School of Electrical and Computer Engineering, Purdue University", "College of Computing, Georgia Institute of Technology", "McCormick School of Engineering, Northwestern University"]


title: AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
authors: Run He, Kai Tong, Di Fang, Han Sun, Ziqian Zeng, Haoran Li, Tianyi Chen, Huiping Zhuang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4988-4998
cities: ["Guangzhou", "Beijing", "Beijing", "Hong Kong", "Redmond"]
countries: ["China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html

abstract:
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-trained models. Our AFL draws inspiration from analytic learning---a gradient-free technique that trains neural networks with analytical solutions in one epoch. In the local client training stage, the AFL facilitates a one-epoch training,  eliminating the necessity for multi-epoch updates. In the aggregation stage, we derive an absolute aggregation (AA) law. This AA law allows a single-round aggregation, reducing heavy communication overhead and achieving fast convergence by removing the need for multiple aggregation rounds. More importantly, the AFL exhibits a property that invariance to data partitioning, meaning that regardless of how the full dataset is distributed among clients, the aggregated result remains identical. This could spawn various potentials, such as data heterogeneity invariance and client-number invariance. We conduct experiments across various FL settings including extremely non-IID ones, and scenarios with a large number of clients (e.g., \ge 1000). In all these settings, our AFL constantly performs competitively while existing FL techniques encounter various obstacles.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_AFL_A_Single-Round_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Run and Tong, Kai and Fang, Di and Sun, Han and Zeng, Ziqian and Li, Haoran and Chen, Tianyi and Zhuang, Huiping},
    title     = {AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4988-4998}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing National Research Center for Information Science and Technology", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]

affiliations:
["South China University of Technology", "Tsinghua University", "Beijing National Research Center for Information Science and Technology", "The Hong Kong University of Science and Technology", "Microsoft"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14325-14336
cities: ["Changsha", "Beijing", "Wuhan", "Beijing", "Beijing", "Wuhan"]
countries: ["China", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.html

title:
XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?

authors:
Fengxiang Wang, Hongzhen Wang, Zonghao Guo, Di Wang, Yulin Wang, Mingshuo Chen, Qiang Ma, Long Lan, Wenjing Yang, Jing Zhang, Zhiyuan Liu, Maosong Sun

abstract:
The astonishing breakthrough of multimodal large language models (MLLMs) has necessitated new benchmarks to quantitatively assess their capabilities, reveal their limitations, and indicate future research directions. However, this is challenging in the context of remote sensing (RS), since the imagery features ultra-high resolution that incorporates extremely complex semantic relationships. Existing benchmarks usually adopt notably smaller image sizes than real-world RS scenarios, suffer from limited annotation quality, and consider insufficient dimensions of evaluation. To address these issues, we present XLRS-Bench: a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios. XLRS-Bench boasts the largest average image size (8500x8500) observed thus far, with all evaluation samples meticulously annotated manually, assisted by a novel semi-automatic captioner on ultra-high-resolution RS images. On top of the XLRS-Bench, 16 sub-tasks are defined to evaluate MLLMs' 10 kinds of perceptual capabilities and 6 kinds of reasoning capabilities, with a primary emphasis on advanced cognitive processes that facilitate real-world decision-making and the capture of spatiotemporal changes. The results of both general and RS-focused MLLMs on XLRS-Bench indicate that further efforts are needed for real-world RS applications. We will open source XLRS-Bench to support further research of developing more powerful MLLMs for RS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_XLRS-Bench_Could_Your_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Fengxiang and Wang, Hongzhen and Guo, Zonghao and Wang, Di and Wang, Yulin and Chen, Mingshuo and Ma, Qiang and Lan, Long and Yang, Wenjing and Zhang, Jing and Liu, Zhiyuan and Sun, Maosong},
    title     = {XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14325-14336}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Zhongguancun Academy", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["College of Computer Science and Technology, National University of Defense Technology", "Tsinghua University", "School of Computer Science, Wuhan University", "Beijing University of Posts and Telecommunications", "Zhongguancun Academy", "School of Artificial Intelligence, Wuhan University"]


title: BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding
authors: Shuming Liu, Chen Zhao, Tianqi Xu, Bernard Ghanem
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3318-3327
affiliations: ["King Abdullah University of Science and Technology (KAUST)"]
cities: ["Thuwal"]
countries: ["Saudi Arabia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_BOLT_Boost_Large_Vision-Language_Model_Without_Training_for_Long-form_Video_CVPR_2025_paper.html

abstract:
Large video-language models (VLMs) have demonstrated promising progress in various video understanding tasks. However, their effectiveness in long-form video analysis is constrained by limited context windows. Traditional approaches, such as uniform frame sampling, often inevitably allocate resources to irrelevant content, diminishing their effectiveness in real-world scenarios. In this paper, we introduce BOLT, a method to boost large VLMs without additional training through a comprehensive study of frame selection strategies. First, to enable a more realistic evaluation of VLMs in long-form video understanding, we propose a multi-source retrieval evaluation setting. Our findings reveal that uniform sampling performs poorly in noisy contexts, underscoring the importance of selecting the right frames. Second, we explore several frame selection strategies based on query-frame similarity and analyze their effectiveness at inference time. Our results show that inverse transform sampling yields the most significant performance improvement, increasing accuracy on the Video-MME benchmark from 53.8% to 56.1% and MLVU benchmark from 58.9% to 63.4%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_BOLT_Boost_Large_Vision-Language_Model_Without_Training_for_Long-form_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_BOLT_Boost_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Shuming and Zhao, Chen and Xu, Tianqi and Ghanem, Bernard},
    title     = {BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3318-3327}
}

raw_affiliation:
[{"affiliation": "King Abdullah University of Science and Technology (KAUST)", "city": "Thuwal", "country": "Saudi Arabia"}]


title: Efficient Data Driven Mixture-of-Expert Extraction from Trained Networks
authors: Uranik Berisha, Jens Mehnert, Alexandru Paul Condurache
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20082-20091
cities: ["Stuttgart", "Lubeck"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Berisha_Efficient_Data_Driven_Mixture-of-Expert_Extraction_from_Trained_Networks_CVPR_2025_paper.html

abstract:
Vision Transformers (ViTs) have emerged as the state-of-the-art models in various Computer Vision (CV) tasks, but their high computational and resource demands pose significant challenges. While Mixture of Experts (MoE) can make these models more efficient, they often require costly retraining or even training from scratch. Recent developments aim to reduce these computational costs by leveraging pretrained networks. These have been shown to produce sparse activation patterns in the Multi-Layer Perceptrons (MLPs) of the encoder blocks, allowing for conditional activation of only relevant subnetworks for each sample. Building on this idea, we propose a new method to construct MoE variants from pretrained models. Our approach extracts expert subnetworks from the model's MLP layers post-training in two phases. First, we cluster output activations to identify distinct activation patterns. In the second phase, we use these clusters to extract the corresponding subnetworks responsible for producing them. On ImageNet-1k recognition tasks, we demonstrate that these extracted experts can perform surprisingly well out of the box and require only minimal fine-tuning to regain 98% of the original performance, all while reducing FLOPs and model size, by up to 36% and 32% respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Berisha_Efficient_Data_Driven_Mixture-of-Expert_Extraction_from_Trained_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Berisha_Efficient_Data_Driven_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Berisha_2025_CVPR,
    author    = {Berisha, Uranik and Mehnert, Jens and Condurache, Alexandru Paul},
    title     = {Efficient Data Driven Mixture-of-Expert Extraction from Trained Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20082-20091}
}

raw_affiliation:
[{"affiliation": "Automated Driving Research, Robert Bosch GmbH, 70469 Stuttgart, Germany", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "Institute for Signal Processing, University of L\u00a8ubeck, 23562 L\u00a8ubeck, Germany", "city": "Lubeck", "country": "Germany"}]

affiliations:
["Automated Driving Research, Robert Bosch GmbH, 70469 Stuttgart, Germany", "Institute for Signal Processing, University of L\u00a8ubeck, 23562 L\u00a8ubeck, Germany"]


title: Reference-Based 3D-Aware Image Editing with Triplanes
authors: Bahri Batuhan Bilecen, Yigit Yalin, Ning Yu, Aysegul Dundar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5904-5915
affiliations: ["Bilkent University", "Netflix Eyeline Studios"]
cities: ["Ankara", "Los Angeles"]
countries: ["Turkey", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bilecen_Reference-Based_3D-Aware_Image_Editing_with_Triplanes_CVPR_2025_paper.html

abstract:
Generative Adversarial Networks (GANs) have emerged as powerful tools for high-quality image generation and real image editing by manipulating their latent spaces. Recent advancements in GANs include 3D-aware models such as EG3D, which feature efficient triplane-based architectures capable of reconstructing 3D geometry from single images. However, limited attention has been given to providing an integrated framework for 3D-aware, high-quality, reference-based image editing. This study addresses this gap by exploring and demonstrating the effectiveness of the triplane space for advanced reference-based edits. Our novel approach integrates encoding, automatic localization, spatial disentanglement of triplane features, and fusion learning to achieve the desired edits. We demonstrate how our approach excels across diverse domains, including human faces, 360-degree heads, animal faces, partially stylized edits like cartoon faces, full-body clothing edits, and edits on class-agnostic samples. Our method shows state-of-the-art performance over relevant latent direction, text, and image-guided 2D and 3D-aware diffusion and GAN methods, both qualitatively and quantitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bilecen_Reference-Based_3D-Aware_Image_Editing_with_Triplanes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bilecen_Reference-Based_3D-Aware_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bilecen_2025_CVPR,
    author    = {Bilecen, Bahri Batuhan and Yalin, Yigit and Yu, Ning and Dundar, Aysegul},
    title     = {Reference-Based 3D-Aware Image Editing with Triplanes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5904-5915}
}

raw_affiliation:
[{"affiliation": "Bilkent University", "city": "Ankara", "country": "Turkey"}, {"affiliation": "Netflix Eyeline Studios", "city": "Los Angeles", "country": "USA"}]


authors: Ruojun Xu, Weijie Xi, XiaoDi Wang, Yongbo Mao, Zach Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18260-18269
affiliations: ["Zhejiang University, Dcar", "Dcar", "Dcar", "Dcar", "Dcar"]
cities: ["Hangzhou", "Hangzhou", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_StyleSSP_Sampling_StartPoint_Enhancement_for_Training-free_Diffusion-based_Method_for_Style_CVPR_2025_paper.html

title:
StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer

abstract:
Training-free diffusion-based methods have achieved remarkable success in style transfer, eliminating the need for extensive training or fine-tuning. However, due to the lack of targeted training for style information extraction and constraints on the content image layout, training-free methods often suffer from layout changes of original content and content leakage from style images. Through a series of experiments, we discovered that an effective startpoint in the sampling stage significantly enhances the style transfer process. Based on this discovery, we propose StyleSSP, which focuses on obtaining a better startpoint to address layout changes of original content and content leakage from style image. StyleSSP comprises two key components: (1) Frequency Manipulation: To improve content preservation, we reduce the low-frequency components of the DDIM latent, allowing the sampling stage to pay more attention to the layout of content images; and (2) Negative Guidance via Inversion: To mitigate the content leakage from style image, we employ negative guidance in the inversion stage to ensure that the startpoint of the sampling stage is distanced from the content of style image. Experiments show that StyleSSP surpasses previous training-free style transfer baselines, particularly in preserving original content and minimizing the content leakage from style image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_StyleSSP_Sampling_StartPoint_Enhancement_for_Training-free_Diffusion-based_Method_for_Style_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_StyleSSP_Sampling_StartPoint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Ruojun and Xi, Weijie and Wang, XiaoDi and Mao, Yongbo and Cheng, Zach},
    title     = {StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18260-18269}
}

raw_affiliation:
[{"affiliation": "Zhejiang University, Dcar", "city": "Hangzhou", "country": "China"}, {"affiliation": "Dcar", "city": "Hangzhou", "country": "China"}, {"affiliation": "Dcar", "city": "Beijing", "country": "China"}, {"affiliation": "Dcar", "city": "Beijing", "country": "China"}, {"affiliation": "Dcar", "city": "Beijing", "country": "China"}]


title: PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking
authors: Zekai Shao, Yufan Hu, Bin Fan, Hongmin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22089-22098
affiliations: ["School of Intelligence Science and Technology, University of Science and Technology Beijing"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_PURA_Parameter_Update-Recovery_Test-Time_Adaption_for_RGB-T_Tracking_CVPR_2025_paper.html

abstract:
Maintaining stable tracking of objects in domain shift scenarios is crucial for RGB-T tracking, prompting us to explore the use of unlabeled test sample information for effective online model adaptation. However, current Test-Time Adaptation (TTA) methods in RGB-T tracking dramatically change the model's internal parameters during long-term adaptation. At the same time, the gradient computations involved in the optimization process impose a significant computational burden. To address these challenges, we propose a Parameter Update-Recovery Adaptation (PURA) framework based on parameter decomposition. Firstly, our fast parameter update strategy adjusts model parameters using statistical information from test samples without requiring gradient calculations, ensuring consistency between the model and test data distribution. Secondly, our parameter decomposition recovery employs orthogonal decomposition to identify the principal update direction and recover parameters in this direction, aiding in the retention of critical knowledge. Finally, we leverage the information obtained from decomposition to provide feedback on the momentum during the update phase, ensuring a stable updating process. Experimental results demonstrate that PURA outperforms current state-of-the-art methods across multiple datasets, validating its effectiveness. The project page is at https://melantech.github.io/PURA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_PURA_Parameter_Update-Recovery_Test-Time_Adaption_for_RGB-T_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_PURA_Parameter_Update-Recovery_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Zekai and Hu, Yufan and Fan, Bin and Liu, Hongmin},
    title     = {PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22089-22098}
}

raw_affiliation:
[{"affiliation": "School of Intelligence Science and Technology, University of Science and Technology Beijing", "city": "Beijing", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1592-1601
cities: ["Beijing", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_One_is_Plenty_A_Polymorphic_Feature_Interpreter_for_Immutable_Heterogeneous_CVPR_2025_paper.html

title:
One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception

authors:
Yuchen Xia, Quan Yuan, Guiyang Luo, Xiaoyuan Fu, Yang Li, Xuanhan Zhu, Tianyou Luo, Siheng Chen, Jinglin Li

abstract:
Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity in collaborative perception, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in their exchanged intermediate features without modifying the perception networks. Most existing methods bridge the semantic gap through interpreters. However, they either require training a new interpreter for each new agent type, limiting extensibility, or rely on a two-stage interpretation via an intermediate standardized semantic space, causing cumulative semantic loss. To achieve both extensibility in immutable heterogeneous scenarios and low-loss feature interpretation, we propose PolyInter, a polymorphic feature interpreter. It contains an extension point through which emerging new agents can seamlessly integrate by overriding only their specific prompts, which are learnable parameters intended to guide the interpretation, while reusing PolyInter's remaining parameters. By leveraging polymorphism, our design ensures that single interpreter is sufficient to accommodate diverse agents and interpret their features into the ego agent's semantic space. Experiments conducted on OPV2V dataset demonstrate that PolyInter improves collaborative perception precision by up to 11.1% compared to SOTA interpreters, while comparable results can be achieved by training only 1.4% of PolyInter's parameters when adapting to new agents.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_One_is_Plenty_A_Polymorphic_Feature_Interpreter_for_Immutable_Heterogeneous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_One_is_Plenty_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Yuchen and Yuan, Quan and Luo, Guiyang and Fu, Xiaoyuan and Li, Yang and Zhu, Xuanhan and Luo, Tianyou and Chen, Siheng and Li, Jinglin},
    title     = {One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1592-1601}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Cooperative Medianet Innovation Center, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications", "Cooperative Medianet Innovation Center, Shanghai Jiao Tong University"]


title: Towards All-in-One Medical Image Re-Identification
authors: Yuan Tian, Kaiyuan Ji, Rongzhao Zhang, Yankai Jiang, Chunyi Li, Xiaosong Wang, Guangtao Zhai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30774-30786
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Towards_All-in-One_Medical_Image_Re-Identification_CVPR_2025_paper.html

abstract:
Medical image re-identification (MedReID) is under-explored so far, despite its critical applications in personalized healthcare and privacy protection.In this paper, we introduce a thorough benchmark and a unified model for this problem.First, to handle various medical modalities, we propose a novel Continuous Modality-based Parameter Adapter (ComPA). ComPA condenses medical content into a continuous modality representation and dynamically adjusts the modality-agnostic model with modality-specific parameters at runtime. This allows a single model to adaptively learn and process diverse modality data.Furthermore, we integrate medical priors into our model by aligning it with a bag of pre-trained medical foundation models, in terms of the differential features.Compared to single-image feature, modeling the inter-image difference better fits the re-identification problem, which involves discriminating multiple images.We evaluate the proposed model against 25 foundation models and 8 large multi-modal language models across 11 image datasets, demonstrating consistently superior performance.Additionally, we deploy the proposed MedReID technique to two real-world applications, i.e., history-augmented personalized diagnosis and medical privacy protection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Towards_All-in-One_Medical_Image_Re-Identification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Towards_All-in-One_Medical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Yuan and Ji, Kaiyuan and Zhang, Rongzhao and Jiang, Yankai and Li, Chunyi and Wang, Xiaosong and Zhai, Guangtao},
    title     = {Towards All-in-One Medical Image Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30774-30786}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Communication and Electronic Engineering, East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute of Image Communication and Network Engineering, Shanghai Jiao Tong Unversity", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "School of Communication and Electronic Engineering, East China Normal University", "Institute of Image Communication and Network Engineering, Shanghai Jiao Tong Unversity"]


authors: Muzhi Zhu, Yuzhuo Tian, Hao Chen, Chunluan Zhou, Qingpei Guo, Yang Liu, Ming Yang, Chunhua Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3686-3696
affiliations: ["Zhejiang University", "Ant Group"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_SegAgent_Exploring_Pixel_Understanding_Capabilities_in_MLLMs_by_Imitating_Human_CVPR_2025_paper.html

title:
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories

abstract:
While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications. Current evaluation tasks like VQA and visual grounding remain too coarse to assess fine-grained pixel comprehension accurately. Though segmentation is foundational for pixel-level understanding, existing methods often require MLLMs to generate implicit tokens, decoded through external pixel decoders. This approach disrupts the MLLM's text output space, potentially compromising language capabilities and reducing flexibility and extensibility, while failing to reflect the model's intrinsic pixel-level understanding.Thus, We introduce the Human-Like Mask Annotation Task (HLMAT), a new paradigm where MLLMs mimic human annotators using interactive segmentation tools. Modeling segmentation as a multi-step Markov Decision Process, HLMAT enables MLLMs to iteratively generate text-based click points, achieving high-quality masks without architectural changes or implicit tokens. Through this setup, we develop SegAgent, a model fine-tuned on human-like annotation trajectories, which achieves performance comparable to SOTA methods and supports additional tasks like mask refinement and annotation filtering.HLMAT provides a protocol for assessing fine-grained pixel understanding in MLLMs and introduces a vision-centric, multi-step decision-making task that facilitates exploration of MLLMs' visual reasoning abilities. Our adaptations of policy improvement method StaR and PRM guided tree search further enhance model robustness in complex segmentation tasks, laying a foundation for future advancements in fine-grained visual perception and multi-step decision-making for MLLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SegAgent_Exploring_Pixel_Understanding_Capabilities_in_MLLMs_by_Imitating_Human_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_SegAgent_Exploring_Pixel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Muzhi and Tian, Yuzhuo and Chen, Hao and Zhou, Chunluan and Guo, Qingpei and Liu, Yang and Yang, Ming and Shen, Chunhua},
    title     = {SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3686-3696}
}

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


title: Motions as Queries: One-Stage Multi-Person Holistic Human Motion Capture
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17529-17539
cities: ["Hangzhou", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Motions_as_Queries_One-Stage_Multi-Person_Holistic_Human_Motion_Capture_CVPR_2025_paper.html

authors:
Kenkun Liu, Yurong Fu, Weihao Yuan, Jing Lin, Peihao Li, Xiaodong Gu, Lingteng Qiu, Haoqian Wang, Zilong Dong, Xiaoguang Han

abstract:
Existing methods for capturing multi-person holistic human motions from a monocular video usually involve integrating the detector, the tracker, and the human pose & shape estimator into a cascaded system. Differently, we develop a one-stage multi-person holistic human motion capture system, which 1) employs only one network, enabling significant benefits from the end-to-end training on a large-scale dataset; 2) enables performance improving of the tracking module during training, avoiding being limited by a pre-trained tracker; 3) captures the motions of all individuals within a single shot, rather than tracking and estimating each person sequentially. In this system, each query within a temporal cross-attention module is responsible for the long motion of a specific individual, implicitly aggregating individual-specific information throughout the entire video. To further boost the proposed system from end-to-end training, we also construct a synthetic human video dataset, with multi-person and whole-body annotations. Extensive experiments across different datasets demonstrate both the efficacy and the efficiency of both the proposed method and the dataset. The code of our method will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Motions_as_Queries_One-Stage_Multi-Person_Holistic_Human_Motion_Capture_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Motions_as_Queries_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Kenkun and Fu, Yurong and Yuan, Weihao and Lin, Jing and Li, Peihao and Gu, Xiaodong and Qiu, Lingteng and Wang, Haoqian and Dong, Zilong and Han, Xiaoguang},
    title     = {Motions as Queries: One-Stage Multi-Person Holistic Human Motion Capture},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17529-17539}
}

raw_affiliation:
[{"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Tongyi Lab, Alibaba Group", "The Chinese University of Hong Kong, Shenzhen", "Tsinghua University"]


title: SceneCrafter: Controllable Multi-View Driving Scene Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6812-6822
affiliations: ["Waymo", "University of Texas at Austin", "Johns Hopkins University", "Google DeepMind"]
cities: ["Mountain View", "Austin", "Baltimore", "Mountain View"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_SceneCrafter_Controllable_Multi-View_Driving_Scene_Editing_CVPR_2025_paper.html

authors:
Zehao Zhu, Yuliang Zou, Chiyu Max Jiang, Bo Sun, Vincent Casser, Xiukun Huang, Jiahao Wang, Zhenpei Yang, Ruiqi Gao, Leonidas Guibas, Mingxing Tan, Dragomir Anguelov

abstract:
Simulation is crucial for developing and evaluating autonomous vehicle (AV) systems. Recent literature builds on a new generation of generative models to synthesize highly realistic images for full-stack simulation. However, purely synthetically generated scenes are not grounded in reality and have difficulty in inspiring confidence in the relevance of its outcomes. Editing models, on the other hand, leverage source scenes from real driving logs, and enable the simulation of different traffic layouts, behaviors, and operating conditions such as weather and time of day. While image editing is an established topic in computer vision, it presents fresh sets of challenges in driving simulation: (1) the need for cross-camera 3D consistency, (2) learning "empty street" priors from driving data with foreground occlusions, and (3) obtaining paired image tuples of varied editing conditions while preserving consistent layout and geometry. To address these challenges, we propose SceneCrafter, a versatile editor for realistic 3D-consistent manipulation of driving scenes captured from multiple cameras. We build on recent advancements in multi-view diffusion models, using a fully controllable framework that scales seamlessly to multi-modality conditions like weather, time of day, agent boxes and high-definition maps. To generate paired data for supervising the editing model, we propose a novel framework on top of Prompt-to-Prompt to generate geometrically consistent synthetic paired data with global edits. We also introduce an alpha-blending framework to synthesize data with local edits, leveraging a model trained on empty street priors through novel masked training and multi-view repaint paradigm. SceneCrafter demonstrates powerful editing capabilities and achieves state-of-the-art realism, controllability, 3D consistency, and scene editing quality compared to existing baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SceneCrafter_Controllable_Multi-View_Driving_Scene_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_SceneCrafter_Controllable_Multi-View_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Zehao and Zou, Yuliang and Jiang, Chiyu Max and Sun, Bo and Casser, Vincent and Huang, Xiukun and Wang, Jiahao and Yang, Zhenpei and Gao, Ruiqi and Guibas, Leonidas and Tan, Mingxing and Anguelov, Dragomir},
    title     = {SceneCrafter: Controllable Multi-View Driving Scene Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6812-6822}
}

raw_affiliation:
[{"affiliation": "Waymo", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


title: AMO Sampler: Enhancing Text Rendering with Overshooting
authors: Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu, Hongliang Fei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13157-13166
affiliations: ["Google", "University of Texas at Austin"]
cities: ["Mountain View", "Austin"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_AMO_Sampler_Enhancing_Text_Rendering_with_Overshooting_CVPR_2025_paper.html

abstract:
Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Open-source models like Stable Diffusion 3 (SD3), Flux, and AuraFlow often struggle with accurate text depiction, resulting in misspelled or inconsistent text. We introduce a training-free method with minimal computational overhead that significantly enhances text rendering quality. Specifically, we introduce an overshooting sampler for a pretrained RF model, by alternating between over-simulating the learned ordinary differential equation (ODE) and reintroducing noise. Compared to the Euler sampler, the overshooting sampler effectively introduces an extra Langevin dynamics term that can help correct the compounding error from successive Euler steps and therefore improve the text rendering. However, when the overshooting strength is high, we observe over-smoothing artifacts on the generated images. To address this issue, we adaptively control the strength of the overshooting for each image patch according to their attention score with the text content. We name the proposed sampler Attention Modulated Overshooting sampler (AMO). AMO demonstrates a 32.3% and 35.9% improvement in text rendering accuracy on SD3 and Flux without compromising overall image quality or increasing inference cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_AMO_Sampler_Enhancing_Text_Rendering_with_Overshooting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_AMO_Sampler_Enhancing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Xixi and Xu, Keyang and Liu, Bo and Liu, Qiang and Fei, Hongliang},
    title     = {AMO Sampler: Enhancing Text Rendering with Overshooting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13157-13166}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}]


title: ImViD: Immersive Volumetric Videos for Enhanced VR Engagement
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16554-16564
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImViD_Immersive_Volumetric_Videos_for_Enhanced_VR_Engagement_CVPR_2025_paper.html

authors:
Zhengxian Yang, Shi Pan, Shengqi Wang, Haoxiang Wang, Li Lin, Guanjun Li, Zhengqi Wen, Borong Lin, Jianhua Tao, Tao Yu

abstract:
User engagement is greatly enhanced by fully immersive multimodal experiences that combine visual and auditory stimuli. Consequently, the next frontier in VR/AR technologies lies in immersive volumetric videos with complete scene capture, large 6-DoF interactive space, Multi-modal feedback, and high resolution & frame-rate contents. To stimulate the reconstruction of immersive volumetric videos, we introduce ImViD, a multi-view, multi-modal dataset featuring complete space-oriented data capture and various indoor/outdoor scenarios. Our capture rig supports multi-view video-audio capture while on the move, a capability absent in existing datasets, which significantly enhances the completeness, flexibility, and efficiency of data capture. The captured multi-view videos (with synchronized audios) are in 5K resolution at 60FPS, lasting from 1-5 minutes, and include rich foreground-background elements, and complex dynamics. We benchmark existing methods using our dataset and establish a base pipeline for constructing immersive volumetric videos from multi-view audiovisual inputs for 6-DoF multimodal immersive VR experiences. The benchmark and the reconstruction and interaction results demonstrate the effectiveness of our dataset and baseline method, which we believe will stimulate future research on immersive volumetric video production.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImViD_Immersive_Volumetric_Videos_for_Enhanced_VR_Engagement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImViD_Immersive_Volumetric_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhengxian and Pan, Shi and Wang, Shengqi and Wang, Haoxiang and Lin, Li and Li, Guanjun and Wen, Zhengqi and Lin, Borong and Tao, Jianhua and Yu, Tao},
    title     = {ImViD: Immersive Volumetric Videos for Enhanced VR Engagement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16554-16564}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Migu Beijing Research Institute", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Science", "city": "Beijing", "country": "China"}]

affiliations:
["Tsinghua University", "Migu Beijing Research Institute", "Institute of Automation, Chinese Academy of Science"]


title: Integral Fast Fourier Color Constancy
authors: Wenjun Wei, Yanlin Qian, Huaian Chen, Junkang Dai, Yi Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26420-26429
affiliations: ["University of Science and Technology of China", "DJI Technology Co., Ltd"]
cities: ["Hefei", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_Integral_Fast_Fourier_Color_Constancy_CVPR_2025_paper.html

abstract:
Traditional auto white balance (AWB) algorithms typically assume a single global illuminant source, which leads to color distortions in multi-illuminant scenes. While recent neural network-based methods have shown excellent accuracy in such scenarios, their high parameter count and computational demands limit their practicality for real-time video applications. The Fast Fourier Color Constancy (FFCC) algorithm was proposed for single-illuminant-source scenes, predicting a global illuminant source with high efficiency. However, it cannot be directly applied to multi-illuminant scenarios unless specifically modified. To address this, we propose Integral Fast Fourier Color Constancy (IFFCC), an extension of FFCC tailored for multi-illuminant scenes. IFFCC leverages the proposed integral UV histogram to accelerate histogram computations across all possible regions in Cartesian space and parallelizes Fourier-based convolution operations, resulting in a spatially-smooth illumination map. This approach enables high-accuracy, real-time AWB in multi-illuminant scenes. Extensive experiments show that IFFCC achieves accuracy that is on par with or surpasses that of pixel-level neural networks, while reducing the parameter count by over 400x and processing speed by 20 - 100x faster than network-based approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_Integral_Fast_Fourier_Color_Constancy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_Integral_Fast_Fourier_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Wenjun and Qian, Yanlin and Chen, Huaian and Dai, Junkang and Jin, Yi},
    title     = {Integral Fast Fourier Color Constancy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26420-26429}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "DJI Technology Co., Ltd", "city": "Shenzhen", "country": "China"}]


title: I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models
authors: Dongnan Gui, Xun Guo, Wengang Zhou, Yan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12595-12604
affiliations: ["University of Science and Technology of China", "Microsoft Research Asia"]
cities: ["Hefei", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gui_I2VGuard_Safeguarding_Images_against_Misuse_in_Diffusion-based_Image-to-Video_Models_CVPR_2025_paper.html

abstract:
Recent advances in image-to-video generation have enabled animation of still images and offered pixel-level controllability. While these models hold great potential to transform single images into vivid and dynamic videos, they also carry risks of misuse that could impact privacy, security, and copyright protection. This paper proposes a novel approach that applies imperceptible perturbations on images to degrade the quality of the generated videos, thereby protecting images from misuse in white-box image-to-video diffusion models. Specifically, we function our approach as an adversarial attack, incorporating spatial, temporal, and diffusion attack modules.  The spatial attack shifts image features from their original distribution to a lower-quality target distribution, reducing visual fidelity. The temporal attack disrupts coherent motion by interfering with temporal attention maps that guide motion generation. To enhance the robustness of our approach across different models, we further propose a diffusion attack module leveraging contrastive loss. Our approach can be easily integrated with mainstream diffusion-based I2V models. Extensive experiments on SVD, CogVideoX, and ControlNeXt demonstrate that our method significantly impairs generation quality in terms of visual clarity and motion consistency, while introducing only minimal artifacts to the images. To the best of our knowledge, we are the first to explore adversarial attacks on image-to-video generation for security purposes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gui_I2VGuard_Safeguarding_Images_against_Misuse_in_Diffusion-based_Image-to-Video_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gui_I2VGuard_Safeguarding_Images_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gui_2025_CVPR,
    author    = {Gui, Dongnan and Guo, Xun and Zhou, Wengang and Lu, Yan},
    title     = {I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12595-12604}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]


title: Saliuitl: Ensemble Salience Guided Recovery of Adversarial Patches against CNNs
authors: Mauricio Byrd Victorica, György Dán, Henrik Sandberg
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20360-20369
raw_affiliation: [{"affiliation": "KTH Royal Institute of Technology", "city": "Stockholm", "country": "Sweden"}]
affiliations: ["KTH Royal Institute of Technology"]
cities: ["Stockholm"]
countries: ["Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Victorica_Saliuitl_Ensemble_Salience_Guided_Recovery_of_Adversarial_Patches_against_CNNs_CVPR_2025_paper.html

abstract:
Adversarial patches are capable of misleading computer vision systems based on convolutional neural networks. Existing recovery methods suffer of at least one of three fundamental shortcomings: no information about the presence of patches in the scene,  inability to efficiently handle noncontiguous patch attacks, and a strong reliance on fixed saliency thresholds. We propose Saliuitl, a recovery method independent of the number of patches and their shape, which unlike prior works, explicitly detects patch attacks before attempting recovery. In our approach, detection is based on the attributes of a binarized feature map ensemble, which is generated by using an ensemble of saliency thresholds. If an attack is detected, Saliuitl recovers clean predictions locating patches guided by an ensemble of binarized feature maps and inpainting them. We evaluate Saliuitl on widely used object detection and image classification benchmarks from the adversarial patch literature, and our results show that compared to  recent state-of-the-art defenses, Saliuitl achieves a recovery rate up to 97.81 and 42.63 percentage points higher at the same rate of lost predictions for image classification and object detection, respectively. By design, Saliuitl has low computational complexity and is robust to adaptive white-box attacks. Our code is available at https://github.com/Saliuitl/Saliuitl/tree/main.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Victorica_Saliuitl_Ensemble_Salience_Guided_Recovery_of_Adversarial_Patches_against_CNNs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Victorica_Saliuitl_Ensemble_Salience_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Victorica_2025_CVPR,
    author    = {Victorica, Mauricio Byrd and D\'an, Gy\"orgy and Sandberg, Henrik},
    title     = {Saliuitl: Ensemble Salience Guided Recovery of Adversarial Patches against CNNs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20360-20369}
}


title: HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery
authors: Yuto Matsubara, Ko Nishino
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6415-6424
affiliations: ["Graduate School of Informatics, Kyoto University"]
cities: ["Kyoto"]
countries: ["Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Matsubara_HeatFormer_A_Neural_Optimizer_for_Multiview_Human_Mesh_Recovery_CVPR_2025_paper.html

abstract:
We introduce a novel method for human shape and pose recovery that can fully leverage multiple static views. We target fixed-multiview people monitoring, including elderly care and safety monitoring, in which cameras can be installed at the corners of a room or an open space but whose configuration may vary depending on the environment. Our key idea is to formulate it as neural optimization. We achieve this with HeatFormer, a neural optimizer that iteratively refines the SMPL parameters given multiview images. HeatFormer realizes this SMPL parameter estimation as heatmap generation and alignment with a novel transformer encoder and decoder. Our formulation makes HeatFormer fundamentally agnostic to the number of cameras, their configuration, and calibration. We demonstrate the effectiveness of HeatFormer including its accuracy, robustness to occlusion, and generalizability through an extensive set of experiments. We believe HeatFormer can serve a key role in passive human behavior modeling.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Matsubara_HeatFormer_A_Neural_Optimizer_for_Multiview_Human_Mesh_Recovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Matsubara_HeatFormer_A_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Matsubara_2025_CVPR,
    author    = {Matsubara, Yuto and Nishino, Ko},
    title     = {HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6415-6424}
}

raw_affiliation:
[{"affiliation": "Graduate School of Informatics, Kyoto University", "city": "Kyoto", "country": "Japan"}]


title: ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
authors: Yuhang Yang, Jinhong Deng, Wen Li, Lixin Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29968-29978
affiliations: ["University of Electronic Science and Technology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ResCLIP_Residual_Attention_for_Training-free_Dense_Vision-language_Inference_CVPR_2025_paper.html

abstract:
While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle with dense predictions. Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlation attention, (e.g., query-query and key-key attention). However, these methods overlook the cross-correlation attention (query-key) properties, which capture the rich spatial correspondence. In this paper, we reveal that the cross-correlation of self-attention in non-final layers of CLIP also exhibits localization properties. Therefore, we propose the Residual Cross-correlation Self-attention (RCS) module, which leverages the cross-correlation self-attention from intermediate layers to remold the attention in the final block. The RCS module effectively reorganizes spatial information, unleashing the localization potential within CLIP for dense vision-language inference. Furthermore, to enhance the focus on regions of the same categories and local consistency, we propose the Semantic Feedback Refinement (SFR) module, which utilizes semantic segmentation maps to further adjust the attention scores. By integrating these two strategies, our method, termed ResCLIP, can be easily incorporated into existing approaches as a plug-and-play module, significantly boosting their performance in dense vision-language inference. Extensive experiments across multiple standard benchmarks demonstrate that our method surpasses state-of-the-art training-free methods, validating the effectiveness of the proposed approach. Code is available at https://github.com/yvhangyang/ResCLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ResCLIP_Residual_Attention_for_Training-free_Dense_Vision-language_Inference_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ResCLIP_Residual_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yuhang and Deng, Jinhong and Li, Wen and Duan, Lixin},
    title     = {ResCLIP: Residual Attention for Training-free Dense Vision-language Inference},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29968-29978}
}

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


title: GPS as a Control Signal for Image Generation
authors: Chao Feng, Ziyang Chen, Aleksander Holynski, Alexei A. Efros, Andrew Owens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2766-2778
affiliations: ["University of Michigan", "UC Berkeley"]
cities: ["Ann Arbor", "Berkeley"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_GPS_as_a_Control_Signal_for_Image_Generation_CVPR_2025_paper.html

abstract:
We show that the GPS tags contained in photo metadata provide a useful control signal for image generation. We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city. In particular, we train a diffusion model to generate images conditioned on both GPS and text. The learned model generates images that capture the distinctive appearance of different neighborhoods, parks, and landmarks. We also extract 3D models from 2D GPS-to-image models through score distillation sampling, using GPS conditioning to constrain the appearance of the reconstruction from each viewpoint. Our evaluations suggest that our GPS-conditioned models successfully learn to generate images that vary based on location, and that GPS conditioning improves estimated 3D structure.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_GPS_as_a_Control_Signal_for_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_GPS_as_a_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Chao and Chen, Ziyang and Holynski, Aleksander and Efros, Alexei A. and Owens, Andrew},
    title     = {GPS as a Control Signal for Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2766-2778}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10360-10371
cities: ["Irvine", "Hangzhou", "Cambridge", "Columbus", "Beijing", "Zhengzhou"]
countries: ["USA", "China", "USA", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_CPath-Omni_A_Unified_Multimodal_Foundation_Model_for_Patch_and_Whole_CVPR_2025_paper.html

title:
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

authors:
Yuxuan Sun, Yixuan Si, Chenglu Zhu, Xuan Gong, Kai Zhang, Pingyi Chen, Ye Zhang, Zhongyi Shui, Tao Lin, Lin Yang

abstract:
The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learned knowledge across patches and WSIs and resulting in redundant models. In this work, we introduce CPath-Omni, the first 15B parameter LMM that unifies patch and WSI analysis, consolidating a variety of tasks at both levels, including classification, visual question answering, captioning, and visual referring prompting. Extensive experiments demonstrate that CPath-Omni achieves state-of-the-art (SOTA) performance across seven diverse tasks on 39 out of 42 datasets, outperforming or matching task-specific models trained for individual tasks. Additionally, we develop a specialized pathology CLIP-based visual processor for CPath-Omni, CPath-CLIP, which, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model, which achieves SOTA performance on nine zero-shot and four few-shot datasets. Our findings highlight CPath-Omni's ability to unify diverse pathology tasks, demonstrating its potential to streamline and advance the field of foundation model in pathology. The code and model are available at https://github.com/PathFoundation/CPath-Omni.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_CPath-Omni_A_Unified_Multimodal_Foundation_Model_for_Patch_and_Whole_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_CPath-Omni_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Yuxuan and Si, Yixuan and Zhu, Chenglu and Gong, Xuan and Zhang, Kai and Chen, Pingyi and Zhang, Ye and Shui, Zhongyi and Lin, Tao and Yang, Lin},
    title     = {CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10360-10371}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Irvine", "country": "USA"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Center for Interdisciplinary Research and Innovation, Muyuan", "city": "Zhengzhou", "country": "China"}]

affiliations:
["Zhejiang University", "Westlake University", "Harvard University", "The Ohio State University", "University of Chinese Academy of Sciences", "Center for Interdisciplinary Research and Innovation, Muyuan"]


title: OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation
authors: Xiao Cui, Yulei Qin, Wengang Zhou, Hongsheng Li, Houqiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15245-15254
cities: ["Hefei", "Hong Kong", "Hong Kong", "Unknown"]
countries: ["China", "China", "China", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_OPTICAL_Leveraging_Optimal_Transport_for_Contribution_Allocation_in_Dataset_Distillation_CVPR_2025_paper.html

abstract:
The demands for increasingly large-scale datasets pose substantial storage and computation challenges to building deep learning models. Dataset distillation methods, especially those via sample generation techniques, rise in response to condensing large original datasets into small synthetic ones while preserving critical information. Existing subset synthesis methods simply minimize the homogeneous distance where uniform contributions from all real instances are allocated to shaping each synthetic sample. We demonstrate that such equal allocation fails to consider the instance-level relationship between each real-synthetic pair and gives rise to insufficient modeling of geometric structural nuances between the distilled and original sets. In this paper, we propose a novel framework named OPTICAL to reformulate the homogeneous distance minimization into a bi-level optimization problem via matching-and-approximating. In the matching step, we leverage optimal transport matrix to dynamically allocate contributions from real instances. Subsequently, we polish the generated samples in accordance with the established allocation scheme for approximating the real ones. Such a strategy better measures intricate geometric characteristics and handles intra-class variations for high fidelity of data distillation. Extensive experiments across seven datasets and three model architectures demonstrate our method's versatility and effectiveness. Its plug-and-play characteristic makes it compatible with a wide range of distillation frameworks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_OPTICAL_Leveraging_Optimal_Transport_for_Contribution_Allocation_in_Dataset_Distillation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Xiao and Qin, Yulei and Zhou, Wengang and Li, Hongsheng and Li, Houqiang},
    title     = {OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15245-15254}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "CUHK MMLab", "city": "Hong Kong", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}]

affiliations:
["University of Science and Technology of China", "CUHK MMLab", "CPII under InnoHK", "Independent Researcher"]


title: MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
authors: Vladimir Yugay, Theo Gevers, Martin R. Oswald
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6741-6750
raw_affiliation: [{"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}]
affiliations: ["University of Amsterdam"]
cities: ["Amsterdam"]
countries: ["Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yugay_MAGiC-SLAM_Multi-Agent_Gaussian_Globally_Consistent__SLAM_CVPR_2025_paper.html

abstract:
Simultaneous localization and mapping (SLAM) systems with novel view synthesis capabilities are widely used in computer vision, with applications in augmented reality, robotics, and autonomous driving. However, existing approaches are limited to single-agent operation. Recent work has addressed this problem using a distributed neural scene representation. Unfortunately, existing methods are slow, cannot accurately render real-world data, are restricted to two agents, and have limited tracking accuracy. In contrast, we propose a rigidly deformable 3D Gaussian-based scene representation that dramatically speeds up the system. However, improving tracking accuracy and reconstructing a globally consistent map from multiple agents remains challenging due to trajectory drift and discrepancies across agents' observations. Therefore, we propose new tracking and map-merging mechanisms and integrate loop closure in the Gaussian-based SLAM pipeline. We evaluate \ours on synthetic and real-world datasets and find it more accurate and faster than the state of the art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yugay_MAGiC-SLAM_Multi-Agent_Gaussian_Globally_Consistent__SLAM_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yugay_2025_CVPR,
    author    = {Yugay, Vladimir and Gevers, Theo and Oswald, Martin R.},
    title     = {MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent  SLAM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6741-6750}
}


authors: Rui Qian, Shuangrui Ding, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24045-24055
cities: ["Hong Kong", "Shanghai", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_Dispider_Enabling_Video_LLMs_with_Active_Real-Time_Interaction_via_Disentangled_CVPR_2025_paper.html

title:
Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction

abstract:
Active Real-time interaction with video LLMs introduces a new paradigm for human-computer interaction, where the model not only understands user intent but also responds while continuously processing streaming video on the fly. Unlike offline video LLMs, which analyze the entire video before answering questions, active real-time interaction requires three capabilities: 1) Perception: real-time video monitoring and interaction capturing. 2) Decision: raising proactive interaction in proper situations, 3) Reaction: continuous interaction with users. However, inherent conflicts exist among the desired capabilities. The Decision and Reaction require a contrary Perception scale and grain, and the autoregressive decoding blocks the real-time Perception and Decision during the Reaction. To unify the conflicted capabilities within a harmonious system, we present Dispider, a solution built on a Disentangled Perception, Decision, and Reaction framework. Dispider features a lightweight Proactive Streaming Video Processing module that tracks the video stream and identifies optimal moments for interaction. Once the interaction is triggered, an asynchronous Precise Interaction module provides detailed responses, while the processing module continues to monitor the video in the meantime. Our disentangled and asynchronous design ensures timely, contextually accurate, and computationally efficient responses, making Dispider ideal for active real-time interaction for long-duration video streams. Experiments prove that Dispider outperforms existing methods not only in its superior understanding of video content in conventional video QA settings, but also in proactive response capability and temporal awareness under the streaming setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_Dispider_Enabling_Video_LLMs_with_Active_Real-Time_Interaction_via_Disentangled_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Rui and Ding, Shuangrui and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and Lin, Dahua and Wang, Jiaqi},
    title     = {Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24045-24055}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Chinese University of Hong Kong", "Shanghai AI Laboratory", "Shanghai Innovation Institute", "CPII under InnoHK"]


title: NTClick: Achieving Precise Interactive Segmentation With Noise-tolerant Clicks
authors: Chenyi Zhang, Ting Liu, Xiaochao Qu, Luoqi Liu, Yao Zhao, Yunchao Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8921-8930
cities: ["Beijing", "Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_NTClick_Achieving_Precise_Interactive_Segmentation_With_Noise-tolerant_Clicks_CVPR_2025_paper.html

abstract:
Interactive segmentation is a pivotal task in computer vision, focused on predicting precise masks with minimal user input. Although the click has recently become the most prevalent form of interaction due to its flexibility and efficiency, its advantages diminish as the complexity and details of target objects increase because it's time-consuming and user-unfriendly to precisely locate and click on narrow, fine regions. To tackle this problem, we propose NTClick, a powerful click-based interactive segmentation method capable of predicting accurate masks even with imprecise user clicks when dealing with intricate targets. We first introduce a novel interaction form called Noist-tolerant Click, a type of click that does not require user's precise localization when selecting fine regions. Then, we design a two-stage workflow, consisting of an Explicit Coarse Perception network for initial estimation and a High Resolution Refinement network for final classification. Quantitative results across extensive datasets demonstrate that NTClick not only maintains an efficient and flexible interaction mode but also significantly outperforms existing methods in segmentation accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_NTClick_Achieving_Precise_Interactive_Segmentation_With_Noise-tolerant_Clicks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_NTClick_Achieving_Precise_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chenyi and Liu, Ting and Qu, Xiaochao and Liu, Luoqi and Zhao, Yao and Wei, Yunchao},
    title     = {NTClick: Achieving Precise Interactive Segmentation With Noise-tolerant Clicks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8921-8930}
}

raw_affiliation:
[{"affiliation": "Institute of Information Science, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "MT Lab, Meitu Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "Visual Intelligence + X International Joint Laboratory of the Ministry of Education", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Institute of Information Science, Beijing Jiaotong University", "MT Lab, Meitu Inc.", "Visual Intelligence + X International Joint Laboratory of the Ministry of Education", "Pengcheng Laboratory"]


title: Show and Segment: Universal Medical Image Segmentation via In-Context Learning
authors: Yunhe Gao, Di Liu, Zhuowei Li, Yunsheng Li, Dongdong Chen, Mu Zhou, Dimitris N. Metaxas
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20830-20840
affiliations: ["Microsoft GenAI", "Rutgers University"]
cities: ["Redmond", "Piscataway"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Show_and_Segment_Universal_Medical_Image_Segmentation_via_In-Context_Learning_CVPR_2025_paper.html

abstract:
Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made significant advances, current approaches struggle to generalize as they require task-specific training or fine-tuning on unseen classes. We present Iris, a novel In-context Reference Image guided Segmentation framework that enables flexible adaptation to novel tasks through the use of reference examples without fine-tuning. At its core, Iris features a lightweight context task encoding module that distills task-specific information from reference context image-label pairs. This rich context embedding information is used to guide the segmentation of target objects. Given a decoupled architecture on 3D data processing, Iris supports diverse inference strategies including one-shot inference, context example ensemble, object-level context example retrieval, and in-context tuning. Through comprehensive evaluation across twelve datasets, we demonstrate that Iris performs strongly compared to specialized supervised models on in-distribution tasks. On seven held-out dataset, Iris shows superior generalization to out-of-distribution data and unseen classes. Further, Iris's task encoding module can automatically discover anatomical relationships across datasets and modalities, offering insights into cross-modality medical objects without explicit anatomical supervision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Show_and_Segment_Universal_Medical_Image_Segmentation_via_In-Context_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Show_and_Segment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Yunhe and Liu, Di and Li, Zhuowei and Li, Yunsheng and Chen, Dongdong and Zhou, Mu and Metaxas, Dimitris N.},
    title     = {Show and Segment: Universal Medical Image Segmentation via In-Context Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20830-20840}
}

raw_affiliation:
[{"affiliation": "Microsoft GenAI", "city": "Redmond", "country": "USA"}, {"affiliation": "Rutgers University", "city": "Piscataway", "country": "USA"}]


title: MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model
authors: Chenjie Cao, Chaohui Yu, Shang Liu, Fan Wang, Xiangyang Xue, Yanwei Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6045-6056
affiliations: ["Fudan University", "DAMO Academy, Alibaba Group", "Hupan Lab"]
cities: ["Shanghai", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_MVGenMaster_Scaling_Multi-View_Generation_from_Any_Image_via_3D_Priors_CVPR_2025_paper.html

abstract:
We introduce MVGenMaster, a multi-view diffusion model enhanced with 3D priors to address versatile Novel View Synthesis (NVS) tasks. MVGenMaster leverages 3D priors that are warped using metric depth and camera poses, significantly enhancing both generalization and 3D consistency in NVS. Our model features a simple yet effective pipeline that can generate up to 100 novel views conditioned on variable reference views and camera poses with a single forward process. Additionally, we have developed a comprehensive large-scale multi-view image dataset called MvD-1M, comprising up to 1.6 million scenes, equipped with well-aligned metric depth to train MVGenMaster. Moreover, we present several training and model modifications to strengthen the model with scaled-up datasets. Extensive evaluations across in- and out-of-domain benchmarks demonstrate the effectiveness of our proposed method and data formulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_MVGenMaster_Scaling_Multi-View_Generation_from_Any_Image_via_3D_Priors_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Chenjie and Yu, Chaohui and Liu, Shang and Wang, Fan and Xue, Xiangyang and Fu, Yanwei},
    title     = {MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6045-6056}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hupan Lab", "city": "Hangzhou", "country": "China"}]


title: CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11073-11082
cities: ["Singapore", "Singapore", "Huzhou", "Austin", "Hangzhou", "Singapore", "Hangzhou"]
countries: ["Singapore", "Singapore", "China", "USA", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_CADCrafter_Generating_Computer-Aided_Design_Models_from_Unconstrained_Images_CVPR_2025_paper.html

authors:
Cheng Chen, Jiacheng Wei, Tianrun Chen, Chi Zhang, Xiaofeng Yang, Shangzhan Zhang, Bingchen Yang, Chuan-Sheng Foo, Guosheng Lin, Qixing Huang, Fayao Liu

abstract:
Creating CAD digital twins from the physical world is crucial for manufacturing, design, and simulation. However, current methods typically rely on costly 3D scanning with labor-intensive post-processing. To provide a user-friendly design process, we explore the problem of reverse engineering from unconstrained real-world CAD images that can be easily captured by users of all experiences. However, the scarcity of real-world CAD data poses challenges in directly training such models. To tackle these challenges, we propose CADCrafter, an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data while testing on real-world images. To bridge the significant representation disparity between images and parametric CAD models, we introduce a geometry encoder to accurately capture diverse geometric features. Moreover, the texture-invariant properties of the geometric features can also facilitate the generalization to real-world scenarios. Since compiling CAD parameter sequences into explicit CAD models is a non-differentiable process, the network training inherently lacks explicit geometric supervision. To impose geometric validity constraints, we employ direct preference optimization (DPO) to fine-tune our model with the automatic code checker feedback on CAD sequence quality. Furthermore, we collected a real-world dataset, comprised of multi-view images and corresponding CAD command sequence pairs, to evaluate our method. Experimental results demonstrate that our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_CADCrafter_Generating_Computer-Aided_Design_Models_from_Unconstrained_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_CADCrafter_Generating_Computer-Aided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Cheng and Wei, Jiacheng and Chen, Tianrun and Zhang, Chi and Yang, Xiaofeng and Zhang, Shangzhan and Yang, Bingchen and Foo, Chuan-Sheng and Lin, Guosheng and Huang, Qixing and Liu, Fayao},
    title     = {CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11073-11082}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute for Infocomm Research, A*ST AR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "KOKONI3D, Moxin (Huzhou) Technology Co., L TD.", "city": "Huzhou", "country": "China"}, {"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Centre for Frontier AI Research, A*ST AR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Nanyang Technological University", "Institute for Infocomm Research, A*ST AR", "KOKONI3D, Moxin (Huzhou) Technology Co., L TD.", "The University of Texas at Austin", "Westlake University", "Centre for Frontier AI Research, A*ST AR", "Zhejiang University"]


title: Bayesian Test-Time Adaptation for Vision-Language Models
authors: Lihua Zhou, Mao Ye, Shuaifeng Li, Nianxin Li, Xiatian Zhu, Lei Deng, Hongbin Liu, Zhen Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29999-30009
cities: ["Beijing", "Chengdu", "Guildford", "Shenzhen", "Beijing", "Beijing", "Macau"]
countries: ["China", "China", "UK", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Bayesian_Test-Time_Adaptation_for_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data.  Existing methods calculate the similarity between visual embedding and learnable class embeddings, which are initialized by text embeddings, for zero-shot image classification. In this work, we first analyze this process based on Bayes theorem, and observe that the core factors influencing the final prediction are the likelihood and the prior. However, existing methods essentially focus on adapting class embeddings to adapt likelihood, but they often ignore the importance of prior. To address this gap, we propose a novel approach, Bayesian Class Adaptation (BCA), which in addition to continuously updating class embeddings to adapt likelihood, also uses the posterior of incoming samples to continuously update the prior for each class embedding. This dual updating mechanism allows the model to better adapt to distribution shifts and achieve higher prediction accuracy. Our method not only surpasses existing approaches in terms of performance metrics but also maintains superior inference rates and memory usage, making it highly efficient and practical for real-world applications.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Bayesian_Test-Time_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Lihua and Ye, Mao and Li, Shuaifeng and Li, Nianxin and Zhu, Xiatian and Deng, Lei and Liu, Hongbin and Lei, Zhen},
    title     = {Bayesian Test-Time Adaptation for Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29999-30009}
}

raw_affiliation:
[{"affiliation": "CAIR, HKSIS, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "UESTC", "city": "Chengdu", "country": "China"}, {"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "MAIS, Institute of Automation, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "SAI, UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "M.U.S.T", "city": "Macau", "country": "China"}]

affiliations:
["CAIR, HKSIS, CAS", "UESTC", "University of Surrey", "Shenzhen University", "MAIS, Institute of Automation, CAS", "SAI, UCAS", "M.U.S.T"]


title: Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation
authors: Hadi Alzayer, Philipp Henzler, Jonathan T. Barron, Jia-Bin Huang, Pratul P. Srinivasan, Dor Verbin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10933-10942
affiliations: ["Google", "University of Maryland, College Park"]
cities: ["Mountain View", "College Park"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Alzayer_Generative_Multiview_Relighting_for_3D_Reconstruction_under_Extreme_Illumination_Variation_CVPR_2025_paper.html

abstract:
Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. Some prior approaches model appearance variation across images using a per-image embedding vector, while others use physically-based rendering to recover the materials and per-image illumination. Such approaches fail at faithfully recovering view-dependent appearance given significant variation in input illumination and tend to produce mostly diffuse results. We present an approach that reconstructs objects from images taken under different illuminations by first relighting the images under a single reference illumination with a multiview relighting diffusion model and then reconstructing the object's geometry and appearance with a radiance field architecture that is robust to the small remaining inconsistencies among the relit images. We validate our proposed approach on both simulated and real datasets and demonstrate that it greatly outperforms existing techniques at reconstructing high-fidelity appearance from images taken under extreme illumination variation. Moreover, our approach is particularly effective at recovering view-dependent "shiny" appearance which cannot be reconstructed by prior methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Alzayer_Generative_Multiview_Relighting_for_3D_Reconstruction_under_Extreme_Illumination_Variation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Alzayer_Generative_Multiview_Relighting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Alzayer_2025_CVPR,
    author    = {Alzayer, Hadi and Henzler, Philipp and Barron, Jonathan T. and Huang, Jia-Bin and Srinivasan, Pratul P. and Verbin, Dor},
    title     = {Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10933-10942}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}]


title: Causal Composition Diffusion Model for Closed-loop Traffic Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27542-27552
affiliations: ["CMU", "Cruise LLC", "UIUC"]
cities: ["Pittsburgh", "San Francisco", "Urbana"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Causal_Composition_Diffusion_Model_for_Closed-loop_Traffic_Generation_CVPR_2025_paper.html

authors:
Haohong Lin, Xin Huang, Tung Phan, David Hayden, Huan Zhang, Ding Zhao, Siddhartha Srinivasa, Eric Wolff, Hongge Chen

abstract:
Simulation is critical for safety evaluation in autonomous driving, particularly in capturing complex interactive behaviors. However, generating **realistic** and **controllable** traffic scenarios in long-tail situations remains a significant challenge. Existing generative models suffer from the conflicting objective between user-defined controllability and realism constraints, which is amplified in safety-critical contexts. In this work, we introduce the **C**ausal **C**ompositional **Diff**usion Model (***CCDiff***), a structure-guided diffusion framework to address these challenges. We first formulate the learning of controllable and realistic closed-loop simulation as a constrained optimization problem. Then, CCDiff maximizes controllability while adhering to realism by automatically identifying and injecting causal structures directly into the diffusion process, providing structured guidance to enhance both realism and controllability. Through rigorous evaluations on benchmark datasets and in a closed-loop simulator, CCDiff demonstrates substantial gains over state-of-the-art approaches in generating realistic and user-preferred trajectories. Our results show CCDiff's effectiveness in extracting and leveraging causal structures, showing improved closed-loop performance based on key metrics such as collision rate, off-road rate, FDE, and comfort. For more details, welcome to check our project website: https://sites.google.com/view/ccdiff/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Causal_Composition_Diffusion_Model_for_Closed-loop_Traffic_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Causal_Composition_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Haohong and Huang, Xin and Phan, Tung and Hayden, David and Zhang, Huan and Zhao, Ding and Srinivasa, Siddhartha and Wolff, Eric and Chen, Hongge},
    title     = {Causal Composition Diffusion Model for Closed-loop Traffic Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27542-27552}
}

raw_affiliation:
[{"affiliation": "CMU", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Cruise LLC", "city": "San Francisco", "country": "USA"}, {"affiliation": "UIUC", "city": "Urbana", "country": "USA"}]


title: Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective
authors: Duowang Zhu, Xiaohu Huang, Haiyan Huang, Hao Zhou, Zhenfeng Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24011-24022
affiliations: ["Wuhan University", "The University of Hong Kong", "Bytedance"]
cities: ["Wuhan", "Hong Kong", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Change3D_Revisiting_Change_Detection_and_Captioning_from_A_Video_Modeling_CVPR_2025_paper.html

abstract:
In this paper, we present Change3D, a framework that reconceptualizes the change detection and captioning tasks through video modeling. Recent methods have achieved remarkable success by regarding each pair of bi-temporal images as separate frames. They employ a shared-weight image encoder to extract spatial features and then use a change extractor to capture differences between the two images. However, image feature encoding, being a task-agnostic process, cannot attend to changed regions effectively. Furthermore, different change extractors designed for various change detection and captioning tasks make it difficult to have a unified framework. To tackle these challenges, Change3D regards the bi-temporal images as comprising two frames akin to a tiny video. By integrating learnable perception frames between the bi-temporal images, a video encoder enables the perception frames to interact with the images directly and perceive their differences. Therefore, we can get rid of the intricate change extractors, providing a unified framework for different change detection and captioning tasks. We verify Change3D on multiple tasks, encompassing change detection (including binary change detection, semantic change detection, and building damage assessment) and change captioning, across eight standard benchmarks. Without bells and whistles, this simple yet effective framework can achieve superior performance with an ultra-light video model comprising only ~6%-13% of the parameters and ~8%-34% of the FLOPs compared to state-of-the-art methods. We hope that Change3D could be an alternative to 2D-based models and facilitate future research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Change3D_Revisiting_Change_Detection_and_Captioning_from_A_Video_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Change3D_Revisiting_Change_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Duowang and Huang, Xiaohu and Huang, Haiyan and Zhou, Hao and Shao, Zhenfeng},
    title     = {Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24011-24022}
}

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


title: DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling
authors: Xin Xie, Dong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13220-13230
affiliations: ["University of New South Wales (UNSW Sydney)"]
cities: ["Sydney"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_DyMO_Training-Free_Diffusion_Model_Alignment_with_Dynamic_Multi-Objective_Scheduling_CVPR_2025_paper.html

abstract:
Text-to-image diffusion model alignment is critical for improving the alignment between the generated images and human preferences. While training-based methods are constrained by high computational costs and dataset requirements, training-free alignment methods remain underexplored and are often limited by inaccurate guidance. We propose a plug-and-play training-free alignment method, DyMO, for aligning the generated images and human preferences during inference. Apart from text-aware human preference scores, we introduce a semantic alignment objective for enhancing the semantic alignment in the early stages of diffusion, relying on the fact that the attention maps are effective reflections of the semantics in noisy images. We propose dynamic scheduling of multiple objectives and intermediate recurrent steps to reflect the requirements at different steps. Experiments with diverse pre-trained diffusion models and metrics demonstrate the effectiveness and robustness of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_DyMO_Training-Free_Diffusion_Model_Alignment_with_Dynamic_Multi-Objective_Scheduling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_DyMO_Training-Free_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Xin and Gong, Dong},
    title     = {DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Scheduling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13220-13230}
}

raw_affiliation:
[{"affiliation": "University of New South Wales (UNSW Sydney)", "city": "Sydney", "country": "Australia"}]


title: HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation
authors: Yiming Liang, Tianhan Xu, Yuta Kikuchi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 886-895
affiliations: ["Waseda University", "Preferred Networks, Inc."]
cities: ["Tokyo", "Tokyo"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_HiMoR_Monocular_Deformable_Gaussian_Reconstruction_with_Hierarchical_Motion_Representation_CVPR_2025_paper.html

abstract:
We present Hierarchical Motion Representation (HiMoR), a novel deformation representation for 3D Gaussian primitives capable of achieving high-quality monocular dynamic 3D reconstruction. The insight behind HiMoR is that motions in everyday scenes can be decomposed into coarser motions that serve as the foundation for finer details. Using a tree structure, HiMoR's nodes represent different levels of motion detail, with shallower nodes modeling coarse motion for temporal smoothness and deeper nodes capturing finer motion. Additionally, our model uses a few shared motion bases to represent motions of different sets of nodes, aligning with the assumption that motion tends to be smooth and simple. This motion representation design provides Gaussians with a more structured deformation, maximizing the use of temporal relationships to tackle the challenging task of monocular dynamic 3D reconstruction. We also propose using a more reliable perceptual metric as an alternative, given that pixel-level metrics for evaluating monocular dynamic 3D reconstruction can sometimes fail to accurately reflect the true quality of reconstruction. Extensive experiments demonstrate our method's efficacy in achieving superior novel view synthesis from challenging monocular videos with complex motions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_HiMoR_Monocular_Deformable_Gaussian_Reconstruction_with_Hierarchical_Motion_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_HiMoR_Monocular_Deformable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Yiming and Xu, Tianhan and Kikuchi, Yuta},
    title     = {HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {886-895}
}

raw_affiliation:
[{"affiliation": "Waseda University", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Preferred Networks, Inc.", "city": "Tokyo", "country": "Japan"}]


title: GENIUS: A Generative Framework for Universal Multimodal Search
authors: Sungyeon Kim, Xinliang Zhu, Xiaofan Lin, Muhammet Bastan, Douglas Gray, Suha Kwak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19659-19669
affiliations: ["Amazon", "POSTECH"]
cities: ["Seattle", "Pohang"]
countries: ["USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_GENIUS_A_Generative_Framework_for_Universal_Multimodal_Search_CVPR_2025_paper.html

abstract:
Generative retrieval is an emerging approach in information retrieval that generates identifiers (IDs) of target data based on a query, providing an efficient alternative to traditional embedding-based retrieval methods. However, existing models are task-specific and fall short of embedding-based retrieval in performance. This paper proposes GENIUS, a universal generative retrieval framework supporting diverse tasks across multiple modalities and domains. At its core, GENIUS introduces modality-decoupled semantic quantization, transforming multimodal data into discrete IDs encoding both modality and semantics. Moreover, to enhance generalization, we propose a query augmentation that interpolates between a query and its target, allowing GENIUS to adapt to varied query forms. Evaluated on the M-BEIR benchmark, it surpasses prior generative methods by a clear margin. Unlike embedding-based retrieval, GENIUS consistently maintains high retrieval speed across database size, with competitive performance across multiple benchmarks. With additional re-ranking, GENIUS often achieves results close to those of embedding-based methods while preserving efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_GENIUS_A_Generative_Framework_for_Universal_Multimodal_Search_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_GENIUS_A_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sungyeon and Zhu, Xinliang and Lin, Xiaofan and Bastan, Muhammet and Gray, Douglas and Kwak, Suha},
    title     = {GENIUS: A Generative Framework for Universal Multimodal Search},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19659-19669}
}

raw_affiliation:
[{"affiliation": "Amazon", "city": "Seattle", "country": "USA"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]


title: Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition
authors: Junyi Wu, Yan Huang, Min Gao, Yuzhen Niu, Yuzhong Chen, Qiang Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9570-9579
cities: ["Fuzhou", "Sydney", "Fuzhou", "Sydney"]
countries: ["China", "Australia", "China", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Enhanced_Visual-Semantic_Interaction_with_Tailored_Prompts_for_Pedestrian_Attribute_Recognition_CVPR_2025_paper.html

abstract:
Pedestrian attribute recognition (PAR) seeks to predict multiple semantic attributes associated with a specific pedestrian. There are two types of approaches for PAR: unimodal framework and bimodal framework. The former one is to seek a robust visual feature. However, the lack of exploiting semantic feature of linguistic modality is the main concern. The latter one utilizes prompt learning techniques to integrate linguistic data. However, static prompt templates and simple bimodal concatenation cannot to capture the extensive intra-class attribute variability and support active modalities collaboration. In this paper, we propose an Enhanced Visual-Semantic Interaction with Tailored Prompts (EVSITP) framework for PAR. We present an Image-Conditional Dual-Prompt Initialization Module (IDIM) to adaptively generate context-sensitive prompts from visual inputs. Subsequently, a Prompt Enhanced and Regularization Module (PERM) is proposed to strengthen linguistic information from IDIM. We further design a Bimodal Mutual Interaction Module (BMIM) to ensure bidirectional modalities communication.  In addition, existing PAR datasets are collected over a short period in limited scenarios, which do not align with real-world scenarios. Therefore, we annotate a long-term person re-identification dataset to create a new PAR dataset, Celeb-PAR. Experiments on several challenging PAR datasets show that our method outperforms state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Enhanced_Visual-Semantic_Interaction_with_Tailored_Prompts_for_Pedestrian_Attribute_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Enhanced_Visual-Semantic_Interaction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Junyi and Huang, Yan and Gao, Min and Niu, Yuzhen and Chen, Yuzhong and Wu, Qiang},
    title     = {Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9570-9579}
}

raw_affiliation:
[{"affiliation": "College of Computer and Data Science, Fuzhou University, China", "city": "Fuzhou", "country": "China"}, {"affiliation": "Australian Artificial Intelligence Institute (AAII), University of Technology Sydney, Australia", "city": "Sydney", "country": "Australia"}, {"affiliation": "College of Physics and Information Engineering, Fuzhou University, China", "city": "Fuzhou", "country": "China"}, {"affiliation": "School of Electrical and Data Engineering, University of Technology Sydney, Australia", "city": "Sydney", "country": "Australia"}]

affiliations:
["College of Computer and Data Science, Fuzhou University, China", "Australian Artificial Intelligence Institute (AAII), University of Technology Sydney, Australia", "College of Physics and Information Engineering, Fuzhou University, China", "School of Electrical and Data Engineering, University of Technology Sydney, Australia"]


title: SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement
authors: Mark Boss, Zixuan Huang, Aaryaman Vasishta, Varun Jampani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16240-16250
affiliations: ["Stability AI", "UIUC"]
cities: ["London", "Urbana"]
countries: ["UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Boss_SF3D_Stable_Fast_3D_Mesh_Reconstruction_with_UV-unwrapping_and_Illumination_CVPR_2025_paper.html

abstract:
We present SF3D, a novel method for rapid and high-quality textured object mesh reconstruction from a single image in just 0.5 seconds. Unlike most existing approaches, SF3D is explicitly trained for mesh generation, incorporating a fast UV unwrapping technique that enables swift texture generation rather than relying on vertex colors. The method also learns to predict material parameters and normal maps to enhance the visual quality of the reconstructed 3D meshes. Furthermore, SF3D integrates a delighting step to effectively remove low-frequency illumination effects, ensuring that the reconstructed meshes can be easily used in novel illumination conditions. Experiments demonstrate the superior performance of SF3D over the existing techniques

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Boss_SF3D_Stable_Fast_3D_Mesh_Reconstruction_with_UV-unwrapping_and_Illumination_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Boss_SF3D_Stable_Fast_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Boss_2025_CVPR,
    author    = {Boss, Mark and Huang, Zixuan and Vasishta, Aaryaman and Jampani, Varun},
    title     = {SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16240-16250}
}

raw_affiliation:
[{"affiliation": "Stability AI", "city": "London", "country": "UK"}, {"affiliation": "UIUC", "city": "Urbana", "country": "USA"}]


title: HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction
authors: Yuan Wang, Yali Li, Xiang Li, Shengjin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7147-7157
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_HSI-GPT_A_General-Purpose_Large_Scene-Motion-Language_Model_for_Human_Scene_Interaction_CVPR_2025_paper.html

abstract:
While flourishing developments have been witnessed in text-to-motion generation, synthesizing physically realistic, controllable, language-conditioned Human Scene Interactions (HSI) remains a relatively underexplored landscape. Current HSI methods naively rely on conditional Variational AutoEncoder (cVAE) and diffusion models. They are typically associated with limited modalities of control signals and task-specific frameworks design, leading to inflexible adaptation across various interaction scenarios and descriptive-unfaithful motions in diverse 3D physical environments. In this paper, we propose HSI-GPT, a General-Purpose Large Scene-Motion-Language Model that applies "next-token prediction" paradigm of Large Language Models to the HSI domain. HSI-GPT not only exhibits remarkable flexibility to accommodate diverse control signals (3D scenes, textual commands, key-frame poses, as well as scene affordances), but it seamlessly supports various HSI-related tasks (e.g., multi-modal controlled HSI generation, HSI understanding, and general motion completion in 3D scenes). First, HSI-GPT quantizes textual descriptions and human motions into discrete, LLM-interpretable tokens with multi-modal tokenizers. Inspired by multi-modal learning, we develop a recipe for aligning mixed-modality tokens into the shared embedding space of LLMs. These interaction tokens are then organized into unified instruction following prompts, allowing HSI-GPT to fine-tune on question-and-answer tasks. Extensive experiments and visualizations validate that our general-purpose HSI-GPT model delivers exceptional performance across multiple HSI-related tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_HSI-GPT_A_General-Purpose_Large_Scene-Motion-Language_Model_for_Human_Scene_Interaction_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuan and Li, Yali and Li, Xiang and Wang, Shengjin},
    title     = {HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7147-7157}
}

raw_affiliation:
[{"affiliation": "Department of Electronic Engineering, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing National Research Center for Information Science and Technology (BNRist)", "city": "Beijing", "country": "China"}]

affiliations:
["Department of Electronic Engineering, Tsinghua University", "Beijing National Research Center for Information Science and Technology (BNRist)"]


title: Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion
authors: Haoyu Wang, Le Wang, Sanping Zhou, Jingyi Tian, Zheng Qin, Yabing Wang, Gang Hua, Wei Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13350-13360
cities: ["Xi'an", "Bellevue", "Chicago"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Towards_Precise_Embodied_Dialogue_Localization_via_Causality_Guided_Diffusion_CVPR_2025_paper.html

abstract:
Embodied localization based on vision and natural language dialogues presents a persistent challenge in embodied intelligence. Existing methods often approach this task as an image translation problem, leveraging encoder-decoder architectures to predict heatmaps. However, these methods frequently experience a deficiency in accuracy, largely due to their heavy reliance on resolution. To address this issue, we introduce CGD, a novel framework that utilizes causality guided diffusion model to directly model coordinate distributions. Specifically, CGD employs a denoising network to regress coordinates, while integrating causal learning modules, namely back-door adjustment (BDA) and front-door adjustment (FDA) to mitigate confounders during the diffusion process. This approach reduces the dependency on high resolution for improving accuracy, while effectively minimizing spurious correlations, thereby promoting unbiased learning. By guiding the denoising process with causal adjustments, CGD offers flexible control over intensity, ensuring seamless integration with diffusion models. Experimental results demonstrate that CGD outperforms state-of-the-art methods across all metrics. Additionally, we also evaluate CGD in a multi-shot setting, achieving consistently high accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Towards_Precise_Embodied_Dialogue_Localization_via_Causality_Guided_Diffusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Haoyu and Wang, Le and Zhou, Sanping and Tian, Jingyi and Qin, Zheng and Wang, Yabing and Hua, Gang and Tang, Wei},
    title     = {Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13350-13360}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Arti\ufb01cial Intelligence and Robotics, Xi'an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Dolby Laboratories", "city": "Bellevue", "country": "USA"}, {"affiliation": "University of Illinois at Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Arti\ufb01cial Intelligence and Robotics, Xi'an Jiaotong University", "Dolby Laboratories", "University of Illinois at Chicago"]


title: Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior
authors: Chen Guo, Junxuan Li, Yash Kant, Yaser Sheikh, Shunsuke Saito, Chen Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5559-5570
affiliations: ["Codec Avatars Lab, Meta", "ETH Z\u00a8urich", "University of Toronto"]
cities: ["Menlo Park", "Zurich", "Toronto"]
countries: ["USA", "Switzerland", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Vid2Avatar-Pro_Authentic_Avatar_from_Videos_in_the_Wild_via_Universal_CVPR_2025_paper.html

abstract:
We present Vid2Avatar-Pro, a method to create photorealistic and animatable 3D human avatars from monocular in-the-wild videos. Building a high-quality avatar that supports animation with diverse poses from a monocular video is challenging because the observation of pose diversity and view points is inherently limited. The lack of pose variations typically leads to poor generalization to novel poses, and avatars can easily overfit to limited input view points, producing artifacts and distortions from other views. In this work, we address these limitations by leveraging a universal prior model (UPM) learned from a large corpus of multi-view clothed human performance capture data. We build our representation on top of expressive 3D Gaussians with canonical front and back maps shared across identities. Once the UPM is learned to accurately reproduce the large-scale multi-view human images, we fine-tune the model with an in-the-wild video via inverse rendering to obtain a personalized photorealistic human avatar that can be faithfully animated to novel human motions and rendered from novel views. The experiments show that our approach based on the learned universal prior sets a new state-of-the-art in monocular avatar reconstruction by substantially outperforming existing approaches relying only on heuristic regularization or a shape prior of minimally clothed bodies (e.g., SMPL) on publicly available datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Vid2Avatar-Pro_Authentic_Avatar_from_Videos_in_the_Wild_via_Universal_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Chen and Li, Junxuan and Kant, Yash and Sheikh, Yaser and Saito, Shunsuke and Cao, Chen},
    title     = {Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5559-5570}
}

raw_affiliation:
[{"affiliation": "Codec Avatars Lab, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}]


title: RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 574-584
affiliations: ["Peking University", "Pengcheng Laboratory"]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_RoomPainter_View-Integrated_Diffusion_for_Consistent_Indoor_Scene_Texturing_CVPR_2025_paper.html

authors:
Zhipeng Huang, Wangbo Yu, Xinhua Cheng, Chengshu Zhao, Yunyang Ge, Mingyi Guo, Li Yuan, Yonghong Tian

abstract:
Indoor scene texture synthesis has garnered significant interest due to its important potential applications in virtual reality, digital media and creative arts. Existing diffusion-model-based researches either rely on per-view inpainting techniques, which are plagued by severe cross-view inconsistencies and conspicuous seams, or adopt optimization-based approaches that involve substantial computational overhead. In this work, we present **RoomPainter**, a framework that seamlessly integrates efficiency and consistency to achieve high-fidelity texturing of indoor scenes. The core of RoomPainter features a zero-shot technique that effectively adapts a 2D diffusion model for 3D-consistent texture synthesis, along with a two-stage generation strategy that ensures both global and local consistency. Specifically, we introduce Attention-Guided Multi-View Integrated Sampling (**MVIS**) combined with a neighbor-integrated attention mechanism for zero-shot texture map generation. Using the **MVIS**, we firstly generate texture map for the entire room to ensure global consistency, then adopt its variant, namely Attention-Guided Multi-View Integrated Repaint Sampling (**MVRS**) to repaint individual instances within the room, thereby further enhancing local consistency and addressing the occlusion problem. Experiments demonstrate that RoomPainter achieves superior performance for indoor scene texture synthesis in visual quality, global consistency and generation efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_RoomPainter_View-Integrated_Diffusion_for_Consistent_Indoor_Scene_Texturing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_RoomPainter_View-Integrated_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhipeng and Yu, Wangbo and Cheng, Xinhua and Zhao, Chengshu and Ge, Yunyang and Guo, Mingyi and Yuan, Li and Tian, Yonghong},
    title     = {RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {574-584}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]


authors: Jianyang Zhang, Qianli Luo, Guowu Yang, Wenjing Yang, Weide Liu, Guosheng Lin, Fengmao Lv
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30291-30300
cities: ["Chengdu", "Chengdu", "Kash", "Minneapolis", "Cambridge", "Singapore", "Chengdu"]
countries: ["China", "China", "China", "USA", "USA", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Attribute-formed_Class-specific_Concept_Space_Endowing_Language_Bottleneck_Model_with_Better_CVPR_2025_paper.html

title:
Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability

abstract:
Language Bottleneck Models (LBMs) are proposed to achieve interpretable image recognition by classifying images based on textual concept bottlenecks. However, current LBMs simply list all concepts together as the bottleneck layer, leading to the spurious cue inference problem and cannot generalized to unseen classes. To address these limitations, we propose the Attribute-formed Language Bottleneck Model (ALBM). ALBM organizes concepts in the attribute-formed class-specific space, where concepts are descriptions of specific attributes for specific classes. In this way, ALBM can avoid the spurious cue inference problem by classifying solely based on the essential concepts of each class. In addition, the cross-class unified attribute set also ensures that the concept spaces of different classes have strong correlations, as a result, the learned concept classifier can be easily generalized to unseen classes. Moreover, to further improve interpretability, we propose Visual Attribute Prompt Learning (VAPL) to extract visual features on fine-grained attributes. Furthermore, to avoid labor-intensive concept annotation, we propose the Description, Summary, and Supplement (DSS) strategy to automatically generate high-quality concept sets with a complete and precise attribute. Extensive experiments on 9 widely used few-shot benchmarks demonstrate the interpretability, transferability, and performance of our approach. The code and collected concept sets are available at https://github.com/tiggers23/ALBM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Attribute-formed_Class-specific_Concept_Space_Endowing_Language_Bottleneck_Model_with_Better_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Attribute-formed_Class-specific_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jianyang and Luo, Qianli and Yang, Guowu and Yang, Wenjing and Liu, Weide and Lin, Guosheng and Lv, Fengmao},
    title     = {Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30291-30300}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}, {"affiliation": "Institute of Electronics and Information Industry Technology of Kash", "city": "Kash", "country": "China"}, {"affiliation": "University of Minnesota", "city": "Minneapolis", "country": "USA"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education", "city": "Chengdu", "country": "China"}]

affiliations:
["University of Electronic Science and Technology of China", "Southwest Jiaotong University", "Institute of Electronics and Information Industry Technology of Kash", "University of Minnesota", "Harvard University", "Nanyang Technological University", "Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education"]


title: Customized Condition Controllable Generation for Video Soundtrack
authors: Fan Qi, Kunsheng Ma, Changsheng Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23914-23924
cities: ["Tianjin", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Customized_Condition_Controllable_Generation_for_Video_Soundtrack_CVPR_2025_paper.html

abstract:
Recent advances in latent diffusion models (LDMs) have enabled data-driven paradigms for video soundtrack generation, improving multimodal alignment capabilities. However, current two-stage frameworks--which separately optimize audio-visual correspondence and conditional audio synthesis--fundamentally limit joint modeling of dynamic acoustic properties. In this paper, we propose a novel framework for generating video soundtracks that simultaneously produces music and sound effect tailored to the video content. Our method incorporates a Contrastive Visual-Sound-Music pretraining process that maps these modalities into a unified feature space, enhancing the model's ability to capture intricate audio dynamics. We design Spectrum Divergence Masked Attention for Unet to differentiate between the unique characteristics of sound effect and music. We utilize Score-guided Noise Iterative Optimization to provide musicians with customizable control during the generation process. Extensive evaluations on the FilmScoreDB and SymMV&HIMV datasets demonstrate that our approach significantly outperforms state-of-the-art baselines in both subjective and objective assessments, highlighting its potential as a robust tool for video soundtrack generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_Customized_Condition_Controllable_Generation_for_Video_Soundtrack_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_Customized_Condition_Controllable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Fan and Ma, Kunsheng and Xu, Changsheng},
    title     = {Customized Condition Controllable Generation for Video Soundtrack},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23914-23924}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Tianjin University of Technology", "city": "Tianjin", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Tianjin University of Technology", "Institute of Automation, Chinese Academy of Sciences"]


title: ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21248-21257
cities: ["Wuhan", "Wuhan", "Hangzhou", "Shenzhen", "Beijing", "Singapore"]
countries: ["China", "China", "China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_ProjAttacker_A_Configurable_Physical_Adversarial_Attack_for_Face_Recognition_via_CVPR_2025_paper.html

authors:
Yuanwei Liu, Hui Wei, Chengyu Jia, Ruqi Xiao, Weijian Ruan, Xingxing Wei, Joey Tianyi Zhou, Zheng Wang

abstract:
Previous physical adversarial attacks have shown that carefully crafted perturbations can deceive face recognition systems, revealing critical security vulnerabilities. However, these attacks often struggle to impersonate multiple targets and frequently fail to bypass liveness detection. For example, attacks using human-skin masks are challenging to fabricate, inconvenient to swap between users, and often fail liveness detection due to facial occlusions. A projector, however, can generate content-rich light without obstructing the face, making it ideal for non-intrusive attacks. Thus, we propose a novel physical adversarial attack using a projector and explore the superposition of projected and natural light to create adversarial facial images. This approach eliminates the need for physical artifacts on the face, effectively overcoming these limitations. Specifically, our proposed ProjAttacker generates adversarial 3D textures that are projected onto human faces. To ensure physical realizability, we introduce a light reflection function that models complex optical interactions between projected light and human skin, accounting for reflection and diffraction effects. Furthermore, we incorporate camera Image Signal Processing (ISP) simulation to maintain the robustness of adversarial perturbations across real-world diverse imaging conditions. Comprehensive evaluations conducted in both digital and physical scenarios validate the effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_ProjAttacker_A_Configurable_Physical_Adversarial_Attack_for_Face_Recognition_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_ProjAttacker_A_Configurable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuanwei and Wei, Hui and Jia, Chengyu and Xiao, Ruqi and Ruan, Weijian and Wei, Xingxing and Zhou, Joey Tianyi and Wang, Zheng},
    title     = {ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21248-21257}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, Institute of Artifcial Intelligence, School of Computer Science, Wuhan University, Wuhan, China", "city": "Wuhan", "country": "China"}, {"affiliation": "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, China", "city": "Wuhan", "country": "China"}, {"affiliation": "Hangzhou Research Institute of Xidian University, Hangzhou, China", "city": "Hangzhou", "country": "China"}, {"affiliation": "Smart City Research Institute of China Electronics Technology Group Corporation, Shenzhen, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Institute of Arti\ufb01cial Intelligence, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*ST AR), Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["National Engineering Research Center for Multimedia Software, Institute of Artifcial Intelligence, School of Computer Science, Wuhan University, Wuhan, China", "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, China", "Hangzhou Research Institute of Xidian University, Hangzhou, China", "Smart City Research Institute of China Electronics Technology Group Corporation, Shenzhen, China", "Institute of Arti\ufb01cial Intelligence, Beihang University, Beijing, China", "Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*ST AR), Singapore"]


title: EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality
authors: Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14923-14933
affiliations: ["Korea University", "KAIST"]
cities: ["Seoul", "Daejeon"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_EfficientViM_Efficient_Vision_Mamba_with_Hidden_State_Mixer_based_State_CVPR_2025_paper.html

abstract:
For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with its favorable linear computational cost in the number of tokens. To harness the efficacy of SSM, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies with further reduced computational cost. With the observation that the runtime of the SSD layer is driven by the linear projections on the input sequences, we redesign the original SSD layer to perform the channel mixing operation within compressed hidden states in the HSM-SSD layer. Additionally, we propose multi-stage hidden state fusion to reinforce the representation power of hidden states and provide the design to alleviate the bottleneck caused by the memory-bound operations. As a result, the EfficientViM family achieves a new state-of-the-art speed-accuracy trade-off on ImageNet-1k, offering up to a 0.7% performance improvement over the second-best model SHViT with faster speed. Further, we observe significant improvements in throughput and accuracy compared to prior works, when scaling images or employing distillation training. Code is available at https://github.com/mlvlab/EfficientViM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_EfficientViM_Efficient_Vision_Mamba_with_Hidden_State_Mixer_based_State_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_EfficientViM_Efficient_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Sanghyeok and Choi, Joonmyung and Kim, Hyunwoo J.},
    title     = {EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14923-14933}
}

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


title: A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models
authors: Keyu Tu, Mengqi Huang, Zhuowei Chen, Zhendong Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18476-18485
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tu_A4A_Adapter_for_Adapter_Transfer_via_All-for-All_Mapping_for_Cross-Architecture_CVPR_2025_paper.html

abstract:
Large-scale text-to-image models evolve rapidly in size and architecture. The existing adapters struggle to keep pace with these models, requiring extensive retraining. This paper proposes a novel adapter transfer framework, A4A (Adapter for Adapter), which uses an all-for-all mapping approach to seamlessly transfer attention-based adapters across different model architectures (e.g., U-Net to transformer). The framework consists of Coupling Space Projection and Upgraded Space Mapping. During Coupling Space Projection, all attention features of the pretrained adapter are aggregated to fully capture the coupling relationship before being projected into a unified space. The unified space maintains coupling features in a consistent dimension, effectively and efficiently addressing feature scale discrepancies arising from the base model's architecture. In the Upgraded Space Mapping Module, randomly initialized learnable features are introduced to connect the unified and upgraded spaces by integrating reference features via the attention mechanism. The learned features are adaptively injected into the upgrade model through the Alignment module, which bridges the discrepancies between the models using the all-for-all mapping. Experimental results on personalized image generation tasks demonstrate that A4A outperforms previous methods in transferring adapters while being the first to achieve adapter transfer across model architectures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_A4A_Adapter_for_Adapter_Transfer_via_All-for-All_Mapping_for_Cross-Architecture_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_A4A_Adapter_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tu_2025_CVPR,
    author    = {Tu, Keyu and Huang, Mengqi and Chen, Zhuowei and Mao, Zhendong},
    title     = {A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18476-18485}
}


authors: Ali Athar, Xueqing Deng, Liang-Chieh Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19023-19035
raw_affiliation: [{"affiliation": "ByteDance Inc.", "city": "Beijing", "country": "China"}]
affiliations: ["ByteDance Inc."]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Athar_ViCaS_A_Dataset_for_Combining_Holistic_and_Pixel-level_Video_Understanding_CVPR_2025_paper.html

title:
ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation

abstract:
Recent advances in multimodal large language models (MLLMs) have expanded research in video understanding, primarily focusing on high-level tasks such as video captioning and question-answering. Meanwhile, a smaller body of work addresses dense, pixel-precise segmentation tasks, which typically involve category-guided or referral-based object segmentation. Although both research directions are essential for developing models with human-level video comprehension, they have largely evolved separately, with distinct benchmarks and architectures. This paper aims to unify these efforts by introducing ViCaS, a new dataset containing thousands of challenging videos, each annotated with detailed, human-written captions and temporally consistent, pixel-accurate masks for multiple objects with phrase grounding. Our benchmark evaluates models on both holistic/high-level understanding and language-guided, pixel-precise segmentation. We also present carefully validated evaluation measures and propose an effective model architecture that can tackle our benchmark. Project page: https://ali2500.github.io/vicas-project/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Athar_ViCaS_A_Dataset_for_Combining_Holistic_and_Pixel-level_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Athar_ViCaS_A_Dataset_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Athar_2025_CVPR,
    author    = {Athar, Ali and Deng, Xueqing and Chen, Liang-Chieh},
    title     = {ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19023-19035}
}


title: A Universal Scale-Adaptive Deformable Transformer for Image Restoration across Diverse Artifacts
authors: Xuyi He, Yuhui Quan, Ruotao Xu, Hui Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12731-12741
cities: ["Guangzhou", "Guangzhou", "Guangzhou", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_A_Universal_Scale-Adaptive_Deformable_Transformer_for_Image_Restoration_across_Diverse_CVPR_2025_paper.html

abstract:
Structured artifacts are semi-regular, repetitive patterns that closely intertwine with genuine image content, making their removal highly challenging. In this paper, we introduce the Scale-Adaptive Deformable Transformer, an network architecture specifically designed to eliminate such artifacts from images. The proposed  network features two key components: a scale-enhanced deformable convolution module for modeling scale-varying patterns with abundant orientations and potential distortions, and a scale-adaptive deformable attention mechanism for capturing long-range relationships among repetitive patterns with different sizes and non-uniform spatial distributions. Extensive experiments show that our network consistently outperforms state-of-the-art methods in diverse artifact removal tasks, including image deraining, image demoireing, and image debanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_A_Universal_Scale-Adaptive_Deformable_Transformer_for_Image_Restoration_across_Diverse_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_A_Universal_Scale-Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Xuyi and Quan, Yuhui and Xu, Ruotao and Ji, Hui},
    title     = {A Universal Scale-Adaptive Deformable Transformer for Image Restoration across Diverse Artifacts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12731-12741}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Institute for Super Robotics, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Mathematics, National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Computer Science and Engineering, South China University of Technology", "Institute for Super Robotics, South China University of Technology", "Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot", "Department of Mathematics, National University of Singapore"]


title: WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression
authors: Yu Mao, Jun Wang, Nan Guan, Chun Jason Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29342-29351
affiliations: ["MBZUAI", "City University of Hong Kong"]
cities: ["Abu Dhabi", "Hong Kong"]
countries: ["United Arab Emirates", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_WISE_A_Framework_for_Gigapixel_Whole-Slide-Image_Lossless_Compression_CVPR_2025_paper.html

abstract:
Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides, WSIs require considerable data volume, which makes the storage and maintenance of WSI records costly and unsustainable. To this end, this work presents the first investigation of lossless compression of WSI images. Interestingly, we find that most existing compression methods fail to compress the WSI images effectively. Furthermore, our analysis reveals that the failure of existing compressors is mainly due to information irregularity in WSI images. To resolve this issue, we develop a simple yet effective lossless compressor called WISE, specifically designed for WSI images. WISE employs a hierarchical encoding strategy to extract effective bits, reducing the entropy of the image and then adopting a dictionary-based method to handle the irregular frequency patterns. Through extensive experiments, we show that WISE can effectively compress the gigapixel WSI images to 36 times on average and up to 136 times.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_WISE_A_Framework_for_Gigapixel_Whole-Slide-Image_Lossless_Compression_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Yu and Wang, Jun and Guan, Nan and Xue, Chun Jason},
    title     = {WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29342-29351}
}

raw_affiliation:
[{"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "United Arab Emirates"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: Gromov-Wasserstein Problem with Cyclic Symmetry
authors: Shoichiro Takeda, Yasunori Akagi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21011-21020
raw_affiliation: [{"affiliation": "NTT Corporation", "city": "Tokyo", "country": "Japan"}]
affiliations: ["NTT Corporation"]
cities: ["Tokyo"]
countries: ["Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Takeda_Gromov-Wasserstein_Problem_with_Cyclic_Symmetry_CVPR_2025_paper.html

abstract:
We propose novel fast algorithms for the Gromov--Wasserstein problem (GW) with cyclic symmetry of input data. This problem naturally appears as an object-matching task, which underlies various real-world computer vision applications, e.g., image registration, point cloud registration, stereo matching, and 3D reconstruction. Gradient-based algorithms have been widely used to solve GW, and our main idea is to utilize the following remarkable property that emerges in GW with cyclic symmetry: By setting the initial solution to have cyclic symmetry, all intermediate solutions and matrices that appear in the gradient-based algorithms have the same cyclic symmetry until convergence. Based on this property, our gradient-based algorithms restrict the solution space to have cyclic symmetry and update only one symmetric part of solutions and matrices at each iteration, resulting in faster computation. Moreover, our algorithms solve the optimal transport problem at each iteration, which also exhibits cyclic symmetry. This problem can be solved efficiently, and as a result, our algorithms perform significantly faster. Experiments showed the effectiveness of our algorithms in synthetic and real-world data with strict and approximate cyclic symmetry.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Takeda_Gromov-Wasserstein_Problem_with_Cyclic_Symmetry_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Takeda_Gromov-Wasserstein_Problem_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Takeda_2025_CVPR,
    author    = {Takeda, Shoichiro and Akagi, Yasunori},
    title     = {Gromov-Wasserstein Problem with Cyclic Symmetry},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21011-21020}
}


title: IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 465-474
affiliations: ["Meta", "University of Illinois Urbana-Champaign", "University of Maryland College Park"]
cities: ["Menlo Park", "Urbana", "College Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_IRIS_Inverse_Rendering_of_Indoor_Scenes_from_Low_Dynamic_Range_CVPR_2025_paper.html

authors:
Chih-Hao Lin, Jia-Bin Huang, Zhengqin Li, Zhao Dong, Christian Richardt, Tuotuo Li, Michael Zollhöfer, Johannes Kopf, Shenlong Wang, Changil Kim

abstract:
Inverse rendering seeks to recover 3D geometry, surface material, and lighting from captured images, enabling advanced applications such as novel-view synthesis, relighting, and virtual object insertion. However, most existing techniques rely on high dynamic range (HDR) images as input, limiting accessibility for general users. In response, we introduce IRIS, an inverse rendering framework that recovers the physically based material, spatially-varying HDR lighting, and camera response functions from multi-view, low-dynamic-range (LDR) images. By eliminating the dependence on HDR input, we make inverse rendering technology more accessible. We evaluate our approach on real-world and synthetic scenes and compare it with state-of-the-art methods. Our results show that IRIS effectively recovers HDR lighting, accurate material, and plausible camera response functions, supporting photorealistic relighting and object insertion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_IRIS_Inverse_Rendering_of_Indoor_Scenes_from_Low_Dynamic_Range_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_IRIS_Inverse_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Chih-Hao and Huang, Jia-Bin and Li, Zhengqin and Dong, Zhao and Richardt, Christian and Li, Tuotuo and Zollh\"ofer, Michael and Kopf, Johannes and Wang, Shenlong and Kim, Changil},
    title     = {IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {465-474}
}

raw_affiliation:
[{"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "University of Maryland College Park", "city": "College Park", "country": "USA"}]


title: SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26320-26330
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/Li_SimAvatar_Simulation-Ready_Avatars_with_Layered_Hair_and_Clothing_CVPR_2025_paper.html

authors:
Xueting Li, Ye Yuan, Shalini De Mello, Gilles Daviet, Jonathan Leaf, Miles Macklin, Jan Kautz, Umar Iqbal

abstract:
We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing and human body using a unified geometry or produce hair and garments that are not easily adaptable for simulation within existing graphics pipelines. The primary challenge lies in representing the hair and garment geometry in a way that allows leveraging established prior knowledge from foundational image diffusion models (e.g., Stable Diffusion) while being simulation-ready using either physics or neural simulators. To address this task, we propose a two-stage framework that combines the flexibility of 3D Gaussians with simulation-ready hair strands and garment meshes. Specifically, we first leverage two text-conditioned diffusion models to generate garment mesh and hair strands from the given text prompt. To leverage prior knowledge from foundational diffusion models, we attach 3D Gaussians to the body mesh, garment mesh, as well as hair strands and learn the avatar appearance through optimization. To drive the avatar given a pose sequence, we first apply physics simulators onto the garment meshes and hair strands. We then transfer the motion onto 3D Gaussians through carefully designed mechanism for different body parts. As a result, our synthesized avatars have vivid texture and realistic dynamic motion. To the best of our knowledge, our method is the first to produce highly realistic, fully simulation-ready 3D avatars, surpassing the capabilities of current approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SimAvatar_Simulation-Ready_Avatars_with_Layered_Hair_and_Clothing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SimAvatar_Simulation-Ready_Avatars_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xueting and Yuan, Ye and De Mello, Shalini and Daviet, Gilles and Leaf, Jonathan and Macklin, Miles and Kautz, Jan and Iqbal, Umar},
    title     = {SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26320-26330}
}


title: Test-Time Backdoor Detection for Object Detection Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24377-24386
cities: ["Wuhan", "Wuhan", "Wuhan", "Harbin", "Sydney", "Brisbane"]
countries: ["China", "China", "China", "China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Test-Time_Backdoor_Detection_for_Object_Detection_Models_CVPR_2025_paper.html

authors:
Hangtao Zhang, Yichen Wang, Shihui Yan, Chenyu Zhu, Ziqi Zhou, Linshan Hou, Shengshan Hu, Minghui Li, Yanjun Zhang, Leo Yu Zhang

abstract:
Object detection models are vulnerable to backdoor attacks, where attackers poison a small subset of training samples by embedding a predefined trigger to manipulate prediction. Detecting poisoned samples (i.e., those containing triggers) at test time can prevent backdoor activation. However, unlike image classification tasks, the unique characteristics of object detection---particularly its output of numerous objects---pose fresh challenges for backdoor detection. The complex attack effects (e.g., "ghost" object emergence or "vanishing" object) further render current defenses fundamentally inadequate. To this end, we design TRAnsformation Consistency Evaluation (TRACE), a brand-new method for detecting poisoned samples at test time in object detection. Our journey begins with two intriguing observations: (1) poisoned samples exhibit significantly more consistent detection results than clean ones across varied backgrounds. (2) clean samples show higher detection consistency when introduced to different focal information. Based on these phenomena, TRACE applies foreground and background transformations to each test sample, then assesses transformation consistency by calculating the variance in objects confidences. TRACE achieves black-box, universal backdoor detection, with extensive experiments showing a 30% improvement in AUROC over state-of-the-art defenses and resistance to adaptive attacks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Test-Time_Backdoor_Detection_for_Object_Detection_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Test-Time_Backdoor_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Hangtao and Wang, Yichen and Yan, Shihui and Zhu, Chenyu and Zhou, Ziqi and Hou, Linshan and Hu, Shengshan and Li, Minghui and Zhang, Yanjun and Zhang, Leo Yu},
    title     = {Test-Time Backdoor Detection for Object Detection Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24377-24386}
}

raw_affiliation:
[{"affiliation": "School of Cyber Science and Engineering, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Software Engineering, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "University of Technology Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Griffith University", "city": "Brisbane", "country": "Australia"}]

affiliations:
["School of Cyber Science and Engineering, Huazhong University of Science and Technology", "School of Software Engineering, Huazhong University of Science and Technology", "School of Computer Science and Technology, Huazhong University of Science and Technology", "Harbin Institute of Technology", "University of Technology Sydney", "Griffith University"]


title: Towards Precise Scaling Laws for Video Diffusion Transformers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18155-18165
affiliations: ["University of Science and Technology of China", "Peking University", "Kuaishou Technology"]
cities: ["Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Towards_Precise_Scaling_Laws_for_Video_Diffusion_Transformers_CVPR_2025_paper.html

authors:
Yuanyang Yin, Yaqi Zhao, Mingwu Zheng, Ke Lin, Jiarong Ou, Rui Chen, Victor Shea-Jay Huang, Jiahao Wang, Xin Tao, Pengfei Wan, Di Zhang, Baoqun Yin, Wentao Zhang, Kun Gai

abstract:
Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs. This necessitates precisely determining the optimal model size and training hyperparameters before large-scale training.  While scaling laws are employed in language models to predict performance, their existence and accurate derivation in visual generation models remain underexplored.  In this paper, we systematically analyze scaling laws for video diffusion transformers and confirm their presence. Moreover, we discover that, unlike language models, video diffusion models are more sensitive to learning rate and batch size--two hyperparameters often not precisely modeled. To address this, we propose a new scaling law that predicts optimal hyperparameters for any model size and compute budget.  Under these optimal settings, we achieve comparable performance and reduce inference costs by 40.1% compared to conventional scaling methods, within a compute budget of 1e10 TFlops.  Furthermore, we establish a more generalized and precise relationship among validation loss, any model size, and compute budget. This enables performance prediction for non-optimal model sizes, which may also be appealed under practical inference cost constraints, achieving a better trade-off.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Towards_Precise_Scaling_Laws_for_Video_Diffusion_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_Towards_Precise_Scaling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Yuanyang and Zhao, Yaqi and Zheng, Mingwu and Lin, Ke and Ou, Jiarong and Chen, Rui and Huang, Victor Shea-Jay and Wang, Jiahao and Tao, Xin and Wan, Pengfei and Zhang, Di and Yin, Baoqun and Zhang, Wentao and Gai, Kun},
    title     = {Towards Precise Scaling Laws for Video Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18155-18165}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}]


title: RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images
authors: Junjin Xiao, Qing Zhang, Yonewei Nie, Lei Zhu, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5980-5990
cities: ["Guangzhou", "Guangzhou", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_RoGSplat_Learning_Robust_Generalizable_Human_Gaussian_Splatting_from_Sparse_Multi-View_CVPR_2025_paper.html

abstract:
This paper presents RoGSplat, a novel approach for synthesizing high-fidelity novel views of unseen human from sparse multi-view images, while requiring no cumbersome per-subject optimization. Unlike previous methods that typically struggle with sparse views with few overlappings and are less effective in reconstructing complex human geometry, the proposed method enables robust reconstruction in such challenging conditions. Our key idea is to lift SMPL vertices to dense and reliable 3D prior points representing accurate human body geometry, and then regress human Gaussian parameters based on the points. To account for possible misalignment between SMPL model and images, we propose to predict image-aligned 3D prior points by leveraging both pixel-level features and voxel-level features, from which we regress the coarse Gaussians. To enhance the ability to capture high-frequency details, we further render depth maps from the coarse 3D Gaussians to help regress fine-grained pixel-wise Gaussians. Experiments on several benchmark datasets demonstrate that our method outperforms state-of-the-art methods in novel view synthesis and cross-dataset generalization. Our code is available at https://github.com/iSEE-Laboratory/RoGSplat.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_RoGSplat_Learning_Robust_Generalizable_Human_Gaussian_Splatting_from_Sparse_Multi-View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_RoGSplat_Learning_Robust_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Junjin and Zhang, Qing and Nie, Yonewei and Zhu, Lei and Zheng, Wei-Shi},
    title     = {RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5980-5990}
}

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

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


title: SDBF: Steep-Decision-Boundary Fingerprinting for Hard-Label Tampering Detection of DNN Models
authors: Xiaofan Bai, Shixin Li, Xiaojing Ma, Bin Benjamin Zhu, Dongmei Zhang, Linchen Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29278-29287
affiliations: ["Huazhong University of Science and Technology", "Microsoft Corporation"]
cities: ["Wuhan", "Redmond"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_SDBF_Steep-Decision-Boundary_Fingerprinting_for_Hard-Label_Tampering_Detection_of_DNN_Models_CVPR_2025_paper.html

abstract:
Cloud-based AI systems offer significant benefits but also introduce vulnerabilities, making deep neural network (DNN) models susceptible to malicious tampering. This tampering may involve harmful behavior injection or resource reduction, compromising model integrity and performance. To detect model tampering, hard-label fingerprinting techniques generate sensitive samples to probe and reveal tampering. Existing fingerprinting methods are mainly based on gradient-defined sensitivity decision boundary, with the latter showing a manifest superior detection performance. However, all existing fingerprinting methods either suffer from insufficient sensitivity or incur high computational costs. In this paper, we theoretically analyze the black-box co-optimal tampering detection sensitivity of fingerprint samples in the context of decision boundary and gradient-defined sensitivity. Based on this, we further propose Steep-Decision-Boundary Fingerprinting (SDBF), a novel lightweight approach for hard-label tampering detection that inherently and efficiently combines the strengths of existing fingerprinting techniques. SDBF places fingerprint samples near the steep decision boundary, where the outputs of samples are inherently highly sensitive to tampering. We also design a Max Boundary Coverage Strategy (MBCS), which enhances samples'  diversity over the decision boundary. Theoretical analysis and extensive experimental results show that SDBF outperforms existing SOTA hard-label fingerprinting methods in both sensitivity and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_SDBF_Steep-Decision-Boundary_Fingerprinting_for_Hard-Label_Tampering_Detection_of_DNN_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_SDBF_Steep-Decision-Boundary_Fingerprinting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Xiaofan and Li, Shixin and Ma, Xiaojing and Zhu, Bin Benjamin and Zhang, Dongmei and Yu, Linchen},
    title     = {SDBF: Steep-Decision-Boundary Fingerprinting for Hard-Label Tampering Detection of DNN Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29278-29287}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Microsoft Corporation", "city": "Redmond", "country": "USA"}]


title: EnliveningGS: Active Locomotion of 3DGS
authors: Siyuan Shen, Tianjia Shao, Kun Zhou, Chenfanfu Jiang, Yin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 896-905
affiliations: ["State Key Laboratory of CAD&CG, Zhejiang University", "UCLA", "University of Utah"]
cities: ["Hangzhou", "Los Angeles", "Salt Lake City"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_EnliveningGS_Active_Locomotion_of_3DGS_CVPR_2025_paper.html

abstract:
This paper presents a novel pipeline named EnliveningGS, which enables active locomotion of 3D models represented with 3D Gaussian splatting (3DGS). We are inspired by the fact that real-world lives pose their bodies in a natural and physically meaningful manner by compressing or elongating muscle fibers embedded in the body. EnliveningGS aims to replicate the similar functionality of 3DGS models so that the object within a 3DGS scene acts like a living creature rather than a static shape --- they walk, jump, and twist in the scene under provided motion trajectories driven by muscle activations. While the concept is straightforward, many challenging technical difficulties need to be taken care of. Synthesizing realistic locomotion of a 3DGS model embodies an inverse physics problem of very high dimensions. The core challenge is how to efficiently and robustly model frictional contacts between an "enlivened model" and the environment, as it is the composition of contact/collision/friction forces triggered by muscle activation that generates the final movement of the object. We propose a hybrid numerical method mixing LCP and penalty method to tackle this NP-hard problem robustly. Our pipeline also addresses the limitation of existing 3DGS deformation algorithms and inpainting the missing information when models move around.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_EnliveningGS_Active_Locomotion_of_3DGS_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_EnliveningGS_Active_Locomotion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Siyuan and Shao, Tianjia and Zhou, Kun and Jiang, Chenfanfu and Yang, Yin},
    title     = {EnliveningGS: Active Locomotion of 3DGS},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {896-905}
}

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


authors: Wenrui Cai, Qingjie Liu, Yunhong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16871-16881
cities: ["Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_SPMTrack_Spatio-Temporal_Parameter-Efficient_Fine-Tuning_with_Mixture_of_Experts_for_Scalable_CVPR_2025_paper.html

title:
SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking

abstract:
Most state-of-the-art trackers adopt one-stream paradigm, using a single Vision Transformer for joint feature extraction and relation modeling of template and search region images. However, relation modeling between different image patches exhibits significant variations. For instance, background regions dominated by target-irrelevant information require reduced attention allocation, while foreground, particularly boundary areas, need to be be emphasized. A single model may not effectively handle all kinds of relation modeling simultaneously. In this paper, we propose a novel tracker called SPMTrack based on mixture-of-experts tailored for visual tracking task (TMoE), combining the capability of multiple experts to handle diverse relation modeling more flexibly. Benefiting from TMoE, we extend relation modeling from image pairs to spatio-temporal context, further improving tracking accuracy with minimal increase in model parameters. Moreover, we employ TMoE as a parameter-efficient fine-tuning method, substantially reducing trainable parameters, which enables us to train SPMTrack of varying scales efficiently and preserve the generalization ability of pretrained models to achieve superior performance. We conduct experiments on seven datasets, and experimental results demonstrate that our method significantly outperforms current state-of-the-art trackers. The source code is available at https://github.com/WenRuiCai/SPMTrack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_SPMTrack_Spatio-Temporal_Parameter-Efficient_Fine-Tuning_with_Mixture_of_Experts_for_Scalable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_SPMTrack_Spatio-Temporal_Parameter-Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Wenrui and Liu, Qingjie and Wang, Yunhong},
    title     = {SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16871-16881}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhongguancun Laboratory", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Innovation Institute, Beihang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "Zhongguancun Laboratory", "Hangzhou Innovation Institute, Beihang University"]


title: AnyCam: Learning to Recover Camera Poses and Intrinsics from Casual Videos
authors: Felix Wimbauer, Weirong Chen, Dominik Muhle, Christian Rupprecht, Daniel Cremers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16717-16727
affiliations: ["Technical University of Munich", "MCML", "University of Oxford"]
cities: ["Munich", "Munich", "Oxford"]
countries: ["Germany", "Germany", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wimbauer_AnyCam_Learning_to_Recover_Camera_Poses_and_Intrinsics_from_Casual_CVPR_2025_paper.html

abstract:
Estimating camera motion and intrinsics from casual videos is a core challenge in computer vision. Traditional bundle-adjustment based methods, such as SfM and SLAM, struggle to perform reliably on arbitrary data. Although specialized SfM approaches have been developed for handling dynamic scenes, they either require intrinsics or computationally expensive test-time optimization and often fall short in performance. Recently, methods like Dust3r have reformulated the SfM problem in a more data-driven way. While such techniques show promising results, they are still 1) not robust towards dynamic objects and 2) require labeled data for supervised training. As an alternative, we propose AnyCam, a fast transformer model that directly estimates camera poses and intrinsics from a dynamic video sequence in feed-forward fashion. Our intuition is that such a network can learn strong priors over realistic camera poses. To scale up our training, we rely on an uncertainty-based loss formulation and pre-trained depth and flow networks instead of motion or trajectory supervision. This allows us to use diverse, unlabelled video datasets obtained mostly from YouTube. Additionally, we ensure that the predicted trajectory does not accumulate drift over time through a lightweight trajectory refinement step. We test AnyCam on established datasets, where it delivers accurate camera poses and intrinsics both qualitatively and quantitatively. Furthermore, even with trajectory refinement, AnyCam is significantly faster than existing works for SfM in dynamic settings. Finally, by combining camera information, uncertainty, and depth, our model can produce high-quality 4D pointclouds. For more details and code, please check out our project page: https://fwmb.github.io/anycam

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wimbauer_AnyCam_Learning_to_Recover_Camera_Poses_and_Intrinsics_from_Casual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wimbauer_AnyCam_Learning_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wimbauer_2025_CVPR,
    author    = {Wimbauer, Felix and Chen, Weirong and Muhle, Dominik and Rupprecht, Christian and Cremers, Daniel},
    title     = {AnyCam: Learning to Recover Camera Poses and Intrinsics from Casual Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16717-16727}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "MCML", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}]


authors: Wen Yin, Yong Wang, Guiduo Duan, Dongyang Zhang, Xin Hu, Yuan-Fang Li, Tao He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3888-3898
cities: ["Chengdu", "Chengdu", "Melbourne"]
countries: ["China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Knowledge-Aligned_Counterfactual-Enhancement_Diffusion_Perception_for_Unsupervised_Cross-Domain_Visual_Emotion_Recognition_CVPR_2025_paper.html

title:
Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition

abstract:
Visual Emotion Recognition (VER) is a critical yet challenging task aimed at inferring  emotional states of individuals based on visual cues. However, existing works focus on single domains, e.g., realistic images or stickers, limiting VER models' cross-domain generalizability. To fill this  gap, we introduce an Unsupervised Cross-Domain Visual Emotion Recognition (UCDVER) task, which aims to generalize visual emotion recognition from the source domain (e.g., realistic images) to the low-resource target domain (e.g., stickers) in an unsupervised manner. Compared to the conventional unsupervised domain adaptation problems, UCDVER presents two key challenges: a significant emotional expression variability and an affective distribution shift. To mitigate these issues, we propose the  Knowledge-aligned Counterfactual-enhancement Diffusion Perception (KCDP) framework. Specifically, KCDP  leverages a VLM to align emotional representations in a shared knowledge space and guides diffusion models for improved visual affective perception. Furthermore, a Counterfactual-Enhanced Language-image Emotional Alignment (CLIEA) method generates high-quality pseudo-labels for the target domain. Extensive experiments demonstrate that our model surpasses SOTA models in both perceptibility and generalization, e.g., gaining 12% improvements over  SOTA VER model TGCA-PVT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Knowledge-Aligned_Counterfactual-Enhancement_Diffusion_Perception_for_Unsupervised_Cross-Domain_Visual_Emotion_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_Knowledge-Aligned_Counterfactual-Enhancement_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Wen and Wang, Yong and Duan, Guiduo and Zhang, Dongyang and Hu, Xin and Li, Yuan-Fang and He, Tao},
    title     = {Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3888-3898}
}

raw_affiliation:
[{"affiliation": "The Laboratory of Intelligent Collaborative Computing of UESTC", "city": "Chengdu", "country": "China"}, {"affiliation": "Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province", "city": "Chengdu", "country": "China"}, {"affiliation": "Faculty of Information Technology, Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["The Laboratory of Intelligent Collaborative Computing of UESTC", "Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province", "Faculty of Information Technology, Monash University"]


title: Distilling Multi-modal Large Language Models for Autonomous Driving
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27575-27585
affiliations: ["Johns Hopkins University", "Qualcomm AI Research"]
cities: ["Baltimore", "San Diego"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hegde_Distilling_Multi-modal_Large_Language_Models_for_Autonomous_Driving_CVPR_2025_paper.html

authors:
Deepti Hegde, Rajeev Yasarla, Hong Cai, Shizhong Han, Apratim Bhattacharyya, Shweta Mahajan, Litian Liu, Risheek Garrepalli, Vishal M. Patel, Fatih Porikli

abstract:
Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as planners to improve generalizability to rare events. However, using LLMs at test time introduces high computational costs. To address this, we propose DiMA, an end-to-end autonomous driving system that maintains the efficiency of an LLM-free (or vision-based) planner while leveraging the world knowledge of an LLM. DiMA distills the information from a multi-modal LLM to a vision-based end-to-end planner through a set of specially designed surrogate tasks. Under a joint training strategy, a scene encoder common to both networks produces structured representations that are semantically grounded as well as aligned to the final planning objective. Notably, the LLM is optional at inference, enabling robust planning without compromising on efficiency. Training with DiMA results in a 37% reduction in the L2 trajectory error and an 80% reduction in the collision rate of the vision-based planner, as well as a 44% trajectory error reduction in long-tail scenarios. \ours also achieves state-of-the-art performance on the nuScenes planning benchmark.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hegde_Distilling_Multi-modal_Large_Language_Models_for_Autonomous_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hegde_Distilling_Multi-modal_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hegde_2025_CVPR,
    author    = {Hegde, Deepti and Yasarla, Rajeev and Cai, Hong and Han, Shizhong and Bhattacharyya, Apratim and Mahajan, Shweta and Liu, Litian and Garrepalli, Risheek and Patel, Vishal M. and Porikli, Fatih},
    title     = {Distilling Multi-modal Large Language Models for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27575-27585}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Qualcomm AI Research", "city": "San Diego", "country": "USA"}]


title: Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision
authors: Jinnyeong Kim, Seung-Hwan Baek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11482-11492
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Pixel-aligned_RGB-NIR_Stereo_Imaging_and_Dataset_for_Robot_Vision_CVPR_2025_paper.html

abstract:
Integrating RGB and NIR imaging provides complementary spectral information, enhancing robotic vision in challenging lighting conditions. However, existing datasets and imaging systems lack pixel-level alignment between RGB and NIR images, posing challenges for downstream tasks.In this paper, we develop a robotic vision system equipped with two pixel-aligned RGB-NIR stereo cameras and a LiDAR sensor mounted on a mobile robot. The system simultaneously captures RGB stereo images, NIR stereo images, and temporally synchronized LiDAR point cloud. Utilizing the mobility of the robot, we present a dataset containing continuous video frames with pixel-aligned RGB and NIR stereo pairs under diverse lighting conditions.We introduce two methods that utilize our pixel-aligned RGB-NIR images: an RGB-NIR image fusion method and a feature fusion method. The first approach enables existing RGB-pretrained vision models to directly utilize RGB-NIR information without fine-tuning. The second approach fine-tunes existing vision models to more effectively utilize RGB-NIR information.Experimental results demonstrate the effectiveness of using pixel-aligned RGB-NIR images across diverse lighting conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Pixel-aligned_RGB-NIR_Stereo_Imaging_and_Dataset_for_Robot_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Pixel-aligned_RGB-NIR_Stereo_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jinnyeong and Baek, Seung-Hwan},
    title     = {Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11482-11492}
}

raw_affiliation:
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proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14411-14421
affiliations: ["Beijing University of Posts and Telecommunications"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Can_Machines_Understand_Composition_Dataset_and_Benchmark_for_Photographic_Image_CVPR_2025_paper.html

title:
Can Machines Understand Composition? Dataset and Benchmark for Photographic Image Composition Embedding and Understanding

authors:
Zhaoran Zhao, Peng Lu, Anran Zhang, Peipei Li, Xia Li, Xuannan Liu, Yang Hu, Shiyi Chen, Liwei Wang, Wenhao Guo

abstract:
With the rapid growth of social media and digital photography, visually appealing images have become essential for effective communication and emotional engagement. Among the factors influencing aesthetic appeal, composition--the arrangement of visual elements within a frame--plays a crucial role. In recent years, specialized models for photographic composition have achieved impressive results across various aesthetic tasks. Meanwhile, rapidly advancing multimodal large language models (MLLMs) have excelled in several visual perception tasks. However, their ability to embed and understand compositional information remains underexplored, primarily due to the lack of suitable evaluation datasets. To address this gap, we introduce the Photographic Image Composition Dataset (PICD), a large-scale dataset consisting of 36,857 images categorized into 24 composition categories across 355 diverse scenes. We demonstrate the advantages of PICD over existing datasets in terms of data scale, composition category, label quality, and scene diversity. Building on PICD, we establish benchmarks to evaluate the composition embedding capabilities of specialized models and the compositional understanding ability of MLLMs. To enable efficient and effective evaluation, we propose a novel Composition Discrimination Accuracy (CDA) metric. Our evaluation highlights the limitations of current models and provides insights into directions for improving their ability to embed and understand composition.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Can_Machines_Understand_Composition_Dataset_and_Benchmark_for_Photographic_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Can_Machines_Understand_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Zhaoran and Lu, Peng and Zhang, Anran and Li, Peipei and Li, Xia and Liu, Xuannan and Hu, Yang and Chen, Shiyi and Wang, Liwei and Guo, Wenhao},
    title     = {Can Machines Understand Composition? Dataset and Benchmark for Photographic Image Composition Embedding and Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14411-14421}
}

raw_affiliation:
[{"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]


title: LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation
authors: Vladan Stojnić, Yannis Kalantidis, Jiří Matas, Giorgos Tolias
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9794-9803
affiliations: ["VRG, FEE, Czech T echnical University in Prague", "NA VER LABS Europe"]
cities: ["Prague", "Unknown"]
countries: ["Czech Republic", "Europe"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stojnic_LPOSS_Label_Propagation_Over_Patches_and_Pixels_for_Open-vocabulary_Semantic_CVPR_2025_paper.html

abstract:
We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs through label propagation, which jointly optimizes predictions by incorporating patch-to-patch relationships. Since VLMs are primarily optimized for cross-modal alignment and not for intra-modal similarity, we use a Vision Model (VM) that is observed to better capture these relationships. We address resolution limitations inherent to patch-based encoders by applying label propagation at the pixel level as a refinement step, significantly improving segmentation accuracy near class boundaries. Our method, called LPOSS+, performs inference over the entire image, avoiding window-based processing and thereby capturing contextual interactions across the full image. LPOSS+ achieves state-of-the-art performance among training-free methods, across a diverse set of datasets. Code: https://github.com/vladan-stojnic/LPOSS

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stojnic_LPOSS_Label_Propagation_Over_Patches_and_Pixels_for_Open-vocabulary_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stojnic_LPOSS_Label_Propagation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Stojnic_2025_CVPR,
    author    = {Stojni\'c, Vladan and Kalantidis, Yannis and Matas, Ji\v{r}{\'\i} and Tolias, Giorgos},
    title     = {LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9794-9803}
}

raw_affiliation:
[{"affiliation": "VRG, FEE, Czech T echnical University in Prague", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "NA VER LABS Europe", "city": "Unknown", "country": "Europe"}]


title: Towards Efficient Foundation Model for Zero-shot Amodal Segmentation
authors: Zhaochen Liu, Limeng Qiao, Xiangxiang Chu, Lin Ma, Tingting Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20254-20264
cities: ["Beijing", "Beijing", "Unknown", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Towards_Efficient_Foundation_Model_for_Zero-shot_Amodal_Segmentation_CVPR_2025_paper.html

abstract:
Aiming to predict the complete shape of partially occluded objects, amodal segmentation is an important capacity towards visual intelligence. In order to promote the practicability, zero-shot foundation model competent for the open world gains growing attention in this field. Nevertheless, prior models exhibit deficiencies in efficiency and stability. To address this problem, utilizing the implicit prior knowledge, we propose the first SAM-based amodal segmentation foundation model, SAMBA. Methodologically, a novel framework with multilevel facilitation is designed to better adapt the task characteristics and unleash the potential capabilities of SAM. In the modality level, a separation-to-fusion structure is employed that jointly learns modal and amodal segmentation to enhance mutual coordination. In the instance level, to ease the complexity of amodal feature extraction, we introduce a principal focusing mechanism to indicate objects of interest. In the pixel level, mixture-of-experts is incorporated with a specialized distribution loss, by which distinct occlusion rates correspond to different experts to improve the accuracy. Experiments are conducted on several eminent datasets, and the results show that the performance of SAMBA is superior to existing zero-shot and even supervised approaches. Furthermore, our proposed model has notable advantages in terms of speed and size.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Towards_Efficient_Foundation_Model_for_Zero-shot_Amodal_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhaochen and Qiao, Limeng and Chu, Xiangxiang and Ma, Lin and Jiang, Tingting},
    title     = {Towards Efficient Foundation Model for Zero-shot Amodal Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20254-20264}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center of Visual Technology, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "AI Innovation Center, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Meituan Inc.", "city": "Unknown", "country": "China"}, {"affiliation": "National Biomedical Imaging Center, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["National Engineering Research Center of Visual Technology, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "AI Innovation Center, School of Computer Science, Peking University", "Meituan Inc.", "National Biomedical Imaging Center, Peking University"]


title: PhysGen3D: Crafting a Miniature Interactive World from a Single Image
authors: Boyuan Chen, Hanxiao Jiang, Shaowei Liu, Saurabh Gupta, Yunzhu Li, Hao Zhao, Shenlong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6178-6189
affiliations: ["Tsinghua University", "University of Illinois Urbana-Champaign", "Columbia University"]
cities: ["Beijing", "Urbana", "New York"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_PhysGen3D_Crafting_a_Miniature_Interactive_World_from_a_Single_Image_CVPR_2025_paper.html

abstract:
Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce MiniTwin, a novel framework that transforms a single image into an amodal, camera-centric, interactive 3D scene. By combining advanced image-based geometric and semantic understanding with physics-based simulation, MiniTwin creates an interactive 3D world from a static image, enabling us to "imagine" and simulate future scenarios based on user input. At its core, MiniTwin estimates 3D shapes, poses, physical and lighting properties of objects, thereby capturing essential physical attributes that drive realistic object interactions. This framework allows users to specify precise initial conditions, such as object speed or material properties, for enhanced control over generated video outcomes. We evaluate MiniTwin's performance against closed-source state-of-the-art (SOTA) image-to-video models, including Pika, Kling, and Gen-3, showing MiniTwin's capacity to generate videos with realistic physics while offering greater flexibility and fine-grained control. Our results show that MiniTwin achieves a unique balance of photorealism, physical plausibility, and user-driven interactivity, opening new possibilities for generating dynamic, physics-grounded video from an image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_PhysGen3D_Crafting_a_Miniature_Interactive_World_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_PhysGen3D_Crafting_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Boyuan and Jiang, Hanxiao and Liu, Shaowei and Gupta, Saurabh and Li, Yunzhu and Zhao, Hao and Wang, Shenlong},
    title     = {PhysGen3D: Crafting a Miniature Interactive World from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6178-6189}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "Columbia University", "city": "New York", "country": "USA"}]


title: Docopilot: Improving Multimodal Models for Document-Level Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4026-4037
cities: ["Shanghai", "Hong Kong", "Nanjing", "Tianjin", "Shanghai", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_Docopilot_Improving_Multimodal_Models_for_Document-Level_Understanding_CVPR_2025_paper.html

authors:
Yuchen Duan, Zhe Chen, Yusong Hu, Weiyun Wang, Shenglong Ye, Botian Shi, Lewei Lu, Qibin Hou, Tong Lu, Hongsheng Li, Jifeng Dai, Wenhai Wang

abstract:
Despite significant progress in multimodal large language models (MLLMs), their performance on complex, multi-page document comprehension remains inadequate, largely due to the lack of high-quality, document-level datasets. While current retrieval-augmented generation (RAG) methods offer partial solutions, they suffer from issues, such as fragmented retrieval contexts, multi-stage error accumulation, and extra time costs of retrieval. In this work, we present a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents. This dataset includes diverse document structures, extensive cross-page dependencies, and real question-answer pairs derived from the original documents. Building on the dataset, we develop a native multimodal model--Docopilot, which can accurately handle document-level dependencies without relying on RAG. Experiments demonstrate that Docopilot achieves superior coherence, accuracy, and efficiency in document understanding tasks and multi-turn interactions, setting a new baseline for document-level multimodal understanding. Data, code, and models are released at https://github.com/OpenGVLab/Docopilot.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_Docopilot_Improving_Multimodal_Models_for_Document-Level_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_Docopilot_Improving_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, Yuchen and Chen, Zhe and Hu, Yusong and Wang, Weiyun and Ye, Shenglong and Shi, Botian and Lu, Lewei and Hou, Qibin and Lu, Tong and Li, Hongsheng and Dai, Jifeng and Wang, Wenhai},
    title     = {Docopilot: Improving Multimodal Models for Document-Level Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4026-4037}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "The Chinese University of Hong Kong", "Nanjing University", "Nankai University", "Fudan University", "Tsinghua University", "SenseTime Research"]


title: Scaling Properties of Diffusion Models For Perceptual Tasks
authors: Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran, Jitendra Malik
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12945-12954
raw_affiliation: [{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]
affiliations: ["UC Berkeley"]
cities: ["Berkeley"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ravishankar_Scaling_Properties_of_Diffusion_Models_For_Perceptual_Tasks_CVPR_2025_paper.html

abstract:
In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a careful analysis of these scaling properties, we formulate compute-optimal training and inference recipes to scale diffusion models for visual perception tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ravishankar_Scaling_Properties_of_Diffusion_Models_For_Perceptual_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ravishankar_Scaling_Properties_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ravishankar_2025_CVPR,
    author    = {Ravishankar, Rahul and Patel, Zeeshan and Rajasegaran, Jathushan and Malik, Jitendra},
    title     = {Scaling Properties of Diffusion Models For Perceptual Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12945-12954}
}


title: HD-EPIC: A Highly-Detailed Egocentric Video Dataset
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23901-23913
affiliations: ["Uni. of Bristol", "Leiden Uni.", "Singapore Management Uni.", "Uni. of Bath"]
cities: ["Bristol", "Leiden", "Singapore", "Bath"]
countries: ["UK", "Netherlands", "Singapore", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Perrett_HD-EPIC_A_Highly-Detailed_Egocentric_Video_Dataset_CVPR_2025_paper.html

authors:
Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

abstract:
We present a validation dataset of newly-collected kitchen based egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HD-EPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing capability to recognise recipes, ingredients, nutrition, fine-grained actions, 3D perception, object motion, and gaze direction. The powerful long-context Gemini Pro only achieves 37.0% on this benchmark, showcasing its difficulty and highlighting shortcomings in current VLMs. We additionally assess action recognition, sound recognition, and long-term video-object segmentation on HD-EPIC. HD-EPIC is 41 hours of video in 9 kitchens with digital twins of 404 kitchen fixtures, capturing 69 recipes, 59K fine-grained actions, 51K audio events, 20K object movements and 37K object masks lifted to 3D. On average, we have 263 annotations per min of our unscripted videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Perrett_HD-EPIC_A_Highly-Detailed_Egocentric_Video_Dataset_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Perrett_HD-EPIC_A_Highly-Detailed_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Perrett_2025_CVPR,
    author    = {Perrett, Toby and Darkhalil, Ahmad and Sinha, Saptarshi and Emara, Omar and Pollard, Sam and Parida, Kranti Kumar and Liu, Kaiting and Gatti, Prajwal and Bansal, Siddhant and Flanagan, Kevin and Chalk, Jacob and Zhu, Zhifan and Guerrier, Rhodri and Abdelazim, Fahd and Zhu, Bin and Moltisanti, Davide and Wray, Michael and Doughty, Hazel and Damen, Dima},
    title     = {HD-EPIC: A Highly-Detailed Egocentric Video Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23901-23913}
}

raw_affiliation:
[{"affiliation": "Uni. of Bristol", "city": "Bristol", "country": "UK"}, {"affiliation": "Leiden Uni.", "city": "Leiden", "country": "Netherlands"}, {"affiliation": "Singapore Management Uni.", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Uni. of Bath", "city": "Bath", "country": "UK"}]


authors: Hao Zhu, Yan Zhu, Jiayu Xiao, Tianxiang Xiao, Yike Ma, Yucheng Zhang, Feng Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14036-14045
affiliations: ["Institute of Computing Technology, Chinese Academy of Sciences"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Exact_Exploring_Space-Time_Perceptive_Clues_for_Weakly_Supervised_Satellite_Image_CVPR_2025_paper.html

title:
Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation

abstract:
Automated crop mapping through Satellite Image Time Series (SITS) has emerged as a crucial avenue for agricultural monitoring and management. However, due to the low resolution and unclear parcel boundaries, annotating pixel-level masks is exceptionally complex and time-consuming in SITS. This paper embraces the weakly supervised paradigm (i.e., only image-level categories available) to liberate the crop mapping task from the exhaustive annotation burden. The unique characteristics of SITS give rise to several challenges in weakly supervised learning: (1) noise perturbation from spatially neighboring regions, and (2) erroneous semantic bias from anomalous temporal periods. To address the above difficulties, we propose a novel method, termed exploring space-time perceptive clues (Exact). First, we introduce a set of spatial clues to explicitly capture the representative patterns of different crops from the most class-relative regions. Besides, we leverage the temporal-to-class interaction of the model to emphasize the contributions of pivotal clips, thereby enhancing the model perception for crop regions. Build upon the space-time perceptive clues, we derive the clue-based CAMs to effectively supervise the SITS segmentation network. Our method demonstrates impressive performance on various SITS benchmarks. Remarkably, the segmentation network trained on  Exact-generated masks achieves 95% of its fully supervised performance, showing the bright promise of weakly supervised paradigm in crop mapping scenario.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Exact_Exploring_Space-Time_Perceptive_Clues_for_Weakly_Supervised_Satellite_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Exact_Exploring_Space-Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Hao and Zhu, Yan and Xiao, Jiayu and Xiao, Tianxiang and Ma, Yike and Zhang, Yucheng and Dai, Feng},
    title     = {Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14036-14045}
}

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


title: Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29791-29802
cities: ["Hangzhou", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Advancing_Myopia_To_Holism_Fully_Contrastive_Language-Image_Pre-training_CVPR_2025_paper.html

authors:
Haicheng Wang, Chen Ju, Weixiong Lin, Shuai Xiao, Mengting Chen, Yixuan Huang, Chang Liu, Mingshuai Yao, Jinsong Lan, Ying Chen, Qingwen Liu, Yanfeng Wang

abstract:
In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream tasks. However, relying on one-to-one (image, text) contrastive paradigm to learn alignment from large-scale messy web data, CLIP faces a serious myopic dilemma, resulting in biases towards monotonous short texts and shallow visual expressivity. To overcome these issues, this paper advances CLIP into one novel holistic paradigm, by updating both diverse data and alignment optimization. To obtain colorful data with low cost, we use image-to-text captioning to generate multi-texts for each image, from multiple perspectives, granularities, and hierarchies. Two gadgets are proposed to encourage textual diversity. To match such (image, multi-texts) pairs, we modify the CLIP image encoder into multi-branch, and propose multi-to-multi contrastive optimization for image-text part-to-part matching. As a result, diverse visual embeddings are learned for each image, bringing good interpretability and generalization. Extensive experiments and ablations across over ten benchmarks indicate that our holistic CLIP significantly outperforms existing myopic CLIP, including image-text retrieval, open-vocabulary classification, and dense visual tasks. Project page is available to further promote the prosperity of VLMs: https://voide1220.github.io/Holism/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Advancing_Myopia_To_Holism_Fully_Contrastive_Language-Image_Pre-training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Advancing_Myopia_To_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Haicheng and Ju, Chen and Lin, Weixiong and Xiao, Shuai and Chen, Mengting and Huang, Yixuan and Liu, Chang and Yao, Mingshuai and Lan, Jinsong and Chen, Ying and Liu, Qingwen and Wang, Yanfeng},
    title     = {Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29791-29802}
}

raw_affiliation:
[{"affiliation": "Taobao & Tmall Group of Alibaba", "city": "Hangzhou", "country": "China"}, {"affiliation": "Paris Elite Insitute of Technology, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Taobao & Tmall Group of Alibaba", "Paris Elite Insitute of Technology, Shanghai Jiao Tong University", "School of Arti\ufb01cial Intelligence, Shanghai Jiao Tong University"]


title: PolarFree: Polarization-based Reflection-Free Imaging
authors: Mingde Yao, Menglu Wang, King-Man Tam, Lingen Li, Tianfan Xue, Jinwei Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10890-10899
cities: ["Hong Kong", "Shanghai", "Hefei", "Tokyo"]
countries: ["China", "China", "China", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_PolarFree_Polarization-based_Reflection-Free_Imaging_CVPR_2025_paper.html

abstract:
Reflection removal is challenging due to complex light interactions, where reflections obscure important details and hinder scene understanding. Polarization naturally provides a powerful cue to distinguish between reflected and transmitted light, enabling more accurate reflection removal. However, existing methods often rely on small-scale or synthetic datasets, which fail to capture the diversity and complexity of real-world scenarios. To this end, we construct a large-scale dataset, PolaRGB, for Polarization-based reflection removal of RGB images, which enables us to train models that generalize effectively across a wide range of real-world scenarios. The PolaRGB dataset contains 6,500 well-aligned mixed-transmission image pairs, 8xlarger than existing polarization datasets, and is the first to include both RGB and polarization images captured across diverse indoor and outdoor environments with varying lighting conditions. Besides, to fully exploit the potential of polarization cues for reflection removal, we introduce PolarFree, which leverages diffusion process to generate reflection-free cues for accurate reflection removal. Extensive experiments show that PolarFree significantly enhances image clarity in challenging reflective scenarios, setting a new benchmark for polarized imaging and reflection removal. Code and dataset are available at https://github.com/mdyao/PolarFree.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_PolarFree_Polarization-based_Reflection-Free_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_PolarFree_Polarization-based_Reflection-Free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Mingde and Wang, Menglu and Tam, King-Man and Li, Lingen and Xue, Tianfan and Gu, Jinwei},
    title     = {PolarFree: Polarization-based Reflection-Free Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10890-10899}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Science Tokyo", "city": "Tokyo", "country": "Japan"}]

affiliations:
["The Chinese University of Hong Kong", "Shanghai AI Laboratory", "University of Science and Technology of China", "Institute of Science Tokyo"]


title: H-MoRe: Learning Human-centric Motion Representation for Action Analysis
authors: Zhanbo Huang, Xiaoming Liu, Yu Kong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22702-22713
affiliations: ["Department of Computer Science and Engineering, Michigan State University"]
cities: ["East Lansing"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_H-MoRe_Learning_Human-centric_Motion_Representation_for_Action_Analysis_CVPR_2025_paper.html

abstract:
In this paper, we propose H-MoRe, a novel pipeline for learning precise human-centric motion representation. Our approach dynamically preserves relevant human motion while filtering out background movement. Notably, unlike previous methods relying on fully supervised learning from synthetic data, H-MoRe learns directly from real-world scenarios in a self-supervised manner, incorporating both human pose and body shape information. Inspired by kinematics, H-MoRe represents absolute and relative movements of each body point in a matrix format that captures nuanced motion details, termed world-local flows. H-MoRe offers refined insights into human motion, which can be integrated seamlessly into various action-related applications. Experimental results demonstrate that H-MoRe brings substantial improvements across various downstream tasks, including gait recognition(CL@R1: +16.01%), action recognition(Acc@1: +8.92%), and video generation(FVD: -67.07%). Additionally, H-MoRe exhibits high inference efficiency (34 fps), making it suitable for most real-time scenarios. Models and code will be released upon publication.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_H-MoRe_Learning_Human-centric_Motion_Representation_for_Action_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_H-MoRe_Learning_Human-centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhanbo and Liu, Xiaoming and Kong, Yu},
    title     = {H-MoRe: Learning Human-centric Motion Representation for Action Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22702-22713}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, Michigan State University", "city": "East Lansing", "country": "USA"}]


title: Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning
authors: Can Kucuksozen, Yucel Yemez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25388-25398
affiliations: ["Department of Computer Engineering, Koc University", "KUIS AI Center"]
cities: ["Istanbul", "Istanbul"]
countries: ["Turkey", "Turkey"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kucuksozen_Hierarchical_Compact_Clustering_Attention_COCA_for_Unsupervised_Object-Centric_Learning_CVPR_2025_paper.html

abstract:
We propose the Compact Clustering Attention (COCA) layer, an effective building block that introduces a hierarchical strategy for object-centric representation learning, while solving the unsupervised object discovery task on single images. COCA is an attention-based clustering module capable of extracting object-centric representations from multi-object scenes, when cascaded into a bottom-up hierarchical network architecture, referred to as COCA-Net. At its core, COCA utilizes a novel clustering algorithm that leverages the physical concept of compactness, to highlight distinct object centroids in a scene, providing a spatial inductive bias. Thanks to this strategy, COCA-Net generates high-quality segmentation masks on both the decoder side and, notably, the encoder side of its pipeline. Additionally, COCA-Net is not bound by a predetermined number of object masks that it generates and handles the segmentation of background elements better than its competitors. We demonstrate COCA-Net's segmentation performance on six widely adopted datasets, achieving superior or competitive results against the state-of-the-art models across nine different evaluation metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kucuksozen_Hierarchical_Compact_Clustering_Attention_COCA_for_Unsupervised_Object-Centric_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kucuksozen_Hierarchical_Compact_Clustering_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kucuksozen_2025_CVPR,
    author    = {Kucuksozen, Can and Yemez, Yucel},
    title     = {Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25388-25398}
}

raw_affiliation:
[{"affiliation": "Department of Computer Engineering, Koc University", "city": "Istanbul", "country": "Turkey"}, {"affiliation": "KUIS AI Center", "city": "Istanbul", "country": "Turkey"}]


title: Effortless Active Labeling for Long-Term Test-Time Adaptation
authors: Guowei Wang, Changxing Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25633-25642
affiliations: ["South China University of Technology", "South China University of Technology"]
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Effortless_Active_Labeling_for_Long-Term_Test-Time_Adaptation_CVPR_2025_paper.html

abstract:
Long-term test-time adaptation (TTA) is a challenging task due to error accumulation. Recent approaches tackle this issue by actively labeling a small proportion of samples in each batch, yet the annotation burden quickly grows as the batch number increases. In this paper, we investigate how to achieve effortless active labeling so that a maximum of one sample is selected for annotation in each batch. First, we annotate the most valuable sample in each batch based on the single-step optimization perspective in the TTA context. In this scenario, the samples that border between the source- and target-domain data distributions are considered the most feasible for the model to learn in one iteration. Then, we introduce an efficient strategy to identify these samples using feature perturbation. Second, we discover that the gradient magnitudes produced by the annotated and unannotated samples have significant variations. Therefore, we propose balancing their impact on model optimization using two dynamic weights. Extensive experiments on the popular ImageNet-C, -R, -K, -A and PACS databases demonstrate that our approach consistently outperforms state-of-the-art methods with significantly  lower annotation costs. Code is available at: \href https://github.com/flash1803/EATTA  https://github.com/flash1803/EATTA .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Effortless_Active_Labeling_for_Long-Term_Test-Time_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Effortless_Active_Labeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Guowei and Ding, Changxing},
    title     = {Effortless Active Labeling for Long-Term Test-Time Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25633-25642}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]


