# papers

title: VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models
authors: Dahun Kim, AJ Piergiovanni, Ganesh Mallya, Anelia Angelova
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29060-29070
raw_affiliation: [{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]
affiliations: ["Google DeepMind"]
cities: ["Mountain View"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_VideoComp_Advancing_Fine-Grained_Compositional_and_Temporal_Alignment_in_Video-Text_Models_CVPR_2025_paper.html

abstract:
We introduce VideoComp, a benchmark and learning framework for advancing video-text compositionality understanding, aimed at improving vision-language models (VLMs) in fine-grained temporal alignment. Unlike existing benchmarks focused on static image-text compositionality or isolated single-event videos, our benchmark targets alignment in continuous multi-event videos. Leveraging video-text datasets with temporally localized event captions (e.g. ActivityNet-Captions, YouCook2), we construct two compositional benchmarks, ActivityNet-Comp and YouCook2-Comp. We create challenging negative samples with subtle temporal disruptions such as reordering, action word replacement, partial captioning, and combined disruptions. These benchmarks comprehensively test models' compositional sensitivity across extended, cohesive video-text sequences. To improve model performance, we propose a hierarchical pairwise preference loss that strengthens alignment with temporally accurate pairs and gradually penalizes increasingly disrupted ones, encouraging fine-grained compositional learning. To mitigate the limited availability of densely annotated video data, we introduce a pretraining strategy that concatenates short video-caption pairs to simulate multi-event sequences. We evaluate video-text foundational models and large multimodal models (LMMs) on our benchmark, identifying both strengths and areas for improvement in compositionality. Overall, our work provides a comprehensive framework for evaluating and enhancing model capabilities in achieving fine-grained, temporally coherent video-text alignment.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_VideoComp_Advancing_Fine-Grained_Compositional_and_Temporal_Alignment_in_Video-Text_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Dahun and Piergiovanni, AJ and Mallya, Ganesh and Angelova, Anelia},
    title     = {VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment in Video-Text Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29060-29070}
}


title: One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28102-28112
cities: ["Wuxi", "Guildford"]
countries: ["China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_One_Model_for_ALL_Low-Level_Task_Interaction_Is_a_Key_CVPR_2025_paper.html

authors:
Chunyang Cheng, Tianyang Xu, Zhenhua Feng, Xiaojun Wu, Zhangyong Tang, Hui Li, Zeyang Zhang, Sara Atito, Muhammad Awais, Josef Kittler

abstract:
Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we propose to leverage low-level vision tasks from digital photography fusion, allowing for effective feature interaction through pixel-level supervision. This new paradigm provides strong guidance for unsupervised multimodal fusion without relying on abstract semantics, enhancing task-shared feature learning for broader applicability. Owning to the hybrid image features and enhanced universal representations, the proposed GIFNet supports diverse fusion tasks, achieving high performance across both seen and unseen scenarios with a single model. Uniquely, experimental results reveal that our framework also supports single-modality enhancement, offering superior flexibility for practical applications. Our code will be available at https://github.com/AWCXV/GIFNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_One_Model_for_ALL_Low-Level_Task_Interaction_Is_a_Key_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_One_Model_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Chunyang and Xu, Tianyang and Feng, Zhenhua and Wu, Xiaojun and Tang, Zhangyong and Li, Hui and Zhang, Zeyang and Atito, Sara and Awais, Muhammad and Kittler, Josef},
    title     = {One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28102-28112}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence and Computer Science, Jiangnan University", "city": "Wuxi", "country": "China"}, {"affiliation": "Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey", "city": "Guildford", "country": "UK"}]

affiliations:
["School of Artificial Intelligence and Computer Science, Jiangnan University", "Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey"]


title: MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing
authors: Feifei Shao, Ping Liu, Zhao Wang, Yawei Luo, Hongwei Wang, Jun Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6616-6626
cities: ["Hangzhou", "Reno", "Ningbo", "Hangzhou", "Hangzhou"]
countries: ["China", "USA", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_MICAS_Multi-grained_In-Context_Adaptive_Sampling_for_3D_Point_Cloud_Processing_CVPR_2025_paper.html

abstract:
Point cloud processing (PCP) encompasses tasks like reconstruction, denoising, registration, and segmentation, each often requiring specialized models to address unique task characteristics. While in-context learning (ICL) has shown promise across tasks by using a single model with task-specific demonstration prompts, its application to PCP reveals  significant limitations. We identify inter-task and intra-task sensitivity issues in current ICL methods for PCP, which we attribute to inflexible sampling strategies lacking context adaptation at the point and prompt levels. To address these challenges, we propose MICAS, an advanced ICL framework featuring a multi-grained adaptive sampling mechanism tailored for PCP. MICAS introduces two core components: task-adaptive point sampling, which leverages inter-task cues for point-level sampling, and query-specific prompt sampling, which selects optimal prompts per query to mitigate intra-task sensitivity. To our knowledge, this is the first approach to introduce adaptive sampling tailored to the unique requirements of point clouds within an ICL framework. Extensive experiments show that MICAS not only efficiently handles various PCP tasks but also significantly outperforms existing methods. Notably, it achieves a remarkable 4.1% improvement in the part segmentation task and delivers consistent gains across various PCP applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_MICAS_Multi-grained_In-Context_Adaptive_Sampling_for_3D_Point_Cloud_Processing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_MICAS_Multi-grained_In-Context_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Feifei and Liu, Ping and Wang, Zhao and Luo, Yawei and Wang, Hongwei and Xiao, Jun},
    title     = {MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6616-6626}
}

raw_affiliation:
[{"affiliation": "Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, China", "city": "Hangzhou", "country": "China"}, {"affiliation": "Computer Science and Engineering, University of Nevada, Reno, USA", "city": "Reno", "country": "USA"}, {"affiliation": "Ningbo Innovation Center, Zhejiang University, China", "city": "Ningbo", "country": "China"}, {"affiliation": "School of Software Technology, Zhejiang University, China", "city": "Hangzhou", "country": "China"}, {"affiliation": "College of Computer Science and Technology, Zhejiang University, China", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, China", "Computer Science and Engineering, University of Nevada, Reno, USA", "Ningbo Innovation Center, Zhejiang University, China", "School of Software Technology, Zhejiang University, China", "College of Computer Science and Technology, Zhejiang University, China"]


title: Can Text-to-Video Generation help Video-Language Alignment?
authors: Luca Zanella, Massimiliano Mancini, Willi Menapace, Sergey Tulyakov, Yiming Wang, Elisa Ricci
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24097-24107
affiliations: ["University of Trento", "Snap Inc.", "Fondazione Bruno Kessler"]
cities: ["Trento", "Los Angeles", "Trento"]
countries: ["Italy", "USA", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zanella_Can_Text-to-Video_Generation_help_Video-Language_Alignment_CVPR_2025_paper.html

abstract:
Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem with this procedure is that negative captions may introduce linguistic biases, i.e., concepts are seen only as negatives and never associated with a video. While a solution would be to collect videos for the negative captions, existing databases lack the fine-grained variations needed to cover all possible negatives. In this work, we study whether synthetic videos can help to overcome this issue. Our preliminary analysis with multiple generators shows that, while promising on some tasks, synthetic videos harm the performance of the model on others. We hypothesize this issue is linked to noise (semantic and visual) in the generated videos and develop a method, SynViTA, that accounts for those. SynViTA dynamically weights the contribution of each synthetic video based on how similar its target caption is w.r.t. the real counterpart. Moreover, a semantic consistency loss makes the model focus on fine-grained differences across captions, rather than differences in video appearance. Experiments show that, on average, SynViTA improves over existing methods on VideoCon test sets and SSv2-Temporal, SSv2-Events, and ATP-Hard benchmarks, being a first promising step for using synthetic videos when learning video-language models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zanella_Can_Text-to-Video_Generation_help_Video-Language_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zanella_Can_Text-to-Video_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zanella_2025_CVPR,
    author    = {Zanella, Luca and Mancini, Massimiliano and Menapace, Willi and Tulyakov, Sergey and Wang, Yiming and Ricci, Elisa},
    title     = {Can Text-to-Video Generation help Video-Language Alignment?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24097-24107}
}

raw_affiliation:
[{"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}, {"affiliation": "Snap Inc.", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Fondazione Bruno Kessler", "city": "Trento", "country": "Italy"}]


authors: Zebin Xing, Xingyu Zhang, Yang Hu, Bo Jiang, Tong He, Qian Zhang, Xiaoxiao Long, Wei Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1602-1611
cities: ["Beijing", "Beijing", "Nanjing", "Wuhan", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_GoalFlow_Goal-Driven_Flow_Matching_for_Multimodal_Trajectories_Generation_in_End-to-End_CVPR_2025_paper.html

title:
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving

abstract:
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suffer from trajectory selection complexity and reduced trajectory quality due to high trajectory divergence and inconsistencies between guidance and scene information. To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories. To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point. GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information. Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates. Our experimental results, validated on the Navsim, demonstrate that GoalFlow achieves state-of-the-art performance, delivering robust multimodal trajectories for autonomous driving. GoalFlow achieved PDMS of 90.3, significantly surpassing other methods. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance. The code is available at https://github.com/YvanYin/GoalFlow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_GoalFlow_Goal-Driven_Flow_Matching_for_Multimodal_Trajectories_Generation_in_End-to-End_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_GoalFlow_Goal-Driven_Flow_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Zebin and Zhang, Xingyu and Hu, Yang and Jiang, Bo and He, Tong and Zhang, Qian and Long, Xiaoxiao and Yin, Wei},
    title     = {GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1602-1611}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Horizon Robotics", "city": "Beijing", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Huazhong University of Science & Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["University of Chinese Academy of Sciences", "Horizon Robotics", "Nanjing University", "Huazhong University of Science & Technology", "Shanghai AI Laboratory"]


title: GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting
authors: Zixuan Chen, Guangcong Wang, Jiahao Zhu, Jianhuang Lai, Xiaohua Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16325-16335
cities: ["Guangzhou", "Dongguan", "Unknown", "Unknown", "Guangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_GuardSplat_Efficient_and_Robust_Watermarking_for_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) has recently created impressive 3D assets for various applications. However, considering security, capacity, invisibility, and training efficiency, the copyright of 3DGS assets is not well protected as existing watermarking methods are unsuited for its rendering pipeline. In this paper, we propose GuardSplat, an innovative and efficient framework for watermarking 3DGS assets. Specifically, 1) We propose a CLIP-guided pipeline for optimizing the message decoder with minimal costs. The key objective is to achieve high-accuracy extraction by leveraging CLIP's aligning capability and rich representations, demonstrating exceptional capacity and efficiency. 2) We tailor a Spherical-Harmonic-aware (SH-aware) Message Embedding module for 3DGS, seamlessly embedding messages into the SH features of each 3D Gaussian while preserving the original 3D structure. This enables watermarking 3DGS assets with minimal fidelity trade-offs and prevents malicious users from removing the watermarks from the model files, meeting the demands for invisibility and security. 3) We present an Anti-distortion Message Extraction module to improve robustness against various distortions. Experiments demonstrate that GuardSplat outperforms state-of-the-art and achieves fast optimization speed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_GuardSplat_Efficient_and_Robust_Watermarking_for_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_GuardSplat_Efficient_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zixuan and Wang, Guangcong and Zhu, Jiahao and Lai, Jianhuang and Xie, Xiaohua},
    title     = {GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16325-16335}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Y at-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "School of Computing and Information Technology, Great Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "Guangdong Province Key Laboratory of Information Security Technology", "city": "Unknown", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education", "city": "Unknown", "country": "China"}, {"affiliation": "Pazhou Lab (Huangpu)", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Y at-sen University", "School of Computing and Information Technology, Great Bay University", "Guangdong Province Key Laboratory of Information Security Technology", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education", "Pazhou Lab (Huangpu)"]


authors: Lilin Zhang, Chengpei Wu, Ning Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25718-25727
affiliations: ["School of Computer Science, Sichuan University"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Weakly_Supervised_Contrastive_Adversarial_Training_for_Learning_Robust_Features_from_CVPR_2025_paper.html

title:
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data

abstract:
Existing adversarial training (AT) methods often suffer from incomplete perturbation, meaning that not all non-robust features are perturbed when generating adversarial examples (AEs). This results in residual correlations between non-robust features and labels, leading to suboptimal learning of robust features. However, achieving complete perturbation--perturbing as many non-robust features as possible--is challenging due to the difficulty in distinguishing robust and non-robust features and the sparsity of labeled data. To address these challenges, we propose a novel approach called Weakly Supervised Contrastive Adversarial Training (WSCAT). WSCAT ensures complete perturbation for improved learning of robust features by disrupting correlations between non-robust features and labels through complete AE generation over partially labeled data, grounded in information theory. Extensive theoretical analysis and comprehensive experiments on widely adopted benchmarks validate the superiority of WSCAT. Our code is available at https://github.com/zhang-lilin/WSCAT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Weakly_Supervised_Contrastive_Adversarial_Training_for_Learning_Robust_Features_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Weakly_Supervised_Contrastive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Lilin and Wu, Chengpei and Yang, Ning},
    title     = {Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25718-25727}
}

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


title: From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization
authors: Chao Yuan, Guiwei Zhang, Changxiao Ma, Tianyi Zhang, Guanglin Niu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24409-24418
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_From_Poses_to_Identity_Training-Free_Person_Re-Identification_via_Feature_Centralization_CVPR_2025_paper.html

abstract:
Person re-identification (ReID) aims to extract accurate identity representation features. However, during feature extraction, individual samples are inevitably affected by noise (background, occlusions, and model limitations). Considering that features from the same identity follow a normal distribution around identity centers after training, we propose a Training-Free Feature Centralization ReID framework (Pose2ID) by aggregating the same identity features to reduce individual noise and enhance the stability of identity representation, which preserves the feature's original distribution for following strategies such as re-ranking. Specifically, to obtain samples of the same identity, we introduce two components: Identity-Guided Pedestrian Generation: by leveraging identity features to guide the generation process, we obtain high-quality images with diverse poses, ensuring identity consistency even in complex scenarios such as infrared, and occlusion. Neighbor Feature Centralization: it explores each sample's potential positive samples from its neighborhood. Experiments demonstrate that our generative model exhibits strong generalization capabilities and maintains high identity consistency. With the Feature Centralization framework, we achieve impressive performance even with an ImageNet pre-trained model without ReID training, reaching mAP/Rank-1 of 52.81/78.92 on Market1501. Moreover, our method sets new state-of-the-art results across standard, cross-modality, and occluded ReID tasks, showcasing strong adaptability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_From_Poses_to_Identity_Training-Free_Person_Re-Identification_via_Feature_Centralization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_From_Poses_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Chao and Zhang, Guiwei and Ma, Changxiao and Zhang, Tianyi and Niu, Guanglin},
    title     = {From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24409-24418}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Beihang University", "School of Artificial Intelligence, Beihang University"]


title: ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration
authors: Johan Edstedt, André Mateus, Alberto Jaenal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6573-6583
affiliations: ["Link\u00a8oping University", "Ericsson Research, Sweden"]
cities: ["Link\u00f6ping", "Stockholm"]
countries: ["Sweden", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_by_Point_Cloud_Registration_CVPR_2025_paper.html

abstract:
Structure-from-Motion (SfM) is the task of estimating 3D structure and camera poses from images. We define Collaborative SfM (ColabSfM) as sharing distributed SfM reconstructions. Sharing maps requires estimating a joint reference frame, which is typically referred to as registration. However, there is a lack of scalable methods and training datasets for registering SfM reconstructions. In this paper, we tackle this challenge by proposing the scalable task of point cloud registration for SfM reconstructions. We find that current registration methods cannot register SfM point clouds when trained on existing datasets. To this end, we propose a SfM registration dataset generation pipeline, leveraging partial reconstructions from synthetically generated camera trajectories for each scene. Finally, we propose a simple but impactful neural refiner on top of the SotA registration method RoITr that yields significant improvements, which we call RefineRoITr. Our extensive experimental evaluation shows that our proposed pipeline and model enables ColabSfM. Code is available at https://github.com/EricssonResearch/ColabSfM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_by_Point_Cloud_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Edstedt_2025_CVPR,
    author    = {Edstedt, Johan and Mateus, Andr\'e and Jaenal, Alberto},
    title     = {ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6573-6583}
}

raw_affiliation:
[{"affiliation": "Link\u00a8oping University", "city": "Link\u00f6ping", "country": "Sweden"}, {"affiliation": "Ericsson Research, Sweden", "city": "Stockholm", "country": "Sweden"}]


authors: Chirag Parikh, Deepti Rawat, Rakshitha R. T., Tathagata Ghosh, Ravi Kiran Sarvadevabhatla
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19002-19011
affiliations: ["CVIT & iHub-Data, IIIT Hyderabad, India"]
cities: ["Hyderabad"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Parikh_RoadSocial_A_Diverse_VideoQA_Dataset_and_Benchmark_for_Road_Event_CVPR_2025_paper.html

title:
RoadSocial: A Diverse VideoQA Dataset and Benchmark for Road Event Understanding from Social Video Narratives

abstract:
We introduce RoadSocial, a large-scale, diverse VideoQA dataset tailored for generic road event understanding from social media narratives. Unlike existing datasets limited by regional bias, viewpoint bias and expert-driven annotations, RoadSocial captures the global complexity of road events with varied geographies, camera viewpoints (CCTV, handheld, drones) and rich social discourse. Our scalable semi-automatic annotation framework leverages Text LLMs and Video LLMs to generate comprehensive question-answer pairs across 12 challenging QA tasks, pushing the boundaries of road event understanding. RoadSocial is derived from social media videos spanning 14M frames and 414K social comments, resulting in a dataset with 13.2K videos, 674 tags and 260K high-quality QA pairs. We evaluate 18 Video LLMs (open-source and proprietary, driving-specific and general-purpose) on our road event understanding benchmark. We also demonstrate RoadSocial's utility in improving road event understanding capabilities of general-purpose Video LLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Parikh_RoadSocial_A_Diverse_VideoQA_Dataset_and_Benchmark_for_Road_Event_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Parikh_RoadSocial_A_Diverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Parikh_2025_CVPR,
    author    = {Parikh, Chirag and Rawat, Deepti and T., Rakshitha R. and Ghosh, Tathagata and Sarvadevabhatla, Ravi Kiran},
    title     = {RoadSocial: A Diverse VideoQA Dataset and Benchmark for Road Event Understanding from Social Video Narratives},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19002-19011}
}

raw_affiliation:
[{"affiliation": "CVIT & iHub-Data, IIIT Hyderabad, India", "city": "Hyderabad", "country": "India"}]


title: MangaNinja: Line Art Colorization with Precise Reference Following
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5666-5677
affiliations: ["HKU", "HKUST", "Tongyi Lab", "Ant Group", "HKU Shanghai Intelligent Computing Research Center"]
cities: ["Hong Kong", "Hong Kong", "Shanghai", "Hangzhou", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MangaNinja_Line_Art_Colorization_with_Precise_Reference_Following_CVPR_2025_paper.html

authors:
Zhiheng Liu, Ka Leong Cheng, Xi Chen, Jie Xiao, Hao Ouyang, Kai Zhu, Yu Liu, Yujun Shen, Qifeng Chen, Ping Luo

abstract:
Derived from diffusion models, MangaNinja specializes in the task of reference-guided line art colorization. We incorporate two thoughtful designs to ensure precise character detail transcription, including a patch shuffling module to facilitate correspondence learning between the reference color image and the target line art, and a point-driven control scheme to enable fine-grained color matching. Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. We further showcase the potential of the proposed interactive point control in handling challenging cases (*e.g.*, extreme poses and shadows), cross-character colorization, multi-reference harmonization, *etc.*, beyond the reach of existing algorithms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MangaNinja_Line_Art_Colorization_with_Precise_Reference_Following_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MangaNinja_Line_Art_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhiheng and Cheng, Ka Leong and Chen, Xi and Xiao, Jie and Ouyang, Hao and Zhu, Kai and Liu, Yu and Shen, Yujun and Chen, Qifeng and Luo, Ping},
    title     = {MangaNinja: Line Art Colorization with Precise Reference Following},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5666-5677}
}

raw_affiliation:
[{"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tongyi Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]


title: Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
authors: Cecilia Curreli, Dominik Muhle, Abhishek Saroha, Zhenzhang Ye, Riccardo Marin, Daniel Cremers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1871-1882
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning"]
cities: ["Munich", "Munich"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Curreli_Nonisotropic_Gaussian_Diffusion_for_Realistic_3D_Human_Motion_Prediction_CVPR_2025_paper.html

abstract:
Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they often generate motions with undetected limb stretching and jitter. To address this, we introduce SkeletonDiffusion, a latent diffusion model that embeds an explicit inductive bias on the human body within its architecture and training. We present a nonisotropic Gaussian diffusion formulation that aligns with the natural kinematic structure of the human skeleton and models relationships between body parts. Results show that our approach outperforms isotropic alternatives, consistently generating realistic predictions while avoiding artifacts such as limb distortion. Additionally, we identify a limitation in commonly used diversity metrics, which may favor models that produce inconsistent limb lengths within the same sequence. SkeletonDiffusion sets a new benchmark on three real-world datasets, outperforming various baselines across multiple evaluation metrics.  We release the code on our \href https://ceveloper.github.io/publications/skeletondiffusion/  project page .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Curreli_Nonisotropic_Gaussian_Diffusion_for_Realistic_3D_Human_Motion_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Curreli_Nonisotropic_Gaussian_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Curreli_2025_CVPR,
    author    = {Curreli, Cecilia and Muhle, Dominik and Saroha, Abhishek and Ye, Zhenzhang and Marin, Riccardo and Cremers, Daniel},
    title     = {Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1871-1882}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13629-13638
cities: ["Seattle", "Santa Cruz", "Atlanta", "San Diego", "Redmond"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Is_Your_World_Simulator_a_Good_Story_Presenter_A_Consecutive_CVPR_2025_paper.html

title:
Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation

authors:
Yiping Wang, Xuehai He, Kuan Wang, Luyao Ma, Jianwei Yang, Shuohang Wang, Simon Shaolei Du, Yelong Shen

abstract:
The current state-of-the-art video generative models can produce commercial-grade videos with highly realistic details. However, they still struggle to coherently present multiple sequential events in specific short stories, which is foreseeable an essential capability for future long video generation scenarios. For example, top T2V generative models still fail to generate a video of the short simple story "how to put an elephant into a refrigerator." While existing detail-oriented benchmarks primarily focus on fine-grained metrics like aesthetic quality and spatial-temporal consistency, they fall short of evaluating models' abilities to handle event-level story presentation. To address this gap, we introduce StoryEval, a story-oriented benchmark specifically designed to assess text-to-video (T2V) models' story-completion capabilities. StoryEval features 423 prompts spanning 7 classes, each representing short stories composed of 2-4 consecutive events. We employ Vision-Language Models, such as GPT-4o and LLaVA-OV-Chat-72B, to verify the completion of each event in the generated videos, applying a unanimous voting method to enhance reliability. Our methods ensure high alignment with human evaluations, and the evaluation of 11 models reveals its challenge, with none exceeding an average story-completion rate of 50%. StoryEval provides a new benchmark for advancing T2V models and highlights the challenges and opportunities in developing next-generation solutions for coherent story-driven video generation. Project website is available at https://ypwang61.github.io/project/StoryEval.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Is_Your_World_Simulator_a_Good_Story_Presenter_A_Consecutive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Is_Your_World_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yiping and He, Xuehai and Wang, Kuan and Ma, Luyao and Yang, Jianwei and Wang, Shuohang and Du, Simon Shaolei and Shen, Yelong},
    title     = {Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13629-13638}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of California, Santa Cruz", "city": "Santa Cruz", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "University of California, San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]

affiliations:
["University of Washington", "University of California, Santa Cruz", "Georgia Institute of Technology", "University of California, San Diego", "Microsoft"]


title: LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene
authors: Xiaoyu Zhang, Weihong Pan, Chong Bao, Xiyu Zhang, Xiaojun Xiang, Hanqing Jiang, Hujun Bao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16122-16132
affiliations: ["SenseTime Research", "State Key Lab of CAD&CG, Zhejiang University"]
cities: ["Unknown", "Hangzhou"]
countries: ["Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_LookCloser_Frequency-aware_Radiance_Field_for_Tiny-Detail_Scene_CVPR_2025_paper.html

abstract:
Humans perceive and comprehend their surroundings through information spanning multiple frequencies. In immersive scenes, people naturally scan their environment to grasp its overall structure while examining fine details of objects that capture their attention. However, current NeRF frameworks primarily focus on modeling either high-frequency local views or the broad structure of scenes with low-frequency information, limited to balance both. We introduce FA-NeRF, a novel frequency-aware framework for view synthesis that simultaneously captures the overall scene structure and high-definition details within a single NeRF model. To achieve this, we propose a 3D frequency quantification method that analyzes the scene's frequency distribution, enabling frequency-aware rendering. Our framework incorporates a frequency grid for fast convergence and querying, a frequency-aware feature re-weighting strategy to balance features across different frequency contents. Extensive experiments show that our method significantly outperforms existing approaches in modeling entire scenes while preserving fine details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_LookCloser_Frequency-aware_Radiance_Field_for_Tiny-Detail_Scene_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_LookCloser_Frequency-aware_Radiance_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xiaoyu and Pan, Weihong and Bao, Chong and Zhang, Xiyu and Xiang, Xiaojun and Jiang, Hanqing and Bao, Hujun},
    title     = {LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16122-16132}
}

raw_affiliation:
[{"affiliation": "SenseTime Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: PICO: Reconstructing 3D People In Contact with Objects
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1783-1794
cities: ["T\u00fcbingen", "Unknown", "Pittsburgh", "Austin", "Amsterdam"]
countries: ["Germany", "Unknown", "USA", "USA", "the Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cseke_PICO_Reconstructing_3D_People_In_Contact_with_Objects_CVPR_2025_paper.html

authors:
Alpár Cseke, Shashank Tripathi, Sai Kumar Dwivedi, Arjun S. Lakshmipathy, Agniv Chatterjee, Michael J. Black, Dimitrios Tzionas

abstract:
Recovering 3D Human-Object Interaction (HOI) from single color images is challenging due to depth ambiguities, occlusions, and the huge variation in object shape and appearance. Thus, past work requires controlled settings such as known object shapes and contacts, and tackles only limited object classes. Instead, we need methods that generalize to natural images and novel object classes. We tackle this in two main ways:(1) We collect PICO-db, a new dataset of natural images uniquely paired with dense 3D contact correspondences on both body and object meshes. To this end, we use images from the recent DAMON dataset that are paired with annotated contacts, but only on a canonical 3D body. In contrast, we seek contact labels on both the body and the object. To infer these given an image, we retrieve an appropriate 3D object mesh from a database by leveraging vision foundation models. Then, we project DAMON's body contact patches onto the object via a novel method needing only 2 clicks per patch. This minimal human input establishes rich contact correspondences between bodies and objects. (2) We exploit our new dataset in a novel render-and-compare fitting method, called PICO-fit, to recover 3D body and object meshes in interaction. PICO-fit infers contact for the SMPL-X body, retrieves a likely 3D object mesh and contact from PICO-db for that object, and uses the contact to iteratively fit the 3D body and object meshes to image evidence via optimization. Uniquely, PICO-fit works well for many object categories that no existing method can tackle. This is crucial for scaling HOI understanding in the wild. Our data and code are available at https://pico.is.tue.mpg.de.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cseke_PICO_Reconstructing_3D_People_In_Contact_with_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cseke_PICO_Reconstructing_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cseke_2025_CVPR,
    author    = {Cseke, Alp\'ar and Tripathi, Shashank and Dwivedi, Sai Kumar and Lakshmipathy, Arjun S. and Chatterjee, Agniv and Black, Michael J. and Tzionas, Dimitrios},
    title     = {PICO: Reconstructing 3D People In Contact with Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1783-1794}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Meshcapade", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "the Netherlands"}]

affiliations:
["Max Planck Institute for Intelligent Systems", "Meshcapade", "Carnegie Mellon University", "UT Austin", "University of Amsterdam"]


title: Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World
authors: Bangyan Liao, Zhenjun Zhao, Haoang Li, Yi Zhou, Yingping Zeng, Hao Li, Peidong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15823-15832
cities: ["Hangzhou", "Hangzhou", "Hong Kong", "Guangzhou", "Changsha"]
countries: ["China", "China", "China", "China", "China"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Convex_Relaxation_for_Robust_Vanishing_Point_Estimation_in_Manhattan_World_CVPR_2025_paper.html

abstract:
Determining the vanishing points (VPs) in a Manhattan world, as a fundamental task in many 3D vision applications, consists of jointly inferring the line-VP association and locating each VP. Existing methods are, however, either sub-optimal solvers or pursuing global optimality at a significant cost of computing time. In contrast to prior works, we introduce convex relaxation techniques to solve this task for the first time. Specifically, we employ a "soft" association scheme, realized via a truncated multi-selection error, that allows for joint estimation of VPs' locations and line-VP associations. This approach leads to a primal problem that can be reformulated into a quadratically constrained quadratic programming (QCQP) problem, which is then relaxed into a convex semidefinite programming (SDP) problem. To solve this SDP problem efficiently, we present a globally optimal outlier-robust iterative solver (called GlobustVP), which independently searches for one VP and its associated lines in each iteration, treating other lines as outliers. After each independent update of all VPs, the mutual orthogonality between the three VPs in a Manhattan world is reinforced via local refinement. Extensive experiments on both synthetic and real-world data demonstrate that GlobustVP achieves a favorable balance between efficiency, robustness, and global optimality compared to previous works. The code is publicly available at github.com/wu-cvgl/GlobustVP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Convex_Relaxation_for_Robust_Vanishing_Point_Estimation_in_Manhattan_World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_Convex_Relaxation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Bangyan and Zhao, Zhenjun and Li, Haoang and Zhou, Yi and Zeng, Yingping and Li, Hao and Liu, Peidong},
    title     = {Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15823-15832}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}]

affiliations:
["Zhejiang University", "Westlake University", "The Chinese University of Hong Kong", "Hong Kong University of Science and Technology (Guangzhou)", "Hunan University"]


title: Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
authors: Yifei Zhang, Chang Liu, Jin Wei, Xiaomeng Yang, Yu Zhou, Can Ma, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9318-9328
cities: ["Beijing", "Tianjin", "Beijing", "Beijing", "Beijing", "Shenyang"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Linguistics-aware_Masked_Image_Modeling_for_Self-supervised_Scene_Text_Recognition_CVPR_2025_paper.html

abstract:
Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates contextual and semantic elements. In scenarios with degraded visual quality, linguistic patterns serve as crucial supplements for comprehension, highlighting the necessity of integrating both aspects for robust scene text recognition (STR). Contemporary STR approaches often use language models or semantic reasoning modules to capture linguistic features, typically requiring large-scale annotated datasets. Self-supervised learning, which lacks annotations, presents challenges in disentangling linguistic features related to the global context. Typically, sequence contrastive learning emphasizes the alignment of local features, while masked image modeling (MIM) tends to exploit local structures to reconstruct visual patterns, resulting in limited linguistic knowledge. In this paper, we propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch. Specifically, we design a linguistics alignment module to extract vision-independent features as linguistic guidance using inputs with different visual appearances. As features extend beyond mere visual structures, LMIM must consider the global context to achieve reconstruction. Extensive experiments on various benchmarks quantitatively demonstrate our state-of-the-art performance, and attention visualizations qualitatively show the simultaneous capture of both visual and linguistic information. The code is available at https://github.com/zhangyifei01/LMIM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Linguistics-aware_Masked_Image_Modeling_for_Self-supervised_Scene_Text_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yifei and Liu, Chang and Wei, Jin and Yang, Xiaomeng and Zhou, Yu and Ma, Can and Ji, Xiangyang},
    title     = {Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9318-9328}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Automation and BNRist, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Lenovo Research", "city": "Beijing", "country": "China"}, {"affiliation": "College of Engineering, Northeastern University", "city": "Shenyang", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "School of Cyber Security, University of Chinese Academy of Sciences", "Department of Automation and BNRist, Tsinghua University", "Lenovo Research", "College of Engineering, Northeastern University"]


title: FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting
authors: Fangyu Wu, Yuhao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11051-11060
raw_affiliation: [{"affiliation": "University of Waterloo", "city": "Waterloo", "country": "Canada"}]
affiliations: ["University of Waterloo"]
cities: ["Waterloo"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_FruitNinja_3D_Object_Interior_Texture_Generation_with_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
In the real world, objects reveal internal textures when sliced or cut, yet this behavior is not well-studied in 3D generation tasks today. For example, slicing a virtual 3D watermelon should reveal flesh and seeds. Given that no available dataset captures an object's full internal structure and collecting data from all slices is impractical, generative methods become the obvious approach. However, current 3D generation and inpainting methods often focus on visible appearance and overlook internal textures. To bridge this gap, we introduce FruitNinja, the first method to generate internal textures for 3D objects undergoing geometric and topological changes. Our approach produces objects via 3D Gaussian Splatting (3DGS) with both surface and interior textures synthesized, enabling real-time slicing and rendering without additional optimization. FruitNinja leverages a pre-trained diffusion model to progressively inpaint cross-sectional views and applies voxel-grid-based smoothing to achieve cohesive textures throughout the object. Our OpaqueAtom GS strategy overcomes 3DGS limitations by employing densely distributed opaque Gaussians, avoiding biases toward larger particles that destabilize training and  sharp color transitions for fine-grained textures. Experimental results show that FruitNinja substantially outperforms existing approaches, showcasing unmatched visual quality in real-time rendered internal views across arbitrary geometry manipulations. Project page: https://fanguw.github.io/FruitNinja3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_FruitNinja_3D_Object_Interior_Texture_Generation_with_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_FruitNinja_3D_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Fangyu and Chen, Yuhao},
    title     = {FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11051-11060}
}


title: Scaling up Image Segmentation across Data and Tasks
authors: Pei Wang, Zhaowei Cai, Hao Yang, Ashwin Swaminathan, R. Manmatha, Stefano Soatto
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4573-4583
affiliations: ["Amazon AGI", "AWS AI Labs"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Scaling_up_Image_Segmentation_across_Data_and_Tasks_CVPR_2025_paper.html

abstract:
Traditional segmentation models, while effective in isolated tasks, often fail to generalize to more complex and open-ended segmentation problems, such as free-form, open-vocabulary, and in-the-wild scenarios. To bridge this gap, we propose to scale up image segmentation across diverse datasets and tasks such that the knowledge across different tasks and datasets can be integrated while improving the generalization ability. QueryMeldNet, a novel segmentation framework, is introduced and designed to scale seamlessly across both data size and task diversity. It is built upon a dynamic object query mechanism called query meld, which fuses different types of queries using cross-attention. This hybrid approach enables the model to balance between instance- and stuff-level segmentation, providing enhanced scalability for handling diverse object types. We further enhance scalability by leveraging synthetic data-generating segmentation masks and captions for pixel-level and open-vocabulary tasks-drastically reducing the need for costly human annotations. By training on multiple datasets and tasks at scale, QueryMeldNet continuously improves performance as the volume and diversity of data and tasks increase. It exhibits strong generalization capabilities, boosting performance in open-set segmentation tasks SeginW by 7 points. These advancements mark a key step toward universal, scalable segmentation models capable of addressing the demands of real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Scaling_up_Image_Segmentation_across_Data_and_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Scaling_up_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Pei and Cai, Zhaowei and Yang, Hao and Swaminathan, Ashwin and Manmatha, R. and Soatto, Stefano},
    title     = {Scaling up Image Segmentation across Data and Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4573-4583}
}

raw_affiliation:
[{"affiliation": "Amazon AGI", "city": "Seattle", "country": "USA"}, {"affiliation": "AWS AI Labs", "city": "Seattle", "country": "USA"}]


title: Take the Bull by the Horns: Learning to Segment Hard Samples
authors: Yuan Guo, Jingyu Kong, Yu Wang, Yuping Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15642-15652
cities: ["Tianjin", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Take_the_Bull_by_the_Horns_Learning_to_Segment_Hard_CVPR_2025_paper.html

abstract:
Medical image segmentation is vital for clinical applications, with hard samples playing a key role in segmentation accuracy. We propose an effective image segmentation framework that includes mechanisms for identifying and segmenting hard samples. It derives a novel image segmentation paradigm: 1) Learning to identify hard samples: automatically selecting inherent hard samples from different datasets, and 2) Learning to segment hard samples: achieving the segmentation of hard samples through effective feature augmentation on dedicated networks. We name our method `Learning to Segment hard samples' (L2S). The hard sample identification module comprises a backbone model and a classifier, which dynamically uncovers inherent dataset patterns. The hard sample segmentation module utilizes the diffusion process for feature augmentation and incorporates a more sophisticated segmentation network to achieve precise segmentation. We justify our motivation through solid theoretical analysis and extensive experiments. Evaluations across various modalities show that our L2S outperforms other SOTA methods, particularly by substantially improving the segmentation accuracy of hard samples. On ISIC dataset, our L2S improves the Dice score on hard samples and overall segmentation by 8.97% and 1.01%, respectively, compared to SOTA methods. The code is available at \href https://github.com/TqlYuanGie/L2S  https://github.com/TqlYuanGie/L2S.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Take_the_Bull_by_the_Horns_Learning_to_Segment_Hard_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Take_the_Bull_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yuan and Kong, Jingyu and Wang, Yu and Duan, Yuping},
    title     = {Take the Bull by the Horns: Learning to Segment Hard Samples},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15642-15652}
}

raw_affiliation:
[{"affiliation": "Center for Applied Mathematics, Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province", "city": "Hangzhou", "country": "China"}, {"affiliation": "School of Mathematical Sciences, Beijing Normal University", "city": "Beijing", "country": "China"}]

affiliations:
["Center for Applied Mathematics, Tianjin University", "Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province", "School of Mathematical Sciences, Beijing Normal University"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24732-24741
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Augusta", "Beijing"]
countries: ["China", "China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_MIMO_A_Medical_Vision_Language_Model_with_Visual_Referring_Multimodal_CVPR_2025_paper.html

title:
MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal Output

authors:
Yanyuan Chen, Dexuan Xu, Yu Huang, Songkun Zhan, Hanpin Wang, Dongxue Chen, Xueping Wang, Meikang Qiu, Hang Li

abstract:
Currently, medical vision language models are widely used in medical vision question answering tasks. However, existing models are confronted with two issues: for input, the model only relies on text instructions and lacks direct understanding of visual clues in the image; for output, the model only gives text answers and lacks connection with key areas in the image. To address these issues, we propose a unified medical vision language model MIMO, with visual referring Multimodal Input and pixel grounding Multimodal Output. MIMO can not only combine visual clues and textual instructions to understand complex medical images and semantics, but can also ground medical terminologies in textual output within the image. To overcome the scarcity of relevant data in the medical field, we propose MIMOSeg, a comprehensive medical multimodal dataset including 895K samples. MIMOSeg is constructed from four different perspectives, covering basic instruction following and complex question answering with multimodal input and multimodal output. We conduct experiments on several downstream medical multimodal tasks. Extensive experimental results verify that MIMO can uniquely combine visual referring and pixel grounding capabilities, which are not available in previous models. Our project can be found in https://github.com/pkusixspace/MIMO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_MIMO_A_Medical_Vision_Language_Model_with_Visual_Referring_Multimodal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_MIMO_A_Medical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yanyuan and Xu, Dexuan and Huang, Yu and Zhan, Songkun and Wang, Hanpin and Chen, Dongxue and Wang, Xueping and Qiu, Meikang and Li, Hang},
    title     = {MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal Output},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24732-24741}
}

raw_affiliation:
[{"affiliation": "School of Software & Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center for Software Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University Sixth Hospital", "city": "Beijing", "country": "China"}, {"affiliation": "Augusta University", "city": "Augusta", "country": "USA"}, {"affiliation": "Peking University First Hospital", "city": "Beijing", "country": "China"}]

affiliations:
["School of Software & Microelectronics, Peking University", "School of Computer Science, Peking University", "National Engineering Research Center for Software Engineering, Peking University", "Peking University Sixth Hospital", "Augusta University", "Peking University First Hospital"]


title: Bias for Action: Video Implicit Neural Representations with Bias Modulation
authors: Alper Kayabasi, Anil Kumar Vadathya, Guha Balakrishnan, Vishwanath Saragadam
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27999-28008
cities: ["Riverside", "Houston", "Houston"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kayabasi_Bias_for_Action_Video_Implicit_Neural_Representations_with_Bias_Modulation_CVPR_2025_paper.html

abstract:
We propose a new continuous video modeling framework based on implicit neural representations (INRs) called ActINR. At the core of our approach is the observation that INRs can be considered as a learnable dictionary, with the shapes of the basis functions governed by the weights of the INR, and their locations governed by the biases. Given compact non-linear activation functions, we hypothesize that an INR's biases are suitable to capture motion across images, and facilitate compact representations for video sequences. Using these observations, we design ActINR to share INR weights across frames of a video sequence, while using unique biases for each frame. We further model the biases as the output of a separate INR conditioned on time index to promote smoothness. By training the video INR and this bias INR together, we demonstrate unique capabilities, including 10x video slow motion, 4x spatial super resolution along with 2x slow motion, denoising, and video inpainting. ActINR performs remarkably well across numerous video processing tasks (often achieving more than 6dB improvement), setting a new standard for continuous modeling of videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kayabasi_Bias_for_Action_Video_Implicit_Neural_Representations_with_Bias_Modulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kayabasi_Bias_for_Action_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kayabasi_2025_CVPR,
    author    = {Kayabasi, Alper and Vadathya, Anil Kumar and Balakrishnan, Guha and Saragadam, Vishwanath},
    title     = {Bias for Action: Video Implicit Neural Representations with Bias Modulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27999-28008}
}

raw_affiliation:
[{"affiliation": "University of California Riverside", "city": "Riverside", "country": "USA"}, {"affiliation": "Rice University", "city": "Houston", "country": "USA"}, {"affiliation": "Neal Cancer Center, Houston Methodist Hospital", "city": "Houston", "country": "USA"}]

affiliations:
["University of California Riverside", "Rice University", "Neal Cancer Center, Houston Methodist Hospital"]


title: Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
authors: Bozhou Zhang, Nan Song, Xin Jin, Li Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6854-6863
affiliations: ["School of Data Science, Fudan University", "Eastern Institute of Technology"]
cities: ["Shanghai", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Bridging_Past_and_Future_End-to-End_Autonomous_Driving_with_Historical_Prediction_CVPR_2025_paper.html

abstract:
End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention.Current methods aggregate historical information either through dense historical bird's-eye-view (BEV) features or by querying a sparse memory bank, following paradigms inherited from detection.However, we argue that these paradigms either omit historical information in motion planning or fail to align with its multi-step nature, which requires predicting or planning multiple future time steps. In line with the philosophy of "future is a continuation of past", we propose **BridgeAD**, which reformulates motion and planning queries as multi-step queries to differentiate the queries for each future time step. This design enables the effective use of historical prediction and planning by applying them to the appropriate parts of the end-to-end system based on the time steps, which improves both perception and motion planning. Specifically, historical queries for the current frame are combined with perception, while queries for future frames are integrated with motion planning. In this way, we bridge the gap between past and future by aggregating historical insights at every time step, enhancing the overall coherence and accuracy of the end-to-end autonomous driving pipeline. Extensive experiments on the nuScenes dataset in both open-loop and closed-loop settings demonstrate that BridgeAD achieves state-of-the-art performance. We will make our code and models publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Bridging_Past_and_Future_End-to-End_Autonomous_Driving_with_Historical_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Bridging_Past_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Bozhou and Song, Nan and Jin, Xin and Zhang, Li},
    title     = {Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6854-6863}
}

raw_affiliation:
[{"affiliation": "School of Data Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Eastern Institute of Technology", "city": "Beijing", "country": "China"}]


title: Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images
authors: Wensheng Cheng, Zhenghong Li, Jiaxiang Ren, Hyomin Jeong, Congwu Du, Yingtian Pan, Haibin Ling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10466-10475
cities: ["Stony Brook", "Stony Brook"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Blood_Flow_Speed_Estimation_with_Optical_Coherence_Tomography_Angiography_Images_CVPR_2025_paper.html

abstract:
Estimating blood flow speed is essential in many medical and physiological applications, yet it is extremely challenging due to complex vascular structure and flow dynamics, particularly for cerebral cortex regions. Existing techniques, such as Optical Doppler Tomography (ODT), generally require complex hardware control and signal processing, and still suffer from inherent system-level artifacts. To address these challenges, we propose a new learning-based approach named OCTA-Flow, which directly estimates vascular blood flow speed from Optical Coherence Tomography Angiography (OCTA) images that are commonly used for vascular structure analysis. OCTA-Flow employs several novel components to achieve this goal. First, using an encoder-decoder architecture, OCTA-Flow leverages ODT data as pseudo labels during training, thus bypassing the difficulty of collecting ground truth data. Second, to capture the relationship between vessels of varying scales and their flow speed, we design an Adaptive Window Fusion module that employs multiscale window attention. Third, to mitigate ODT artifacts, we incorporate a Conditional Random Field Decoder that promotes smoothness and consistency in the estimated blood flow. Together, these innovations enable OCTA-Flow to effectively produce accurate flow estimation, suppress the artifacts in ODT, and enhance practicality, benefiting from the established techniques of OCTA data acquisition. The code and data are available at https://github.com/Spritea/OCTA-Flow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Blood_Flow_Speed_Estimation_with_Optical_Coherence_Tomography_Angiography_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Blood_Flow_Speed_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Wensheng and Li, Zhenghong and Ren, Jiaxiang and Jeong, Hyomin and Du, Congwu and Pan, Yingtian and Ling, Haibin},
    title     = {Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10466-10475}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Department of Biomedical Engineering, Stony Brook University", "city": "Stony Brook", "country": "USA"}]

affiliations:
["Department of Computer Science, Stony Brook University", "Department of Biomedical Engineering, Stony Brook University"]


title: DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking
authors: Mingzhe Guo, Weiping Tan, Wenyu Ran, Liping Jing, Zhipeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7201-7210
affiliations: ["Beijing Jiaotong University", "Shanghai Jiaotong University"]
cities: ["Beijing", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_DreamTrack_Dreaming_the_Future_for_Multimodal_Visual_Object_Tracking_CVPR_2025_paper.html

abstract:
Aiming to achieve class-agnostic perception in visual object tracking, current trackers commonly formulate tracking as a one-shot detection problem with the template-matching architecture. Despite the success, severe environmental variations in long-term tracking raise challenges to generalizing the tracker in novel situations. Temporal trackers try to fix it by preserving the time-validity of target information with historical predictions, e.g., updating the template. However, solely transmitting the previous observations instead of learning from them leads to an inferior capability of understanding the tracking scenario from past experience, which is critical for the generalization in new frames. To address this issue, we reformulate temporal learning in visual tracking as a History-to-Future process and propose a novel tracking framework DreamTrack. Our DreamTrack learns the temporal dynamics from past observations to dream the future variations of the environment, which boosts the generalization with the extended future information from history. Considering the uncertainty of future variation, multimodal prediction is designed to infer the target trajectory of each possible future situation. The experiments demonstrate that our DreamTrack achieves leading performance with real-time inference speed. In particular, DreamTrack obtains SUC scores of 76.6%/87.9% on LaSOT/TrackingNet, surpassing all recent SOTA trackers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_DreamTrack_Dreaming_the_Future_for_Multimodal_Visual_Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_DreamTrack_Dreaming_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Mingzhe and Tan, Weiping and Ran, Wenyu and Jing, Liping and Zhang, Zhipeng},
    title     = {DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7201-7210}
}

raw_affiliation:
[{"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}]


title: OmniStyle: Filtering High Quality Style Transfer Data at Scale
authors: Ye Wang, Ruiqi Liu, Jiang Lin, Fei Liu, Zili Yi, Yilin Wang, Rui Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7847-7856
cities: ["Changchun", "Nanjing", "Beijing", "San Jose", "Beijing"]
countries: ["China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OmniStyle_Filtering_High_Quality_Style_Transfer_Data_at_Scale_CVPR_2025_paper.html

abstract:
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style categories, each enhanced with textual descriptions and instruction prompts. We show that OmniStyle-1M can not only improve the generalization of style transfer models through supervised training but also facilitates precise control over target stylization. Especially, to ensure the quality of the dataset, we introduce OmniFilter, a comprehensive style transfer quality assessment framework, which filters high-quality triplets based on content preservation, style consistency, and aesthetic appeal. Building upon this foundation, we propose OmniStyle, a framework based on the Diffusion Transformer (DiT) architecture designed for high-quality and efficient style transfer. This framework supports both instruction-guided and image-guided style transfer, generating high resolution outputs with exceptional detail. Extensive qualitative and quantitative evaluations demonstrate OmniStyle's superior performance compared to existing approaches, highlighting its efficiency and versatility. OmniStyle-1M and its accompanying methodologies provide a significant contribution to advancing high-quality style transfer, offering a valuable resource for the research community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OmniStyle_Filtering_High_Quality_Style_Transfer_Data_at_Scale_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OmniStyle_Filtering_High_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ye and Liu, Ruiqi and Lin, Jiang and Liu, Fei and Yi, Zili and Wang, Yilin and Ma, Rui},
    title     = {OmniStyle: Filtering High Quality Style Transfer Data at Scale},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7847-7856}
}

raw_affiliation:
[{"affiliation": "Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}, {"affiliation": "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China", "city": "Beijing", "country": "China"}]

affiliations:
["Jilin University", "Nanjing University", "ByteDance", "Adobe", "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China"]


authors: Diljeet Jagpal, Xi Chen, Vinay P. Namboodiri
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18219-18228
affiliations: ["University of Bath", "Fudan University"]
cities: ["Bath", "Shanghai"]
countries: ["United Kingdom", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jagpal_EIDT-V_Exploiting_Intersections_in_Diffusion_Trajectories_for_Model-Agnostic_Zero-Shot_Training-Free_CVPR_2025_paper.html

title:
EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation

abstract:
Zero-shot, training-free, image-based text-to-video generation is an emerging area that aims to generate videos using existing image-based diffusion models. Current methods in this space require specific architectural changes to image-generation models, which limit their adaptability and scalability.In contrast to such methods, we provide a model-agnostic approach. We use intersections in diffusion trajectories, working only with the latent values. We could not obtain localized frame-wise coherence and diversity using only the intersection of trajectories. Thus, we instead use a grid-based approach. An in-context trained LLM is used to generate coherent frame-wise prompts; another is used to identify differences between frames. Based on these, we obtain a CLIP-based attention mask that controls the timing of switching the prompts for each grid cell. Earlier switching results in higher variance, while later switching results in more coherence. Therefore, Our approach can ensure appropriate control between coherence and variance for the frames.Our approach results in state-of-the-art performance while being more flexible when working with diverse image-generation models. The empirical analysis using quantitative metrics and user studies confirms our model's superior temporal consistency, visual fidelity and user satisfaction, thus providing a novel way to obtain training-free, image-based text-to-video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jagpal_EIDT-V_Exploiting_Intersections_in_Diffusion_Trajectories_for_Model-Agnostic_Zero-Shot_Training-Free_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jagpal_EIDT-V_Exploiting_Intersections_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jagpal_2025_CVPR,
    author    = {Jagpal, Diljeet and Chen, Xi and Namboodiri, Vinay P.},
    title     = {EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18219-18228}
}

raw_affiliation:
[{"affiliation": "University of Bath", "city": "Bath", "country": "United Kingdom"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: Cross-View Completion Models are Zero-shot Correspondence Estimators
authors: Honggyu An, Jin Hyeon Kim, Seonghoon Park, Jaewoo Jung, Jisang Han, Sunghwan Hong, Seungryong Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1103-1115
affiliations: ["KAIST", "Korea University", "Samsung Electronics"]
cities: ["Daejeon", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/An_Cross-View_Completion_Models_are_Zero-shot_Correspondence_Estimators_CVPR_2025_paper.html

abstract:
In this work, we analyze new aspects of cross-view completion, mainly through the analogy of cross-view completion and traditional self-supervised correspondence learning algorithms. Based on our analysis, we reveal that the cross-attention map of Croco-v2, best reflects this correspondence information compared to other correlations from the encoder or decoder features. We further verify the effectiveness of the cross-attention map by evaluating on both zero-shot and supervised dense geometric correspondence and multi-frame depth estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/An_Cross-View_Completion_Models_are_Zero-shot_Correspondence_Estimators_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/An_Cross-View_Completion_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{An_2025_CVPR,
    author    = {An, Honggyu and Kim, Jin Hyeon and Park, Seonghoon and Jung, Jaewoo and Han, Jisang and Hong, Sunghwan and Kim, Seungryong},
    title     = {Cross-View Completion Models are Zero-shot Correspondence Estimators},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1103-1115}
}

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


title: Multi-party Collaborative Attention Control for Image Customization
authors: Han Yang, Chuanguang Yang, Qiuli Wang, Zhulin An, Weilun Feng, Libo Huang, Yongjun Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7942-7951
cities: ["Beijing", "Beijing", "Chongqing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Multi-party_Collaborative_Attention_Control_for_Image_Customization_CVPR_2025_paper.html

abstract:
The rapid development of diffusion models has fueled a growing demand for customized image generation. However, current customization methods face several limitations: 1) typically accept either image or text conditions alone; 2) customization in complex visual scenarios often leads to subject leakage or confusion; 3) image-conditioned outputs tend to suffer from inconsistent backgrounds; and 4) high computational costs. To address these issues, this paper introduces Multi-party Collaborative Attention Control (MCA-Ctrl), a tuning-free approach that enables high-quality image customization under both text and complex visual conditions. Specifically, MCA-Ctrl leverages two key operations within the self-attention layer to coordinate multiple parallel diffusion processes and guide the target image generation. This approach allows MCA-Ctrl to capture the content and appearance of specific subjects while maintaining semantic consistency with the conditional input. Additionally, to mitigate subject leakage and confusion issues common in complex visual scenarios, we introduce a Subject Localization Module that extracts precise subject and editable image layers based on user instructions. Extensive quantitative and human evaluation experiments demonstrate that MCA-Ctrl outperforms previous methods in zero-shot image customization, effectively addressing the aforementioned issues.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Multi-party_Collaborative_Attention_Control_for_Image_Customization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Multi-party_Collaborative_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Han and Yang, Chuanguang and Wang, Qiuli and An, Zhulin and Feng, Weilun and Huang, Libo and Xu, Yongjun},
    title     = {Multi-party Collaborative Attention Control for Image Customization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7942-7951}
}

raw_affiliation:
[{"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Radiology, The First Affiliated Hospital of Army Medical University", "city": "Chongqing", "country": "China"}]

affiliations:
["Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Department of Radiology, The First Affiliated Hospital of Army Medical University"]


title: Reproducible Vision-Language Models Meet Concepts Out of Pre-Training
authors: Ziliang Chen, Xin Huang, Xiaoxuan Fan, Keze Wang, Yuyu Zhou, Quanlong Guan, Liang Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14701-14711
cities: ["Shenzhen", "Guangzhou", "Lausanne", "Guangzhou"]
countries: ["China", "China", "Switzerland", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.html

abstract:
Contrastive Language-Image Pre-training (CLIP) models as a milestone of modern multimodal intelligence, its generalization mechanism grasped massive research interests in the community. While existing studies limited in the scope of pre-training knowledge, hardly underpinned its generalization to countless open-world concepts absent from the pre-training regime. This paper dives into such Out-of-Pre-training (OOP) generalization problem from a holistic perspective. We propose LAION-Beyond benchmark to isolate the evaluation of OOP concepts from pre-training knowledge, with regards to OpenCLIP and its reproducible variants derived from LAION datasets. Empirical analysis evidences that despite image features of OOP concepts born with significant category margins, their zero-shot transfer significantly fails due to the poor image-text alignment. To this, we elaborate the "name-tuning" methodology with its theoretical merits in terms of OOP generalization, then propose few-shot name learning (FSNL) and zero-shot name learning (ZSNL) algorithms to achieve OOP generalization in a data-efficient manner. Their superiority have been further verified in our comprehensive experiments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Reproducible_Vision-Language_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ziliang and Huang, Xin and Fan, Xiaoxuan and Wang, Keze and Zhou, Yuyu and Guan, Quanlong and Lin, Liang},
    title     = {Reproducible Vision-Language Models Meet Concepts Out of Pre-Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14701-14711}
}

raw_affiliation:
[{"affiliation": "Research Institute of Multiple Agents and Embodied Intelligence, Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Jinan University", "city": "Guangzhou", "country": "China"}]

affiliations:
["Research Institute of Multiple Agents and Embodied Intelligence, Peng Cheng Laboratory", "Sun Yat-sen University", "EPFL", "Jinan University"]


title: Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18198-18208
affiliations: ["GenAI, Meta", "FAIR, Meta", "The Hebrew University of Jerusalem"]
cities: ["Menlo Park", "Menlo Park", "Jerusalem"]
countries: ["USA", "USA", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yariv_Through-The-Mask_Mask-based_Motion_Trajectories_for_Image-to-Video_Generation_CVPR_2025_paper.html

authors:
Guy Yariv, Yuval Kirstain, Amit Zohar, Shelly Sheynin, Yaniv Taigman, Yossi Adi, Sagie Benaim, Adam Polyak

abstract:
We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advancements produce photorealistic outputs, they frequently struggle to create videos with accurate and consistent object motion, especially in multi-object scenarios. To address these limitations, we propose a two-stage compositional framework that decomposes I2V generation into: (i) An explicit intermediate representation generation stage, followed by (ii) A video generation stage that is conditioned on this representation. Our key innovation is the introduction of a mask-based motion trajectory as an intermediate representation, that captures both semantic object information and motion, enabling an expressive but compact representation of motion and semantics. To incorporate the learned representation in the second stage, we utilize object-level attention objectives. Specifically, we consider a spatial, per-object, masked-cross attention objective, integrating object-specific prompts into corresponding latent space regions and a masked spatio-temporal self-attention objective, ensuring frame-to-frame consistency for each object. We evaluate our method on challenging benchmarks with multi-object and high-motion scenarios and empirically demonstrate that the proposed method achieves state-of-the-art results in temporal coherence, motion realism, and text-prompt faithfulness. Additionally, we introduce SA-V-128, a new challenging benchmark for single-object and multi-object I2V generation, and demonstrate our method's superiority on this benchmark. Project page is available at https://guyyariv.github.io/TTM/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yariv_Through-The-Mask_Mask-based_Motion_Trajectories_for_Image-to-Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yariv_Through-The-Mask_Mask-based_Motion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yariv_2025_CVPR,
    author    = {Yariv, Guy and Kirstain, Yuval and Zohar, Amit and Sheynin, Shelly and Taigman, Yaniv and Adi, Yossi and Benaim, Sagie and Polyak, Adam},
    title     = {Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18198-18208}
}

raw_affiliation:
[{"affiliation": "GenAI, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "FAIR, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "The Hebrew University of Jerusalem", "city": "Jerusalem", "country": "Israel"}]


title: MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model
authors: Haoyuan Wang, Zhenwei Wang, Xiaoxiao Long, Cheng Lin, Gerhard Hancke, Rynson W.H. Lau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10985-10995
affiliations: ["City University of Hong Kong", "Nanjing University", "The University of Hong Kong"]
cities: ["Hong Kong", "Nanjing", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MAGE__Single_Image_to_Material-Aware_3D_via_the_Multi-View_CVPR_2025_paper.html

abstract:
With advances in deep learning models and the availability of large-scale 3D datasets, we have recently witnessed significant progress in single-view 3D reconstruction. However, existing methods often fail to reconstruct physically based material properties given a single image, limiting their applicability in complicated scenarios. This paper presents a novel approach (named MAGE) for generating 3D geometry with realistic decomposed material properties given a single image as input. Our method leverages inspiration from traditional computer graphics deferred rendering pipelines to introduce a multi-view G-buffer estimation model. The proposed model estimates G-buffers for various views as multi-domain images, including XYZ coordinates, normals, albedo, roughness, and metallic properties from a single-view RGB image. To address the inherent ambiguity and inconsistency in generating G-buffers simultaneously, we also formulate a deterministic network from the pretrained diffusion models and propose a lighting response loss that enforces consistency across these domains using PBR principles. Finally, we propose a large-scale synthetic dataset rich in material diversity for our model training. Experimental results demonstrate the effectiveness of our method in producing high-quality 3D meshes with rich material properties. Our code and dataset can be found at https://www.whyy.site/paper/mage.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MAGE__Single_Image_to_Material-Aware_3D_via_the_Multi-View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MAGE__Single_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Haoyuan and Wang, Zhenwei and Long, Xiaoxiao and Lin, Cheng and Hancke, Gerhard and Lau, Rynson W.H.},
    title     = {MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10985-10995}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: Segment Anything, Even Occluded
authors: Wei-En Tai, Yu-Lin Shih, Cheng Sun, Yu-Chiang Frank Wang, Hwann-Tzong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29385-29394
affiliations: ["National Tsing Hua University", "NVIDIA", "National Taiwan University", "Aeolus Robotics"]
cities: ["Hsinchu", "Santa Clara", "Taipei", "Taipei"]
countries: ["Taiwan", "USA", "Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tai_Segment_Anything_Even_Occluded_CVPR_2025_paper.html

abstract:
Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in various applications, including autonomous driving, robotic manipulation, and scene understanding. While existing methods require training both front-end detectors and mask decoders jointly, this approach lacks flexibility and fails to leverage the strengths of pre-existing modal detectors. To address this limitation, we propose SAMEO, a novel framework that adapts the Segment Anything Model (SAM) as a versatile mask decoder capable of interfacing with various front-end detectors to enable mask prediction even for partially occluded objects. Acknowledging the constraints of limited amodal segmentation datasets, we introduce Amodal-LVIS, a large-scale synthetic dataset comprising 300K images derived from the modal LVIS and LVVIS datasets. This dataset significantly expands the training data available for amodal segmentation research. Our experimental results demonstrate that our approach, when trained on the newly extended dataset, including Amodal-LVIS, achieves remarkable zero-shot performance on both COCOA-cls and D2SA benchmarks, highlighting its potential for generalization to unseen scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tai_Segment_Anything_Even_Occluded_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tai_Segment_Anything_Even_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tai_2025_CVPR,
    author    = {Tai, Wei-En and Shih, Yu-Lin and Sun, Cheng and Wang, Yu-Chiang Frank and Chen, Hwann-Tzong},
    title     = {Segment Anything, Even Occluded},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29385-29394}
}

raw_affiliation:
[{"affiliation": "National Tsing Hua University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "Aeolus Robotics", "city": "Taipei", "country": "Taiwan"}]


title: HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7061-7071
raw_affiliation: [{"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}]
affiliations: ["Meta Reality Labs"]
cities: ["Menlo Park"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Banerjee_HOT3D_Hand_and_Object_Tracking_in_3D_from_Egocentric_Multi-View_CVPR_2025_paper.html

authors:
Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Shangchen Han, Fan Zhang, Linguang Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, Jakob Julian Engel, Tomas Hodan

abstract:
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 subjects interacting with 33 diverse rigid objects. In addition to simple pick-up, observe, and put-down actions, the subjects perform actions typical for a kitchen, office, and living room environment. The recordings include multiple synchronized data streams containing egocentric multi-view RGB/monochrome images, eye gaze signal, scene point clouds, and 3D poses of cameras, hands, and objects. The dataset is recorded with two headsets from Meta: Project Aria, which is a research prototype of AI glasses, and Quest 3, a virtual-reality headset that has shipped millions of units. Ground-truth poses were obtained by a motion-capture system using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack and MANO formats, and objects are represented by 3D meshes with PBR materials obtained by an in-house scanner. In our experiments, we demonstrate the effectiveness of multi-view egocentric data for three popular tasks: 3D hand tracking, model-based 6DoF object pose estimation, and 3D lifting of unknown in-hand objects. The evaluated multi-view methods, whose benchmarking is uniquely enabled by HOT3D, significantly outperform their single-view counterparts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Banerjee_HOT3D_Hand_and_Object_Tracking_in_3D_from_Egocentric_Multi-View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Banerjee_HOT3D_Hand_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Banerjee_2025_CVPR,
    author    = {Banerjee, Prithviraj and Shkodrani, Sindi and Moulon, Pierre and Hampali, Shreyas and Han, Shangchen and Zhang, Fan and Zhang, Linguang and Fountain, Jade and Miller, Edward and Basol, Selen and Newcombe, Richard and Wang, Robert and Engel, Jakob Julian and Hodan, Tomas},
    title     = {HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7061-7071}
}


title: DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation
authors: Zhiqiang Shen, Ammar Sherif, Zeyuan Yin, Shitong Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4797-4806
raw_affiliation: [{"affiliation": "VILA Lab, MBZUAI", "city": "Abu Dhabi", "country": "UAE"}]
affiliations: ["VILA Lab, MBZUAI"]
cities: ["Abu Dhabi"]
countries: ["UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_DELT_A_Simple_Diversity-driven_EarlyLate_Training_for_Dataset_Distillation_CVPR_2025_paper.html

abstract:
Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution matching, gradient matching, and weight trajectory matching. Conversely, batch-to-global matching typifies decoupled methods, which are particularly advantageous for large-scale datasets. This approach has garnered substantial interest within the community, as seen in SRe^2L, G-VBSM, WMDD, and CDA. A primary challenge with the second approach is the lack of diversity among syntheses within each class since samples are optimized independently and the same global supervision signals are reused across different synthetic images. In this study, we propose a new Diversity-driven EarlyLate Training (DELT) scheme to enhance the diversity of images in batch-to-global matching with less computation. Our approach is conceptually simple yet effective, it partitions predefined IPC samples into smaller subtasks and employs local optimizations to distill each subset into distributions from distinct phases, reducing the uniformity induced by the unified optimization process. These distilled images from the subtasks demonstrate effective generalization when applied to the entire task. We conduct extensive experiments on CIFAR, Tiny-ImageNet, ImageNet-1K, and its sub-datasets. Our approach outperforms the previous state-of-the-art by 2~5% on average across different datasets and IPCs (images per class), increasing diversity per class by more than 5% while reducing synthesis time by up to 39.3% for enhancing the training efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_DELT_A_Simple_Diversity-driven_EarlyLate_Training_for_Dataset_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_DELT_A_Simple_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Zhiqiang and Sherif, Ammar and Yin, Zeyuan and Shao, Shitong},
    title     = {DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4797-4806}
}


title: MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image
authors: Shaoming Li, Qing Cai, Songqi Kong, Runqing Tan, Heng Tong, Shiji Qiu, Yongguo Jiang, Zhi Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16912-16921
cities: ["Qingdao", "Jinan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MESC-3DMining_Effective_Semantic_Cues_for_3D_Reconstruction_from_a_Single_CVPR_2025_paper.html

abstract:
Reconstructing 3D shapes from a single image plays an important role in computer vision. Many methods have been proposed and achieve impressive performance. However, existing methods mainly focus on extracting semantic information from images and then simply concatenating it with 3D point clouds without further exploring the concatenated semantics. As a result, these entangled semantic features significantly hinder the reconstruction performance. In this paper, we propose a novel single-image 3D reconstruction method called Mining Effective Semantic Cues for 3D Reconstruction from a Single Image (MESC-3D), which can actively mine effective semantic cues from entangled features. Specifically, we design an Effective Semantic Mining Module, which incorporates a point-selection map establish connections between point clouds and semantic attributes, enabling the point clouds to autonomously select the necessary information. Furthermore, to address the potential insufficiencies in semantic information from a single image, such as occlusions, inspired by the human ability to represent 3D objects using prior knowledge drawn from daily experiences, we introduce a 3D Semantic Prior Learning Module. This module employs contrastive learning paradigm along with a learnable text prompt to incorporates semantic understanding of spatial structures, enabling the model to interpret and reconstruct 3D objects with greater accuracy and realism, closely mirroring human perception of complex 3D environments. Extensive evaluations show that our method achieves significant improvements in reconstruction quality and robustness compared to prior works. Additionally, further experiments validate the strong generalization capabilities and excels in zero-shot preformance on unseen classes. Code is available at https://github.com/QINGQINGLE/MESC-3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MESC-3DMining_Effective_Semantic_Cues_for_3D_Reconstruction_from_a_Single_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MESC-3DMining_Effective_Semantic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shaoming and Cai, Qing and Kong, Songqi and Tan, Runqing and Tong, Heng and Qiu, Shiji and Jiang, Yongguo and Liu, Zhi},
    title     = {MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16912-16921}
}

raw_affiliation:
[{"affiliation": "Faculty of Computer Science and Technology, Ocean University of China1", "city": "Qingdao", "country": "China"}, {"affiliation": "School of Information Science and Engineering, Shandong University2", "city": "Jinan", "country": "China"}]

affiliations:
["Faculty of Computer Science and Technology, Ocean University of China1", "School of Information Science and Engineering, Shandong University2"]


title: RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1724-1734
raw_affiliation: []
affiliations: []
cities: []
countries: []
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_RoboBrain_A_Unified_Brain_Model_for_Robotic_Manipulation_from_Abstract_CVPR_2025_paper.html

authors:
Yuheng Ji, Huajie Tan, Jiayu Shi, Xiaoshuai Hao, Yuan Zhang, Hengyuan Zhang, Pengwei Wang, Mengdi Zhao, Yao Mu, Pengju An, Xinda Xue, Qinghang Su, Huaihai Lyu, Xiaolong Zheng, Jiaming Liu, Zhongyuan Wang, Shanghang Zhang

abstract:
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: Planning Capability, which involves decomposing complex manipulation instructions into manageable sub-tasks; Affordance Perception, the ability to recognize and interpret the affordances of interactive objects; and Trajectory Prediction, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain's core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot's diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities.Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various obotic tasks, highlighting its potential to advance robotic brain capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_RoboBrain_A_Unified_Brain_Model_for_Robotic_Manipulation_from_Abstract_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_RoboBrain_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and Xue, Xinda and Su, Qinghang and Lyu, Huaihai and Zheng, Xiaolong and Liu, Jiaming and Wang, Zhongyuan and Zhang, Shanghang},
    title     = {RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1724-1734}
}


title: Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging
authors: Xianrui Li, Yufei Cui, Jun Li, Antoni B. Chan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20800-20809
cities: ["Hong Kong", "Montreal", "Guangzhou", "Hong Kong"]
countries: ["China", "Canada", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Advancing_Multiple_Instance_Learning_with_Continual_Learning_for_Whole_Slide_CVPR_2025_paper.html

abstract:
Advances in medical imaging and deep learning have propelled progress in whole slide image (WSI) analysis, with multiple instance learning (MIL) showing promise for efficient and accurate diagnostics. However, conventional MIL models often lack adaptability to evolving datasets, as they rely on static training that cannot incorporate new information without extensive retraining. Applying continual learning (CL) to MIL models is a possible solution, but often sees limited improvements. In this paper, we analyze CL in the context of attention MIL models and find that the model forgetting is mainly concentrated in the attention layers of the MIL model. Using the results of this analysis we propose two components for improving CL on MIL:Attention Knowledge Distillation (AKD) and the Pseudo-Bag Memory Pool (PMP). AKD mitigates catastrophic forgetting by focusing on retaining attention layer knowledge between learning sessions, while PMP reduces the memory footprint by selectively storing only the most informative patches, or "pseudo-bags" from WSIs. Experimental evaluations demonstrate that our method significantly improves both accuracy and memory efficiency on diverse WSI datasets, outperforming current state-of-the-art CL methods. This work provides a foundation for CL in large-scale, weakly annotated clinical datasets, paving the way for more adaptable and resilient diagnostic models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Advancing_Multiple_Instance_Learning_with_Continual_Learning_for_Whole_Slide_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Advancing_Multiple_Instance_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xianrui and Cui, Yufei and Li, Jun and Chan, Antoni B.},
    title     = {Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20800-20809}
}

raw_affiliation:
[{"affiliation": "Dept. of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Noah\u2019s Ark Lab, Huawei Canada", "city": "Montreal", "country": "Canada"}, {"affiliation": "Guangzhou Bingli Technology Co., Ltd.", "city": "Guangzhou", "country": "China"}, {"affiliation": "Dept. of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Dept. of Computer Science, City University of Hong Kong", "Noah\u2019s Ark Lab, Huawei Canada", "Guangzhou Bingli Technology Co., Ltd.", "Dept. of Computer Science, City University of Hong Kong"]


authors: Dongyao Jiang, Haodong Jing, Yongqiang Ma, Nanning Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9860-9869
affiliations: ["Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Beyond_Image_Classification_A_Video_Benchmark_and_Dual-Branch_Hybrid_Discrimination_CVPR_2025_paper.html

title:
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning

abstract:
Human reasoning naturally combines concepts to identify unseen compositions, a capability that Compositional Zero-Shot Learning (CZSL) aims to replicate in machine learning models. However, we observe that focusing solely on typical image classification tasks in CZSL may limit models' compositional generalization potential. To address this, we introduce C-EgoExo, a video-based benchmark, along with a compositional action recognition task to enable more comprehensive evaluations. Inspired by human reasoning processes, we propose a Dual-branch Hybrid Discrimination (DHD) framework, featuring two branches that decode visual inputs in distinct observation sequences. Through a cross-attention mechanism and a contextual dependency encoder, DHD effectively mitigates challenges posed by conditional variance. We further design a Copula-based orthogonal decoding loss to counteract contextual interference in primitive decoding. Our approach demonstrates outstanding performance across diverse CZSL tasks, excelling in both image-based and video-based modalities and in attribute-object and action-object compositions, setting a new benchmark for CZSL evaluation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Beyond_Image_Classification_A_Video_Benchmark_and_Dual-Branch_Hybrid_Discrimination_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Beyond_Image_Classification_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Dongyao and Jing, Haodong and Ma, Yongqiang and Zheng, Nanning},
    title     = {Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9860-9869}
}

raw_affiliation:
[{"affiliation": "Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University", "city": "Xi'an", "country": "China"}]


authors: Woojin Lee, Hyugjae Chang, Jaeho Moon, Jaehyup Lee, Munchurl Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8848-8858
affiliations: ["KAIST", "KNU"]
cities: ["Daejeon", "Daegu"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_ABBSPO_Adaptive_Bounding_Box_Scaling_and_Symmetric_Prior_based_Orientation_CVPR_2025_paper.html

title:
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects

abstract:
Weakly supervised Oriented Object Detection (WS-OOD) has gained attention as a cost-effective alternative to fully supervised methods, providing efficiency and high accuracy. Among weakly supervised approaches, horizontal bounding box (HBox) supervised OOD stands out for its ability to directly leverage existing HBox annotations while achieving the highest accuracy under weak supervision settings. This paper introduces adaptive bounding box scaling and symmetry-prior-based orientation prediction, called ABBSPO that is a framework for WS-OOD. Our ABBSPO addresses the limitations of previous HBox-supervised OOD methods, which compare ground truth (GT) HBoxes directly with predicted RBoxes' minimum circumscribed rectangles, often leading to inaccuracies. To overcome this, we propose: (i) Adaptive Bounding Box Scaling (ABBS) that appropriately scales the GT HBoxes to optimize for the size of each predicted RBox, ensuring more accurate prediction for RBoxes' scales; and (ii) a Symmetric Prior Angle (SPA) loss that uses the inherent symmetry of aerial objects for self-supervised learning, addressing the issue in previous methods where learning fails if they consistently make incorrect predictions for all three augmented views (original, rotated, and flipped). Extensive experimental results demonstrate that our ABBSPO achieves state-of-the-art results, outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_ABBSPO_Adaptive_Bounding_Box_Scaling_and_Symmetric_Prior_based_Orientation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_ABBSPO_Adaptive_Bounding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Woojin and Chang, Hyugjae and Moon, Jaeho and Lee, Jaehyup and Kim, Munchurl},
    title     = {ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8848-8858}
}

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


title: Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks
authors: Yu Zhou, Dian Zheng, Qijie Mo, Renjie Lu, Kun-Yu Lin, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20350-20359
cities: ["Guangzhou", "Shenzhen", "Beijing", "Guangzhou"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Decoupled_Distillation_to_Erase_A_General_Unlearning_Method_for_Any_CVPR_2025_paper.html

abstract:
In this work, we present DEcoupLEd Distillation To Erase (DELETE), a general and strong unlearning method for any class-centric tasks. To derive this, we first propose a theoretical framework to analyze the general form of unlearning loss and decompose it into forgetting and retention terms. Through the theoretical framework, we point out that a class of previous methods could be mainly formulated as a loss that implicitly optimizes the forgetting term while lacking supervision for the retention term, disturbing the distribution of pre-trained model and struggling to adequately preserve knowledge of the remaining classes. To address it, we refine the retention term using "dark knowledge" and propose a mask distillation unlearning method. By applying a mask to separate forgetting logits from retention logits, our approach optimizes both the forgetting and refined retention components simultaneously, retaining knowledge of the remaining classes while ensuring thorough forgetting of the target class. Without access to the remaining data or intervention (i.e., used in some works), we achieve state-of-the-art performance across various benchmarks. What's more, DELETE is a general solution that can be applied to various downstream tasks, including face recognition, backdoor defense, and semantic segmentation with great performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Decoupled_Distillation_to_Erase_A_General_Unlearning_Method_for_Any_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Decoupled_Distillation_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yu and Zheng, Dian and Mo, Qijie and Lu, Renjie and Lin, Kun-Yu and Zheng, Wei-Shi},
    title     = {Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20350-20359}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Peng Cheng Laboratory, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Province Key Laboratory of Information Security Technology, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "Peng Cheng Laboratory, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "Guangdong Province Key Laboratory of Information Security Technology, China"]


title: TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
authors: Wang Yu-Hang, Junkang Guo, Aolei Liu, Kaihao Wang, Zaitong Wu, Zhenyu Liu, Wenfei Yin, Jian Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15476-15485
affiliations: ["School of Computer Science and Information Engineering, Hefei University of Technology"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu-Hang_TAET_Two-Stage_Adversarial_Equalization_Training_on_Long-Tailed__Distributions_CVPR_2025_paper.html

abstract:
Adversarial robustness remains a significant challenge in deploying deep neural networks for real-world applications. While adversarial training is widely acknowledged as a promising defense strategy, most existing studies primarily focus on balanced datasets, neglecting the fact that real-world data often exhibit a long-tailed distribution, which introduces substantial challenges to robustness. In this paper, we provide an in-depth analysis of adversarial training in the context of long-tailed distributions and identify the limitations of the current state-of-the-art method, AT-BSL, in achieving robust performance under such conditions. To address these challenges, we propose a novel training framework, TAET, which incorporates an initial stabilization phase followed by a stratified, equalization adversarial training phase. Furthermore, prior work on long-tailed robustness has largely overlooked a crucial evaluation metric--Balanced Accuracy. To fill this gap, we introduce the concept of Balanced Robustness, a comprehensive metric that measures robustness specifically under long-tailed distributions. Extensive experiments demonstrate that our method outperforms existing advanced defenses, yielding significant improvements in both memory and computational efficiency. We believe this work represents a substantial step forward in tackling robustness challenges in real-world applications. Our paper code can be found at https://github.com/BuhuiOK/TAET-Two-Stage-Adversarial-Equalization-Training-on-Long-Tailed-Distributions

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu-Hang_TAET_Two-Stage_Adversarial_Equalization_Training_on_Long-Tailed__Distributions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu-Hang_TAET_Two-Stage_Adversarial_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu-Hang_2025_CVPR,
    author    = {Yu-Hang, Wang and Guo, Junkang and Liu, Aolei and Wang, Kaihao and Wu, Zaitong and Liu, Zhenyu and Yin, Wenfei and Liu, Jian},
    title     = {TAET: Two-Stage Adversarial Equalization Training on Long-Tailed  Distributions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15476-15485}
}

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


title: Few-shot Personalized Scanpath Prediction
authors: Ruoyu Xue, Jingyi Xu, Sounak Mondal, Hieu Le, Greg Zelinsky, Minh Hoai, Dimitris Samaras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13497-13507
affiliations: ["Stony Brook University, USA", "EPFL, Switzerland", "The University of Adelaide, Australia"]
cities: ["Stony Brook", "Lausanne", "Adelaide"]
countries: ["USA", "Switzerland", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Few-shot_Personalized_Scanpath_Prediction_CVPR_2025_paper.html

abstract:
A personalized model for scanpath prediction provides insights into the visual preferences and attention patterns of individual subjects. However, existing methods for training scanpath prediction models are data-intensive and cannot be effectively personalized to new individuals with only a few available examples. In this paper, we propose few-shot personalized scanpath prediction task (FS-PSP) and a novel method to address it, which aims to predict scanpaths for an unseen subject using minimal support data of that subject's scanpath behavior. The key to our method's adaptability is the Subject-Embedding Network (SE-Net), specifically designed to capture unique, individualized representations for each subject's scanpaths. SE-Net generates subject embeddings that effectively distinguish between subjects while minimizing variability among scanpaths from the same individual. The personalized scanpath prediction model is then conditioned on these subject embeddings to produce accurate, personalized results. Experiments on multiple eye-tracking datasets demonstrate that our method excels in FS-PSP settings and does not require any fine-tuning steps at test time. Code is available at: https://github.com/cvlab-stonybrook/few-shot-scanpath

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Few-shot_Personalized_Scanpath_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_Few-shot_Personalized_Scanpath_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Ruoyu and Xu, Jingyi and Mondal, Sounak and Le, Hieu and Zelinsky, Greg and Hoai, Minh and Samaras, Dimitris},
    title     = {Few-shot Personalized Scanpath Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13497-13507}
}

raw_affiliation:
[{"affiliation": "Stony Brook University, USA", "city": "Stony Brook", "country": "USA"}, {"affiliation": "EPFL, Switzerland", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "The University of Adelaide, Australia", "city": "Adelaide", "country": "Australia"}]


title: Do Your Best and Get Enough Rest for Continual Learning
authors: Hankyul Kang, Gregor Seifer, Donghyun Lee, Jongbin Ryu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10077-10086
affiliations: ["Ajou University", "KAIST"]
cities: ["Suwon", "Daejeon"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_Do_Your_Best_and_Get_Enough_Rest_for_Continual_Learning_CVPR_2025_paper.html

abstract:
According to the forgetting curve theory, we can enhance memory retention by learning extensive data and taking adequate rest. This means that in order to effectively retain new knowledge, it is essential to learn it thoroughly and ensure sufficient rest so that our brain can memorize without forgetting. The main takeaway from this theory is that learning extensive data at once necessitates sufficient rest before learning the same data again. This aspect of human long-term memory retention can be effectively utilized to address the continual learning of neural networks. Retaining new knowledge for a long period of time without catastrophic forgetting is the critical problem of continual learning. Therefore, based on Ebbinghaus' theory, we introduce the view-batch model that adjusts the learning schedules to optimize the recall interval between retraining the same samples. The proposed view-batch model allows the network to get enough rest to learn extensive knowledge from the same samples with a recall interval of sufficient length. To this end, we specifically present two approaches: 1) a replay method that guarantees the optimal recall interval, and 2) a self-supervised learning that acquires extensive knowledge from a single training sample at a time. We empirically show that these approaches of our method are aligned with the forgetting curve theory, which can enhance long-term memory. In our experiments, we also demonstrate that our method significantly improves many state-of-the-art continual learning methods in various protocols and scenarios. We open-source this project at https://github.com/hankyul2/ViewBatchModel.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_Do_Your_Best_and_Get_Enough_Rest_for_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_Do_Your_Best_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kang_2025_CVPR,
    author    = {Kang, Hankyul and Seifer, Gregor and Lee, Donghyun and Ryu, Jongbin},
    title     = {Do Your Best and Get Enough Rest for Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10077-10086}
}

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


title: Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration
authors: Yiyang Chen, Tianyu Ding, Lei Wang, Jing Huo, Yang Gao, Wenbin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9881-9890
cities: ["Nanjing", "Redmond", "Wollongong", "Shenzhen"]
countries: ["China", "USA", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Enhancing_Few-Shot_Class-Incremental_Learning_via_Training-Free_Bi-Level_Modality_Calibration_CVPR_2025_paper.html

abstract:
Few-shot Class-Incremental Learning (FSCIL) challenges models to adapt to new classes with limited samples, presenting greater difficulties than traditional classincremental learning. While existing approaches rely heavily on visual models and require additional training during base or incremental phases, we propose a training-free framework that leverages pre-trained visual-language models like CLIP. At the core of our approach is a novel Bilevel Modality Calibration (BiMC) strategy. Our framework initially performs intra-modal calibration, combining LLM-generated fine-grained category descriptions with visual prototypes from the base session to achieve precise classifier estimation. This is further complemented by inter-modal calibration that fuses pre-trained linguistic knowledge with task-specific visual priors to mitigate modality-specific biases. To enhance prediction robustness, we introduce additional metrics and strategies that maximize the utilization of limited data. Extensive experimental results demonstrate that our approach significantly outperforms existing methods. Code is available at: https://github.com/yychen016/BiMC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Enhancing_Few-Shot_Class-Incremental_Learning_via_Training-Free_Bi-Level_Modality_Calibration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Enhancing_Few-Shot_Class-Incremental_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yiyang and Ding, Tianyu and Wang, Lei and Huo, Jing and Gao, Yang and Li, Wenbin},
    title     = {Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9881-9890}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Applied Sciences Group, Microsoft, USA", "city": "Redmond", "country": "USA"}, {"affiliation": "University of Wollongong, Australia", "city": "Wollongong", "country": "Australia"}, {"affiliation": "Shenzhen Research Institute of Nanjing University, Shenzhen, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University, China", "Applied Sciences Group, Microsoft, USA", "University of Wollongong, Australia", "Shenzhen Research Institute of Nanjing University, Shenzhen, China"]


title: MUSt3R: Multi-view Network for Stereo 3D Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1050-1060
affiliations: ["Naver Labs Europe", "EPFL"]
cities: ["Grenoble", "Lausanne"]
countries: ["France", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cabon_MUSt3R_Multi-view_Network_for_Stereo_3D_Reconstruction_CVPR_2025_paper.html

authors:
Yohann Cabon, Lucas Stoffl, Leonid Antsfeld, Gabriela Csurka, Boris Chidlovskii, Jerome Revaud, Vincent Leroy

abstract:
DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be aligned in a global coordinate system. The number of pairs, growing quadratically, is an inherent limitation that becomes especially concerning for robust and fast optimization in the case of large image collections. In this paper, we propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns. Indeed, we propose a Multi-view Network for Stereo 3D Reconstruction, or MUSt3R, that modifies the DUSt3R architecture by making it symmetric and extending it to directly predict 3D structure for all views in a common coordinate frame. Second, we entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity and to scale the reconstruction to large collections, inferring thousands of 3D pointmaps at high frame-rates with limited added complexity. The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios showing state-of-the-art performance on various 3D downstream tasks, including uncalibrated Visual Odometry, relative camera pose, scale and focal estimation, 3D reconstruction and multi-view depth estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cabon_MUSt3R_Multi-view_Network_for_Stereo_3D_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cabon_MUSt3R_Multi-view_Network_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cabon_2025_CVPR,
    author    = {Cabon, Yohann and Stoffl, Lucas and Antsfeld, Leonid and Csurka, Gabriela and Chidlovskii, Boris and Revaud, Jerome and Leroy, Vincent},
    title     = {MUSt3R: Multi-view Network for Stereo 3D Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1050-1060}
}

raw_affiliation:
[{"affiliation": "Naver Labs Europe", "city": "Grenoble", "country": "France"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}]


title: Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8568-8578
cities: ["Hefei", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid-Level_Instruction_Injection_for_Video_Token_Compression_in_Multi-modal_Large_CVPR_2025_paper.html

authors:
Zhihang Liu, Chen-Wei Xie, Pandeng Li, Liming Zhao, Longxiang Tang, Yun Zheng, Chuanbin Liu, Hongtao Xie

abstract:
Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strategies. However, the visual content is not equally contributed to user instructions, existing strategies (e.g., average pool) inevitably lead to the loss of potentially useful information. To tackle this, we propose the Hybrid-level Instruction Injection Strategy for Conditional Token Compression in MLLMs (HICom), utilizing the instruction as a condition to guide the compression from both local and global levels. This encourages the compression to retain the maximum amount of user-focused information while reducing visual tokens to minimize computational burden. Specifically, the instruction condition is injected into the grouped visual tokens at the local level and the learnable tokens at the global level, and we conduct the attention mechanism to complete the conditional compression. From the hybrid-level compression, the instruction-relevant visual parts are highlighted while the temporal-spatial structure is also preserved for easier understanding of LLMs. To further unleash the potential of HICom, we introduce a new conditional pre-training stage with our proposed dataset HICom-248K. Experiments show that our HICom can obtain distinguished video understanding ability with fewer tokens, increasing the performance by 2.43% average on three multiple-choice QA benchmarks and saving 78.8% tokens compared with the SOTA method. The code is available at https://github.com/lntzm/HICom.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid-Level_Instruction_Injection_for_Video_Token_Compression_in_Multi-modal_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hybrid-Level_Instruction_Injection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhihang and Xie, Chen-Wei and Li, Pandeng and Zhao, Liming and Tang, Longxiang and Zheng, Yun and Liu, Chuanbin and Xie, Hongtao},
    title     = {Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8568-8578}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Tongyi Lab, Alibaba Group", "Tsinghua University"]


authors: Jiuming Liu, Jinru Han, Lihao Liu, Angelica I. Aviles-Rivero, Chaokang Jiang, Zhe Liu, Hesheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17626-17636
cities: ["Shanghai", "Cambridge", "Xuzhou", "Shanghai"]
countries: ["China", "UK", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Mamba4D_Efficient_4D_Point_Cloud_Video_Understanding_with_Disentangled_Spatial-Temporal_CVPR_2025_paper.html

title:
Mamba4D: Efficient 4D Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models

abstract:
Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world. However, designing an effective 4D backbone remains challenging, mainly due to the irregular and unordered distribution of points and temporal inconsistencies across frames. Also, recent transformer-based 4D backbones commonly suffer from large computational costs due to their quadratic complexity, particularly for long video sequences. To address these challenges, we propose a novel point cloud video understanding backbone purely based on the State Space Models (SSMs). Specifically, we first disentangle space and time in 4D video sequences and then establish the spatio-temporal correlation with the unified spatial-temporal Mamba blocks. The Intra-frame Spatial Mamba module is developed to encode locally similar geometric structures within a certain temporal stride. Subsequently, locally correlated tokens are delivered to the Inter-frame Temporal Mamba module, which integrates long-term point features across the entire video with linear complexity. Our proposed Mamba4D achieves competitive performance on the MSR-Action3D action recognition (+10.4% accuracy), HOI4D action segmentation (+0.7 F1 Score), and Synthia4D semantic segmentation (+0.19 mIoU) datasets. Mamba4D also has a significant efficiency improvement, especially for long video sequences, with 87.5% GPU memory reduction and 5.36 times speed-up. Codes are released at https://github.com/IRMVLab/Mamba4D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Mamba4D_Efficient_4D_Point_Cloud_Video_Understanding_with_Disentangled_Spatial-Temporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Mamba4D_Efficient_4D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiuming and Han, Jinru and Liu, Lihao and Aviles-Rivero, Angelica I. and Jiang, Chaokang and Liu, Zhe and Wang, Hesheng},
    title     = {Mamba4D: Efficient 4D Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17626-17636}
}

raw_affiliation:
[{"affiliation": "Department of Automation, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}, {"affiliation": "China University of Mining and Technology", "city": "Xuzhou", "country": "China"}, {"affiliation": "MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Department of Automation, Shanghai Jiao Tong University", "University of Cambridge", "China University of Mining and Technology", "MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University"]


authors: Xin Zhang, Robby T. Tan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14527-14537
affiliations: ["National University of Singapore", "ASUS Intelligent Cloud Services"]
cities: ["Singapore", "Taipei"]
countries: ["Singapore", "Taiwan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Mamba_as_a_Bridge_Where_Vision_Foundation_Models_Meet_Vision_CVPR_2025_paper.html

title:
Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation

abstract:
Vision Foundation Models (VFMs) and Vision-Language Models (VLMs) have gained traction in Domain Generalized Semantic Segmentation (DGSS) due to their strong generalization capabilities. However, existing DGSS methods often rely exclusively on either VFMs or VLMs, overlooking their complementary strengths. VFMs (e.g., DINOv2) excel at capturing fine-grained features, while VLMs (e.g., CLIP) provide robust text alignment but struggle with coarse granularity. Despite their complementary strengths, effectively integrating VFMs and VLMs with attention mechanisms is challenging, as the increased patch tokens complicate long-sequence modeling. To address this, we propose MFuser, a novel Mamba-based fusion framework that efficiently combines the strengths of VFMs and VLMs while maintaining linear scalability in sequence length. MFuser consists of two key components: MVFuser, which acts as a co-adapter to jointly fine-tune the two models by capturing both sequential and spatial dynamics; and MTEnhancer, a hybrid attention-Mamba module that refines text embeddings by incorporating image priors. Our approach achieves precise feature locality and strong text alignment without incurring significant computational overhead. Extensive experiments demonstrate that MFuser significantly outperforms state-of-the-art DGSS methods, achieving 68.20 mIoU on synthetic-to-real and 71.87 mIoU on real-to-real benchmarks. The code is available at https://github.com/devinxzhang/MFuser.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Mamba_as_a_Bridge_Where_Vision_Foundation_Models_Meet_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Mamba_as_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xin and Tan, Robby T.},
    title     = {Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14527-14537}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "ASUS Intelligent Cloud Services", "city": "Taipei", "country": "Taiwan"}]


authors: Ting Yu, Yi Lin, Jun Yu, Zhenyu Lou, Qiongjie Cui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12335-12346
cities: ["Hangzhou", "Shenzhen", "Hangzhou", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Vision-Guided_Action_Enhancing_3D_Human_Motion_Prediction_with_Gaze-informed_Affordance_CVPR_2025_paper.html

title:
Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes

abstract:
Recent advances in human motion prediction (HMP) have shifted focus from isolated motion data to integrating human-scene correlations. In particular, the latest methods leverage human gaze points, using their spatial coordinates to indicate intent--where a person might move within a 3D environment. Despite promising trajectory results, these methods often produce inaccurate poses by overlooking the semantic implications of gaze, specifically the affordances of observed objects, which indicate the possible interactions. To address this, we propose GAP3DS, an affordance-aware HMP model that utilizes gaze-informed object affordances to improve HMP in complex 3D environments. GAP3DS incorporates a gaze-guided affordance learner to identify relevant objects in the scene and infer their affordances based on human gaze, thus contextualizing future human-object interactions. This affordance information, enriched with visual features and gaze data, conditions the generation of multiple human-object interaction poses, which are subsequently decoded into final motion predictions. Extensive experiments on two real-world datasets demonstrate that GAP3DS outperforms state-of-the-art methods in both trajectory and pose accuracy, producing more physically consistent and contextually grounded predictions. For more details and code, please refer to the project page.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Vision-Guided_Action_Enhancing_3D_Human_Motion_Prediction_with_Gaze-informed_Affordance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Vision-Guided_Action_Enhancing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Ting and Lin, Yi and Yu, Jun and Lou, Zhenyu and Cui, Qiongjie},
    title     = {Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12335-12346}
}

raw_affiliation:
[{"affiliation": "Hangzhou Normal University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Singapore University of Technology and Design", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Hangzhou Normal University", "Harbin Institute of Technology (Shenzhen)", "Zhejiang University", "Singapore University of Technology and Design"]


title: LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning
authors: Peng Wu, Xiankai Lu, Hao Hu, Yongqin Xian, Jianbing Shen, Wenguan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30301-30311
cities: ["Jinan", "Hangzhou", "Mountain View", "Macau"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_LOGICZSL_Exploring_Logic-induced_Representation_for_Compositional_Zero-shot_Learning_CVPR_2025_paper.html

abstract:
Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by learning the primitive concepts (*i.e.*, attribute and object) from the training set. While recent works achieve impressive results in CZSL by leveraging large vision-language models like CLIP, they ignore the rich semantic relationships between primitive concepts and their compositions. In this work, we propose LOGICZSL, a novel logic-induced learning framework to explicitly model the semantic relationships. Our logic-induced learning framework formulates the relational knowledge constructed from large language models as a set of logic rules, and grounds them onto the training data. Our logic-induced losses are complementary to the widely used CZSL losses, therefore can be employed to inject the semantic information into any existing CZSL methods. Extensive experimental results show that our method brings significant performance improvements across diverse datasets (*i.e.*, CGQA, UT-Zappos50K, MIT-States) with strong CLIP-based methods and settings (*i.e.*, Close World, Open World).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_LOGICZSL_Exploring_Logic-induced_Representation_for_Compositional_Zero-shot_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_LOGICZSL_Exploring_Logic-induced_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Peng and Lu, Xiankai and Hu, Hao and Xian, Yongqin and Shen, Jianbing and Wang, Wenguan},
    title     = {LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30301-30311}
}

raw_affiliation:
[{"affiliation": "Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "SKL-IOTSC, CIS, University of Macau", "city": "Macau", "country": "China"}]

affiliations:
["Shandong University", "ReLER, CCAI, Zhejiang University", "Google", "SKL-IOTSC, CIS, University of Macau"]


title: ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation
authors: Ling-An Zeng, Guohong Huang, Yi-Lin Wei, Shengbo Gu, Yu-Ming Tang, Jingke Meng, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12358-12369
cities: ["Guangzhou", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_ChainHOI_Joint-based_Kinematic_Chain_Modeling_for_Human-Object_Interaction_Generation_CVPR_2025_paper.html

abstract:
We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels.    Unlike existing methods that implicitly model interactions using full-body poses as tokens, we argue that explicitly modeling joint-level interactions is more natural and effective for generating realistic HOIs, as it directly captures the geometric and semantic relationships between joints, rather than modeling interactions in the latent pose space.    To this end, ChainHOI introduces a novel joint graph to capture potential interactions with objects, and a Generative Spatiotemporal Graph Convolution Network to explicitly model interactions at the joint level.     Furthermore, we propose a Kinematics-based Interaction Module that explicitly models interactions at the kinetic chain level, ensuring more realistic and biomechanically coherent motions.    Evaluations on two public datasets demonstrate that ChainHOI significantly outperforms previous methods, generating more realistic, and semantically consistent HOIs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_ChainHOI_Joint-based_Kinematic_Chain_Modeling_for_Human-Object_Interaction_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_ChainHOI_Joint-based_Kinematic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Ling-An and Huang, Guohong and Wei, Yi-Lin and Gu, Shengbo and Tang, Yu-Ming and Meng, Jingke and Zheng, Wei-Shi},
    title     = {ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12358-12369}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Beijing", "country": "China"}]

affiliations:
["Sun Yat-sen University, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss
authors: Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15538-15548
affiliations: ["University of Adelaide, Australia", "CVSSP, University of Surrey, UK"]
cities: ["Adelaide", "Guildford"]
countries: ["Australia", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pitawela_CLOC_Contrastive_Learning_for_Ordinal_Classification_with_Multi-Margin_N-pair_Loss_CVPR_2025_paper.html

abstract:
In ordinal classification, misclassifying neighboring ranks is common, yet the consequences of these errors are not the same.For example, misclassifying benign tumor categories is less consequential, compared to an error at the pre-cancerous to cancerous threshold, which could profoundly influence treatment choices. Despite this, existing ordinal classification methods do not account for the varying importance of these margins, treating all neighboring classes as equally significant. To address this limitation, we propose CLOC, a new margin-based contrastive learning method for ordinal classification that learns an ordered representation based on the optimization of multiple margins with a novel multi-margin n-pair loss (MMNP).CLOC enables flexible decision boundaries across key adjacent categories, facilitating smooth transitions between classes and reducing the risk of overfitting to biases present in the training data.We provide empirical discussion regarding the properties of MMNP and show experimental results on five real-world image datasets (Adience, Historical Colour Image Dating, Knee Osteoarthritis, Indian Diabetic Retinopathy Image, and Breast Carcinoma Subtyping) and one synthetic dataset simulating clinical decision bias.Our results demonstrate that CLOC outperforms existing ordinal classification methods and show the interpretability and controllability of CLOC in learning meaningful, ordered representations that align with clinical and practical needs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pitawela_CLOC_Contrastive_Learning_for_Ordinal_Classification_with_Multi-Margin_N-pair_Loss_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pitawela_CLOC_Contrastive_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pitawela_2025_CVPR,
    author    = {Pitawela, Dileepa and Carneiro, Gustavo and Chen, Hsiang-Ting},
    title     = {CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15538-15548}
}

raw_affiliation:
[{"affiliation": "University of Adelaide, Australia", "city": "Adelaide", "country": "Australia"}, {"affiliation": "CVSSP, University of Surrey, UK", "city": "Guildford", "country": "UK"}]


title: Universal Actions for Enhanced Embodied Foundation Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22508-22519
cities: ["Beijing", "Hong Kong", "Beijing", "Beijing", "Shanghai", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Universal_Actions_for_Enhanced_Embodied_Foundation_Models_CVPR_2025_paper.html

authors:
Jinliang Zheng, Jianxiong Li, Dongxiu Liu, Yinan Zheng, Zhihao Wang, Zhonghong Ou, Yu Liu, Jingjing Liu, Ya-Qin Zhang, Xianyuan Zhan

abstract:
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticeable difficulties. Despite the availability of many crowd-sourced embodied datasets, their action spaces often exhibit significant heterogeneity due to distinct physical embodiment and control interfaces for different robots, causing substantial challenges in developing embodied foundation models using cross-embodiment data. In this paper, we introduce UniAct, a new embodied foundation modeling framework operating in the Universal Action Space. Our learned universal actions capture the generic behaviors across diverse robots by exploiting their shared structural features, and enable enhanced cross-domain data utilization and cross-embodiment generalizations by eliminating the notorious heterogeneity. Moreover, the universal actions can be efficiently translated back to heterogeneous actionable commands by simply adding embodiment-specific details, from which fast adaptation to new robots becomes simple and straightforward. Our 0.5B instantiation of UniAct outperforms 14X larger SOTA embodied foundation models in extensive evaluations on various real-world and simulation robots, showcasing exceptional cross-embodiment control and adaptation capability, highlighting the crucial benefit of adopting universal actions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Universal_Actions_for_Enhanced_Embodied_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Universal_Actions_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Jinliang and Li, Jianxiong and Liu, Dongxiu and Zheng, Yinan and Wang, Zhihao and Ou, Zhonghong and Liu, Yu and Liu, Jingjing and Zhang, Ya-Qin and Zhan, Xianyuan},
    title     = {Universal Actions for Enhanced Embodied Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22508-22519}
}

raw_affiliation:
[{"affiliation": "AIR, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Sensetime Research", "city": "Hong Kong", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "BAAI", "city": "Beijing", "country": "China"}]

affiliations:
["AIR, Tsinghua University", "Sensetime Research", "Peking University", "Beijing University of Posts and Telecommunications", "Shanghai AI Lab", "BAAI"]


title: ObjectMover: Generative Object Movement with Video Prior
authors: Xin Yu, Tianyu Wang, Soo Ye Kim, Paul Guerrero, Xi Chen, Qing Liu, Zhe Lin, Xiaojuan Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17682-17691
affiliations: ["The University of Hong Kong", "Adobe Research"]
cities: ["Hong Kong", "San Jose"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_ObjectMover_Generative_Object_Movement_with_Video_Prior_CVPR_2025_paper.html

abstract:
Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose based on perspective, accurately filling occluded regions, and ensuring coherent synchronization of shadows and reflections while maintaining the object identity. In this paper, we present ObjectMover, a generative model that can perform object movement in highly challenging scenes. Our key insight is that we model this task as a sequence-to-sequence problem and fine-tune a video generation model to leverage its knowledge of consistent object generation across video frames. We show that with this approach, our model is able to adjust to complex real-world scenarios, handling extreme lighting harmonization and object effect movement. As large-scale data for object movement are unavailable, we construct a data generation pipeline using a modern game engine to synthesize high-quality data pairs. We further propose a multi-task learning strategy that enables training on real-world video data to improve the model generalization. Through extensive experiments, we demonstrate that ObjectMover achieves outstanding results and adapts well to real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_ObjectMover_Generative_Object_Movement_with_Video_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_ObjectMover_Generative_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Xin and Wang, Tianyu and Kim, Soo Ye and Guerrero, Paul and Chen, Xi and Liu, Qing and Lin, Zhe and Qi, Xiaojuan},
    title     = {ObjectMover: Generative Object Movement with Video Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17682-17691}
}

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


title: FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution
authors: Junyang Chen, Jinshan Pan, Jiangxin Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28188-28197
affiliations: ["School of Computer Science and Engineering, Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FaithDiff_Unleashing_Diffusion_Priors_for_Faithful_Image_Super-resolution_CVPR_2025_paper.html

abstract:
Faithful image super-resolution (SR) not only needs to recover images that appear realistic, similar to image generation tasks, but also requires that the restored images maintain fidelity and structural consistency with the input. To this end, we propose a simple and effective method, named FaithDiff, to fully harness the impressive power of latent diffusion models (LDMs) for faithful image SR. In contrast to existing diffusion-based SR methods that freeze the diffusion model pre-trained on high-quality images, we propose to unleash the diffusion prior to identify useful information and recover faithful structures. As there exists a significant gap between the features of degraded inputs and the noisy latent from the diffusion model, we then develop an effective alignment module to explore useful features from degraded inputs to align well with the diffusion process. Considering the indispensable roles and interplay of the encoder and diffusion model in LDMs, we jointly fine-tune them in a unified optimization framework, facilitating the encoder to extract useful features that coincide with diffusion process. Extensive experimental results demonstrate that FaithDiff outperforms state-of-the-art methods, providing high-quality and faithful SR results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_FaithDiff_Unleashing_Diffusion_Priors_for_Faithful_Image_Super-resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_FaithDiff_Unleashing_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Junyang and Pan, Jinshan and Dong, Jiangxin},
    title     = {FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28188-28197}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]


title: MLLM-as-a-Judge for Image Safety without Human Labeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14657-14666
affiliations: ["1", "2", "3", "4"]
cities: ["Beijing", "College Park", "Hong Kong", "Beijing"]
countries: ["China", "USA", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MLLM-as-a-Judge_for_Image_Safety_without_Human_Labeling_CVPR_2025_paper.html

authors:
Zhenting Wang, Shuming Hu, Shiyu Zhao, Xiaowen Lin, Felix Juefei-Xu, Zhuowei Li, Ligong Han, Harihar Subramanyam, Li Chen, Jianfa Chen, Nan Jiang, Lingjuan Lyu, Shiqing Ma, Dimitris N. Metaxas, Ankit Jain

abstract:
Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however brings a series of drawbacks. First, relying on human annotators to label data following intricate and detailed guidelines is both expensive and labor-intensive. Furthermore, users of safety judgment systems may need to frequently update safety rules, making fine-tuning on human-based annotation more challenging. This raises the research question: Can we detect unsafe images by querying MLLMs in a zero-shot setting using a predefined safety constitution (a set of safety rules)? Our research showed that simply querying pre-trained MLLMs does not yield satisfactory results. This lack of effectiveness stems from factors such as the subjectivity of safety rules, the complexity of lengthy constitutions, and the inherent biases in the models. To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary. Experiment results demonstrate that our method is highly effective for zero-shot image safety judgment tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MLLM-as-a-Judge_for_Image_Safety_without_Human_Labeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MLLM-as-a-Judge_for_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhenting and Hu, Shuming and Zhao, Shiyu and Lin, Xiaowen and Juefei-Xu, Felix and Li, Zhuowei and Han, Ligong and Subramanyam, Harihar and Chen, Li and Chen, Jianfa and Jiang, Nan and Lyu, Lingjuan and Ma, Shiqing and Metaxas, Dimitris N. and Jain, Ankit},
    title     = {MLLM-as-a-Judge for Image Safety without Human Labeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14657-14666}
}

raw_affiliation:
[{"affiliation": "1", "city": "Beijing", "country": "China"}, {"affiliation": "2", "city": "College Park", "country": "USA"}, {"affiliation": "3", "city": "Hong Kong", "country": "China"}, {"affiliation": "4", "city": "Beijing", "country": "China"}]


authors: Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1230-1240
cities: ["Paris", "Villeurbanne", "Grenoble", "New York", "Paris", "Grenoble"]
countries: ["France", "France", "France", "USA", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bodrito_A_New_Statistical_Model_of_Star_Speckles_for_Learning_to_CVPR_2025_paper.html

title:
A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations

abstract:
The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight.  Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles.  Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation.  The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT).  It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers.  The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bodrito_A_New_Statistical_Model_of_Star_Speckles_for_Learning_to_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bodrito_A_New_Statistical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bodrito_2025_CVPR,
    author    = {Bodrito, Th\'eo and Flasseur, Olivier and Mairal, Julien and Ponce, Jean and Langlois, Maud and Lagrange, Anne-Marie},
    title     = {A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1230-1240}
}

raw_affiliation:
[{"affiliation": "D\u00b4epartement d\u2019Informatique de l\u2019 \u00b4Ecole normale sup\u00b4erieure (ENS-PSL, CNRS, Inria)", "city": "Paris", "country": "France"}, {"affiliation": "Universite Claude Bernard Lyon 1, Centre de Recherche Astrophysique de Lyon UMR 5574, ENS de Lyon, CNRS, Villeurbanne, F-69622, France", "city": "Villeurbanne", "country": "France"}, {"affiliation": "Universit\u00b4e Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK", "city": "Grenoble", "country": "France"}, {"affiliation": "Courant Institute and Center for Data Science, New York University", "city": "New York", "country": "USA"}, {"affiliation": "Laboratoire d\u2019 \u00b4Etudes Spatiales et d\u2019Instrumentation en Astrophysique, Observatoire de Paris, Universit\u00b4e PSL, Sorbonne Universit\u00b4e, Universit\u00b4e Paris Diderot", "city": "Paris", "country": "France"}, {"affiliation": "Universit\u00b4e Grenoble Alpes, Institut de Plan\u00b4etologie et d\u2019Astrophysique de Grenoble", "city": "Grenoble", "country": "France"}]

affiliations:
["D\u00b4epartement d\u2019Informatique de l\u2019 \u00b4Ecole normale sup\u00b4erieure (ENS-PSL, CNRS, Inria)", "Universite Claude Bernard Lyon 1, Centre de Recherche Astrophysique de Lyon UMR 5574, ENS de Lyon, CNRS, Villeurbanne, F-69622, France", "Universit\u00b4e Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK", "Courant Institute and Center for Data Science, New York University", "Laboratoire d\u2019 \u00b4Etudes Spatiales et d\u2019Instrumentation en Astrophysique, Observatoire de Paris, Universit\u00b4e PSL, Sorbonne Universit\u00b4e, Universit\u00b4e Paris Diderot", "Universit\u00b4e Grenoble Alpes, Institut de Plan\u00b4etologie et d\u2019Astrophysique de Grenoble"]


title: Scene-agnostic Pose Regression for Visual Localization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27092-27102
affiliations: ["Karlsruhe Institute of Technology", "ETH Zurich", "Hunan University", "MIT-IBM Watson AI Lab"]
cities: ["Karlsruhe", "Zurich", "Changsha", "Cambridge"]
countries: ["Germany", "Switzerland", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Scene-agnostic_Pose_Regression_for_Visual_Localization_CVPR_2025_paper.html

authors:
Junwei Zheng, Ruiping Liu, Yufan Chen, Zhenfang Chen, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen

abstract:
Absolute Pose Regression (APR) predicts 6D camera poses but lacks the adaptability to unknown environments without retraining, while Relative Pose Regression (RPR) generalizes better yet requires a large image retrieval database. Visual Odometry (VO) generalizes well in unseen environments but suffers from accumulated error in open trajectories. To address this dilemma, we introduce a new task, Scene-agnostic Pose Regression (SPR), which can achieve accurate pose regression in a flexible way while eliminating the need for retraining or databases. To benchmark SPR, we created a large-scale dataset, 360SPR, with over 200K photorealistic panoramas, 3.6M pinhole images and camera poses in 270 scenes at three different sensor heights. Furthermore, a SPR-Mamba model is initially proposed to address SPR in a dual-branch manner. Extensive experiments and studies demonstrate the effectiveness of our SPR paradigm, dataset, and model. In the unknown scenes of both 360SPR and 360Loc datasets, our method consistently outperforms APR, RPR and VO. The dataset and code are available at SPR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Scene-agnostic_Pose_Regression_for_Visual_Localization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Scene-agnostic_Pose_Regression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Junwei and Liu, Ruiping and Chen, Yufan and Chen, Zhenfang and Yang, Kailun and Zhang, Jiaming and Stiefelhagen, Rainer},
    title     = {Scene-agnostic Pose Regression for Visual Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27092-27102}
}

raw_affiliation:
[{"affiliation": "Karlsruhe Institute of Technology", "city": "Karlsruhe", "country": "Germany"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}]


title: Learning to Filter Outlier Edges in Global SfM
authors: Nicole Damblon, Marc Pollefeys, Daniel Barath
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11558-11568
affiliations: ["ETH Zurich", "Microsoft", "HUN-REN SZTAKI"]
cities: ["Zurich", "Redmond", "Budapest"]
countries: ["Switzerland", "USA", "Hungary"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Damblon_Learning_to_Filter_Outlier_Edges_in_Global_SfM_CVPR_2025_paper.html

abstract:
We present a novel approach to enhance camera pose estimation in global Structure-from-Motion (SfM) frameworks by filtering inaccurate pose graph edges - representing relative translation estimates - before applying translation averaging. In SfM, pose graph vertices represent images, and edges represent relative poses (rotations and translations) between cameras. We reformulate the edge filtering problem as a vertex filtering in the dual graph, specifically, a line graph where vertices correspond to edges in the original graph and edges correspond to cameras. Utilizing this representation, we frame the problem as a binary classification over nodes in the dual graph. To identify outlier edges, we employ a Transformer-based architecture. To overcome the challenge of memory overflow caused by converting to a line graph, we introduce a clustering-based graph processing approach, enabling our method to be applied to arbitrarily large pose graphs. Our method outperforms existing relative translation filtering techniques in terms of camera position accuracy and can be seamlessly integrated with other filters. The code is available at https://github.com/DmblnNicole/LFOE-GlobalSfM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Damblon_Learning_to_Filter_Outlier_Edges_in_Global_SfM_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Damblon_Learning_to_Filter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Damblon_2025_CVPR,
    author    = {Damblon, Nicole and Pollefeys, Marc and Barath, Daniel},
    title     = {Learning to Filter Outlier Edges in Global SfM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11558-11568}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "HUN-REN SZTAKI", "city": "Budapest", "country": "Hungary"}]


title: Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification
authors: Gaozheng Pei, Shaojie Lyu, Gong Chen, Ke Ma, Qianqian Xu, Yingfei Sun, Qingming Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29268-29277
cities: ["Beijing", "Shenzhen", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pei_Divide_and_Conquer_Heterogeneous_Noise_Integration_for_Diffusion-based_Adversarial_Purification_CVPR_2025_paper.html

abstract:
Existing diffusion-based purification methods aim to disrupt adversarial perturbations by introducing a certain amount of noise through a forward diffusion process, followed by a reverse process to recover clean examples. However, this approach is fundamentally flawed: the uniform operation of the forward process across all pixels compromises normal pixels while attempting to combat adversarial perturbations, resulting in the target model producing incorrect predictions. Simply relying on low-intensity noise is insufficient for effective defense. To address this critical issue, we implement a heterogeneous purification strategy grounded in the interpretability of neural networks. Our method decisively applies higher-intensity noise to specific pixels that the target model focuses on while the remaining pixels are subjected to only low-intensity noise. This requirement motivates us to redesign the sampling process of the diffusion model, allowing for the effective removal of varying noise levels. Furthermore, to evaluate our method against strong adaptative attack, our proposed method sharply reduces time cost and memory usage through a single-step resampling. The empirical evidence from extensive experiments across three datasets demonstrates that our method outperforms most current adversarial training and purification techniques by a substantial margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pei_Divide_and_Conquer_Heterogeneous_Noise_Integration_for_Diffusion-based_Adversarial_Purification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Pei_2025_CVPR,
    author    = {Pei, Gaozheng and Lyu, Shaojie and Chen, Gong and Ma, Ke and Xu, Qianqian and Sun, Yingfei and Huang, Qingming},
    title     = {Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29268-29277}
}

raw_affiliation:
[{"affiliation": "School of Electronic, Electrical and Communication Engineering, UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent Corporate", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Technology, UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "Key Laboratory of Big Data Mining and Knowledge Management, UCAS", "city": "Beijing", "country": "China"}]

affiliations:
["School of Electronic, Electrical and Communication Engineering, UCAS", "Tencent Corporate", "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, CAS", "School of Computer Science and Technology, UCAS", "Key Laboratory of Big Data Mining and Knowledge Management, UCAS"]


title: SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
authors: Ye Liu, Meng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25643-25656
cities: ["Guangzhou", "Xi'an"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SEC-PromptSEmantic_Complementary_Prompting_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
Few-shot class-incremental learning (FSCIL) presents a significant challenge in machine learning, requiring models to integrate new classes from limited examples while preserving performance on previously learned classes. Recently, prompt-based CIL approaches leverage ample data to train prompts, effectively mitigating catastrophic forgetting. However, these methods do not account for the semantic features embedded in prompts, exacerbating the plasticity-stability dilemma in few-shot incremental learning. In this paper, we propose a novel and simple framework named SEmantic Complementary Prompt(SEC-Prompt), which learns two sets of semantically complementary prompts based on an adaptive query: discriminative prompts(D-Prompt) and non-discriminative prompts(ND-Prompt). D-Prompt enhances the separation of class-specific feature distributions by strengthening key discriminative features, while ND-Prompt balances non-discriminative information to promote generalization to novel classes. To efficiently learn high-quality knowledge from limited samples, we leverage ND-Prompt for data augmentation to increase sample diversity and introduce Prompt Clustering Loss to prevent noise contamination in D-Prompt, ensuring robust discriminative feature learning and improved generalization. Our experimental results showcase state-of-the-art performance across three benchmark datasets, including CIFAR100, ImageNet-R and CUB datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SEC-PromptSEmantic_Complementary_Prompting_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SEC-PromptSEmantic_Complementary_Prompting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Ye and Yang, Meng},
    title     = {SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25643-25656}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Y at-Sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "State Key Laboratory of Integrated Services Networks, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Y at-Sen University", "State Key Laboratory of Integrated Services Networks, Xidian University"]


title: LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
authors: Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, Qingshan Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27368-27379
cities: ["Nanjing", "Singapore", "Shanghai", "Nanjing", "Singapore"]
countries: ["China", "Singapore", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_LiMoE_Mixture_of_LiDAR_Representation_Learners_from_Automotive_Scenes_CVPR_2025_paper.html

abstract:
LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focus on sparse voxel representation, overlooking the complementary attributes provided by other LiDAR representations. In this work, we propose LiMoE, a framework that integrates the Mixture of Experts (MoE) paradigm into LiDAR data representation learning to synergistically combine multiple representations, such as range images, sparse voxels, and raw points. Our approach consists of three stages: i) Image-to-LiDAR Pretraining, which transfers prior knowledge from images to point clouds across different representations; ii) Contrastive Mixture Learning (CML), which uses MoE to adaptively activate relevant attributes from each representation and distills these mixed features into a unified 3D network; iii) Semantic Mixture Supervision (SMS), which combines semantic logits from multiple representations to boost downstream segmentation performance. Extensive experiments across eleven large-scale LiDAR datasets demonstrate our effectiveness and superiority. The code has been made publicly accessible.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_LiMoE_Mixture_of_LiDAR_Representation_Learners_from_Automotive_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_LiMoE_Mixture_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Xiang and Kong, Lingdong and Shuai, Hui and Pan, Liang and Liu, Ziwei and Liu, Qingshan},
    title     = {LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27368-27379}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanjing University of Posts and Telecommunications", "city": "Nanjing", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Nanjing University of Aeronautics and Astronautics", "National University of Singapore", "Shanghai AI Laboratory", "Nanjing University of Posts and Telecommunications", "S-Lab, Nanyang Technological University"]


title: CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1702-1713
affiliations: ["NVIDIA", "Stanford University", "MIT"]
cities: ["Santa Clara", "Stanford", "Cambridge"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_CoT-VLA_Visual_Chain-of-Thought_Reasoning_for_Vision-Language-Action_Models_CVPR_2025_paper.html

authors:
Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, Ankur Handa, Tsung-Yi Lin, Gordon Wetzstein, Ming-Yu Liu, Donglai Xiang

abstract:
Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on direct input--output mappings, lacking the intermediate reasoning steps crucial for complex manipulation tasks. As a result, existing VLAs lack temporal planning or reasoning capabilities. In this paper, we introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs) by predicting future image frames autoregressively as visual goals before generating a short action sequence to achieve these goals. We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens. Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks. Videos are available at: https://cot-vla.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_CoT-VLA_Visual_Chain-of-Thought_Reasoning_for_Vision-Language-Action_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_CoT-VLA_Visual_Chain-of-Thought_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Qingqing and Lu, Yao and Kim, Moo Jin and Fu, Zipeng and Zhang, Zhuoyang and Wu, Yecheng and Li, Zhaoshuo and Ma, Qianli and Han, Song and Finn, Chelsea and Handa, Ankur and Lin, Tsung-Yi and Wetzstein, Gordon and Liu, Ming-Yu and Xiang, Donglai},
    title     = {CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1702-1713}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}]


title: WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models
authors: Fu Feng, Yucheng Xie, Jing Wang, Xin Geng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4819-4828
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_WAVE_Weight_Templates_for_Adaptive_Initialization_of_Variable-sized_Models_CVPR_2025_paper.html

abstract:
The growing complexity of model parameters underscores the significance of pre-trained models. However, deployment constraints often necessitate models of varying sizes, exposing limitations in the conventional pre-training and fine-tuning paradigm, particularly when target model sizes are incompatible with pre-trained ones. To address this challenge, we propose WAVE, a novel approach that reformulates variable-sized model initialization from a multi-task perspective, where initializing each model size is treated as a distinct task. WAVE employs shared, size-agnostic weight templates alongside size-specific weight scalers to achieve consistent initialization across various model sizes. These weight templates, constructed within the Learngene framework, integrate knowledge from pre-trained models through a distillation process constrained by Kronecker-based rules. Target models are then initialized by concatenating and weighting these templates, with adaptive connection rules established by lightweight weight scalers, whose parameters are learned from minimal training data. Extensive experiments demonstrate the efficiency of WAVE, achieving state-of-the-art performance in initializing models of various depth and width. The knowledge encapsulated in weight templates is also task-agnostic, allowing for seamless transfer across diverse downstream datasets. Code will be made available at https://github.com/fu-feng/WAVE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_WAVE_Weight_Templates_for_Adaptive_Initialization_of_Variable-sized_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_WAVE_Weight_Templates_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Fu and Xie, Yucheng and Wang, Jing and Geng, Xin},
    title     = {WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4819-4828}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Southeast University, Nanjing, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Southeast University, Nanjing, China", "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China"]


title: Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
authors: Xinjie Cui, Yuezun Li, Ao Luo, Jiaran Zhou, Junyu Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19207-19217
cities: ["Qingdao", "Chengdu"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Forensics_Adapter_Adapting_CLIP_for_Generalizable_Face_Forgery_Detection_CVPR_2025_paper.html

abstract:
We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge. Existing methods treat CLIP merely as a feature extractor, lacking task-specific adaptation, which limits their effectiveness. To address this, we introduce an adapter to learn face forgery traces -- the blending boundaries unique to forged faces, guided by task-specific objectives. Then we enhance the CLIP visual tokens with a dedicated interaction strategy that communicates knowledge across CLIP and the adapter. Since the adapter is alongside CLIP, its versatility is highly retained, naturally ensuring strong generalizability in face forgery detection. With only 5.7M trainable parameters, our method achieves a significant performance boost, improving by approximately 7% on average across five standard datasets. We believe the proposed method can serve as a baseline for future CLIP-based face forgery detection methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_Forensics_Adapter_Adapting_CLIP_for_Generalizable_Face_Forgery_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Xinjie and Li, Yuezun and Luo, Ao and Zhou, Jiaran and Dong, Junyu},
    title     = {Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19207-19217}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Ocean University of China", "city": "Qingdao", "country": "China"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Ocean University of China", "Southwest Jiaotong University"]


title: KAC: Kolmogorov-Arnold Classifier for Continual Learning
authors: Yusong Hu, Zichen Liang, Fei Yang, Qibin Hou, Xialei Liu, Ming-Ming Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15297-15307
affiliations: ["VCIP, CS, Nankai University", "NKIARI, Shenzhen Futian"]
cities: ["Tianjin", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_KAC_Kolmogorov-Arnold_Classifier_for_Continual_Learning_CVPR_2025_paper.html

abstract:
Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a stable classification space while learning new tasks. Inspired by the success of Kolmogorov-Arnold Networks (KAN) in preserving learning stability during simple continual regression tasks, we set out to explore their potential in more complex continual learning scenarios. In this paper, we introduce the Kolmogorov-Arnold Classifier (KAC), a novel classifier developed for continual learning based on the KAN structure. We delve into the impact of KAN's spline functions and introduce Radial Basis Functions (RBF) for improved compatibility with continual learning. We replace linear classifiers with KAC in several recent approaches and conduct experiments across various continual learning benchmarks, all of which demonstrate performance improvements, highlighting the effectiveness and robustness of KAC in continual learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_KAC_Kolmogorov-Arnold_Classifier_for_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_KAC_Kolmogorov-Arnold_Classifier_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Yusong and Liang, Zichen and Yang, Fei and Hou, Qibin and Liu, Xialei and Cheng, Ming-Ming},
    title     = {KAC: Kolmogorov-Arnold Classifier for Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15297-15307}
}

raw_affiliation:
[{"affiliation": "VCIP, CS, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "NKIARI, Shenzhen Futian", "city": "Shenzhen", "country": "China"}]


title: PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing
authors: Ziyu Wu, Yufan Xiong, Mengting Niu, Fangting Xie, Quan Wan, Qijun Ying, Boyan Liu, Xiaohui Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27739-27749
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_PI-HMR_Towards_Robust_In-bed_Temporal_Human_Shape_Reconstruction_with_Contact_CVPR_2025_paper.html

abstract:
Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PI-HMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole tool-chain to support the development of in-bed monitoring with pressure contact sensing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_PI-HMR_Towards_Robust_In-bed_Temporal_Human_Shape_Reconstruction_with_Contact_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_PI-HMR_Towards_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Ziyu and Xiong, Yufan and Niu, Mengting and Xie, Fangting and Wan, Quan and Ying, Qijun and Liu, Boyan and Cai, Xiaohui},
    title     = {PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27739-27749}
}

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


title: BOOTPLACE: Bootstrapped Object Placement with Detection Transformers
authors: Hang Zhou, Xinxin Zuo, Rui Ma, Li Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19294-19303
affiliations: ["University of Alberta", "Concordia University", "Jilin University"]
cities: ["Edmonton", "Montreal", "Changchun"]
countries: ["Canada", "Canada", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_BOOTPLACE_Bootstrapped_Object_Placement_with_Detection_Transformers_CVPR_2025_paper.html

abstract:
In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss have been explored, but their over-relaxed regularization often leads to imprecise object placement. We introduce BOOTPLACE, a novel paradigm that formulates object placement as a placement-by-detection problem. Our approach begins by identifying suitable regions of interest for object placement. This is achieved by training a specialized detection transformer on object-subtracted backgrounds, enhanced with multi-object supervisions. It then semantically associates each target compositing object with detected regions based on their complementary characteristics. Through a boostrapped training approach applied to randomly object-subtracted images, our model enforces meaningful placements through extensive paired data augmentation. Experimental results on established benchmarks demonstrate BOOTPLACE's superior performance in object repositioning, markedly surpassing state-of-the-art baselines on Cityscapes and OPA datasets with notable improvements in IOU scores. Additional ablation studies further showcase the compositionality and generalizability of our approach, supported by user study evaluations. Code is available at https://github.com/RyanHangZhou/BOOTPLACE

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_BOOTPLACE_Bootstrapped_Object_Placement_with_Detection_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_BOOTPLACE_Bootstrapped_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Hang and Zuo, Xinxin and Ma, Rui and Cheng, Li},
    title     = {BOOTPLACE: Bootstrapped Object Placement with Detection Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19294-19303}
}

raw_affiliation:
[{"affiliation": "University of Alberta", "city": "Edmonton", "country": "Canada"}, {"affiliation": "Concordia University", "city": "Montreal", "country": "Canada"}, {"affiliation": "Jilin University", "city": "Changchun", "country": "China"}]


title: CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation
authors: Yuxing Long, Jiyao Zhang, Mingjie Pan, Tianshu Wu, Taewhan Kim, Hao Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22595-22604
affiliations: ["CFCS, School of Computer Science, Peking University", "PKU-Agibot Lab"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Long_CheckManual_A_New_Challenge_and_Benchmark_for_Manual-based_Appliance_Manipulation_CVPR_2025_paper.html

abstract:
Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined by manufacturers. If we want the robot to heat bread by microwave, we should enable them to review the microwave's manual first. From the manual, it can learn about component functions, interaction methods, and representative task steps about appliances. However, previous manual-related works remain limited to question-answering tasks while existing manipulation researchers ignore the manual's important role and fail to comprehend multi-page manuals. In this paper, we propose the first manual-based appliance manipulation benchmark CheckManual. Specifically, we design a large model-assisted human-revised data generation pipeline to create manuals based on CAD appliance models. With these manuals, we establish novel manual-based manipulation challenges, metrics, and simulator environments for model performance evaluation. Furthermore, we propose the first manual-based manipulation planning model ManualPlan to set up a group of baselines for the CheckManual benchmark.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Long_CheckManual_A_New_Challenge_and_Benchmark_for_Manual-based_Appliance_Manipulation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Long_2025_CVPR,
    author    = {Long, Yuxing and Zhang, Jiyao and Pan, Mingjie and Wu, Tianshu and Kim, Taewhan and Dong, Hao},
    title     = {CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22595-22604}
}

raw_affiliation:
[{"affiliation": "CFCS, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "PKU-Agibot Lab", "city": "Beijing", "country": "China"}]


authors: Xiao Wang, Fuling Wang, Yuehang Li, Qingchuan Ma, Shiao Wang, Bo Jiang, Jin Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5123-5133
affiliations: ["School of Computer Science and Technology, Anhui University"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_CXPMRG-Bench_Pre-training_and_Benchmarking_for_X-ray_Medical_Report_Generation_on_CVPR_2025_paper.html

title:
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset

abstract:
X-ray image-based medical report generation (MRG) is a pivotal area in artificial intelligence that can significantly reduce diagnostic burdens and patient wait times. Despite significant progress, we believe that the task has reached a bottleneck due to the limited benchmark datasets and the existing large models' insufficient capability enhancements in this specialized domain. Specifically, the recently released CheXpert Plus dataset lacks comparative evaluation algorithms and their results, providing only the dataset itself. This situation makes the training, evaluation, and comparison of subsequent algorithms challenging. Thus, we conduct a comprehensive benchmarking of existing mainstream X-ray report generation models and large language models (LLMs), on the CheXpert Plus dataset. We believe that the proposed benchmark can provide a solid comparative basis for subsequent algorithms and serve as a guide for researchers to quickly grasp the state-of-the-art models in this field. More importantly, we propose a large model for the X-ray image report generation using a multi-stage pre-training strategy, including self-supervised autoregressive generation and Xray-report contrastive learning, and supervised fine-tuning. Extensive experimental results indicate that the autoregressive pre-training based on Mamba effectively encodes X-ray images, and the image-text contrastive pre-training further aligns the feature spaces, achieving better experimental results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_CXPMRG-Bench_Pre-training_and_Benchmarking_for_X-ray_Medical_Report_Generation_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_CXPMRG-Bench_Pre-training_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xiao and Wang, Fuling and Li, Yuehang and Ma, Qingchuan and Wang, Shiao and Jiang, Bo and Tang, Jin},
    title     = {CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5123-5133}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Anhui University", "city": "Hefei", "country": "China"}]


title: FASTer: Focal token Acquiring-and-Scaling Transformer for Long-term 3D Objection Detection
authors: Chenxu Dang, ZaiPeng Duan, Pei An, Xinmin Zhang, Xuzhong Hu, Jie Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17029-17038
affiliations: ["Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dang_FASTer_Focal_token_Acquiring-and-Scaling_Transformer_for_Long-term_3D_Objection_Detection_CVPR_2025_paper.html

abstract:
Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual points and lead to exponentially increased complexity as the number of input frames grows. Moreover, arbitrary result-level concatenation limits the global information extraction. In this paper, we propose a Focal Token Acquring-and-Scaling Transformer (FASTer), which dynamically selects focal tokens and condenses token sequences in an adaptive and lightweight manner. Emphasizing the contribution of individual tokens, we propose a simple but effective Adaptive Scaling mechanism to capture geometric contexts while sifting out focal points. Adaptively storing and processing only focal points in historical frames dramatically reduces the overall complexity. Furthermore, a novel Grouped Hierarchical Fusion strategy is proposed, progressively performing sequence scaling and Intra-Group Fusion operations to facilitate the exchange of global spatial and temporal information. Experiments on the Waymo Open Dataset demonstrate that our FASTer significantly outperforms other state-of-the-art detectors in both performance and efficiency while also exhibiting improved flexibility and robustness. The code is available at https://github.com/MSunDYY/FASTer.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dang_FASTer_Focal_token_Acquiring-and-Scaling_Transformer_for_Long-term_3D_Objection_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dang_FASTer_Focal_token_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dang_2025_CVPR,
    author    = {Dang, Chenxu and Duan, ZaiPeng and An, Pei and Zhang, Xinmin and Hu, Xuzhong and Ma, Jie},
    title     = {FASTer: Focal token Acquiring-and-Scaling Transformer for Long-term 3D Objection Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17029-17038}
}

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


title: SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25465-25475
cities: ["Beijing", "Beijing", "Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SEEN-DA_SEmantic_ENtropy_guided_Domain-aware_Attention_for_Domain_Adaptive_Object_CVPR_2025_paper.html

authors:
Haochen Li, Rui Zhang, Hantao Yao, Xin Zhang, Yifan Hao, Xinkai Song, Shaohui Peng, Yongwei Zhao, Chen Zhao, Yanjun Wu, Ling Li

abstract:
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. Traditional works focus on aligning visual features between domains to extract domain-invariant knowledge, and recent VLM-based DAOD methods leverage semantic information provided by the textual encoder to supplement domain-specific features for each domain.However, they overlook the role of semantic information in guiding the learning of visual features that are beneficial for adaptation.To solve the problem, we propose semantic entropy to quantify the semantic information contained in visual features, and design SEmantic ENtropy guided Domain-aware Attention (SEEN-DA) to adaptively refine visual features with the semantic information of two domains.Semantic entropy reflects the importance of features based on semantic information, which can serve as attention to select discriminative visual features and suppress semantically irrelevant redundant information.Guided by semantic entropy, we introduce domain-aware attention modules into the visual encoder in SEEN-DA.It utilizes an inter-domain attention branch to extract domain-invariant features and eliminate redundant information, and an intra-domain attention branch to supplement the domain-specific semantic information discriminative on each domain.Comprehensive experiments validate the effectiveness of SEEN-DA, demonstrating significant improvements in cross-domain object detection performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SEEN-DA_SEmantic_ENtropy_guided_Domain-aware_Attention_for_Domain_Adaptive_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SEEN-DA_SEmantic_ENtropy_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Haochen and Zhang, Rui and Yao, Hantao and Zhang, Xin and Hao, Yifan and Song, Xinkai and Peng, Shaohui and Zhao, Yongwei and Zhao, Chen and Wu, Yanjun and Li, Ling},
    title     = {SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25465-25475}
}

raw_affiliation:
[{"affiliation": "Intelligent Software Research Center, Institute of Software, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Lab of Processors, Institute of Computing Technology, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "School of Information Science and Technology, University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Software, CAS", "city": "Beijing", "country": "China"}]

affiliations:
["Intelligent Software Research Center, Institute of Software, CAS", "State Key Lab of Processors, Institute of Computing Technology, CAS", "School of Information Science and Technology, University of Science and Technology of China", "University of Chinese Academy of Sciences", "Institute of Software, CAS"]


title: Event-Equalized Dense Video Captioning
authors: Kangyi Wu, Pengna Li, Jingwen Fu, Yizhe Li, Yang Wu, Yuhan Liu, Jinjun Wang, Sanping Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8417-8427
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Event-Equalized_Dense_Video_Captioning_CVPR_2025_paper.html

abstract:
Dense video captioning aims to localize and caption all events in arbitrary untrimmed videos. Although previous methods have achieved appealing results, they still face the issue of temporal bias, i.e, models tend to focus more on events with certain temporal characteristics. Specifically, 1) the temporal distribution of events in training datasets is uneven. Models trained on these datasets will pay less attention to out-of-distribution events. 2) long-duration events have more frame features than short ones and will attract more attention. To address this, we argue that events, with varying temporal characteristics, should be treated equally when it comes to dense video captioning. Intuitively, different events tend to have distinct visual differences due to varied camera views, backgrounds, or subjects. Inspired by that, we intend to utilize visual features to have an approximate perception of possible events and pay equal attention to them. In this paper, we introduce a simple but effective framework, called Event-Equalized Dense Video Captioning(E^2DVC) to overcome the temporal bias and treat all possible events equally. Specifically, an event perception module(EPM) is proposed to do uneven clustering on visual frame features to generate pseudo-events. We enforce the model's attention to these pseudo-events through the pseudo-event initialization module(PEI). A novel event-enhanced encoder(EEE) is also devised to enhance the model's ability to explore frame-frame and frame-event relationships. Experimental results validate the effectiveness of the proposed methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Event-Equalized_Dense_Video_Captioning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Event-Equalized_Dense_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Kangyi and Li, Pengna and Fu, Jingwen and Li, Yizhe and Wu, Yang and Liu, Yuhan and Wang, Jinjun and Zhou, Sanping},
    title     = {Event-Equalized Dense Video Captioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8417-8427}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University", "city": "Xi'an", "country": "China"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University"]


title: Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11275-11285
affiliations: ["KAIST", "Meta"]
cities: ["Daejeon", "Menlo Park"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ha_Geometry-guided_Online_3D_Video_Synthesis_with_Multi-View_Temporal_Consistency_CVPR_2025_paper.html

authors:
Hyunho Ha, Lei Xiao, Christian Richardt, Thu Nguyen-Phuoc, Changil Kim, Min H. Kim, Douglas Lanman, Numair Khan

abstract:
We introduce a novel geometry-guided online video view synthesis method with enhanced view and temporal consistency. Traditional approaches achieve high-quality synthesis from dense multi-view camera setups but require significant computational resources. In contrast, selective-input methods reduce this cost but often compromise quality, leading to multi-view and temporal inconsistencies such as flickering artifacts. Our method addresses this challenge to deliver efficient, high-quality novel-view synthesis with view and temporal consistency. The key innovation of our approach lies in using global geometry to guide an image-based rendering pipeline. To accomplish this, we progressively refine depth maps using color difference masks across time. These depth maps are then accumulated through truncated signed distance fields in the synthesized view's image space. This depth representation is view and temporally consistent, and is used to guide a pre-trained blending network that fuses multiple forward-rendered input-view images. Thus, the network is encouraged to output geometrically consistent synthesis results across multiple views and time. Our approach achieves consistent, high-quality video synthesis, while running efficiently in an online manner.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ha_Geometry-guided_Online_3D_Video_Synthesis_with_Multi-View_Temporal_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ha_Geometry-guided_Online_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ha_2025_CVPR,
    author    = {Ha, Hyunho and Xiao, Lei and Richardt, Christian and Nguyen-Phuoc, Thu and Kim, Changil and Kim, Min H. and Lanman, Douglas and Khan, Numair},
    title     = {Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11275-11285}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


title: EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation
authors: Daikun Liu, Lei Cheng, Teng Wang, Changyin Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1984-1993
raw_affiliation: [{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}]
affiliations: ["Southeast University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EDCFlow_Exploring_Temporally_Dense_Difference_Maps_for_Event-based_Optical_Flow_CVPR_2025_paper.html

abstract:
Recent learning-based methods for event-based optical flow estimation utilize cost volumes for pixel matching but suffer from redundant computations and limited scalability to higher resolutions for flow refinement. In this work, we take advantage of the complementarity between temporally dense feature differences of adjacent event frames and cost volume and present a lightweight event-based optical flow network (EDCFlow) to achieve high-quality flow estimation at a higher resolution. Specifically, an attention-based multi-scale temporal feature difference layer is developed to capture diverse motion patterns at high resolution in a computation-efficient manner. An adaptive fusion of high-resolution difference motion features and low-resolution correlation motion features is performed to enhance motion representation and model generalization. Notably, EDCFlow can serve as a plug-and-play refinement module for RAFT-like event-based methods to enhance flow details. Extensive experiments demonstrate that EDCFlow achieves better performance with lower complexity compared to existing methods, offering superior generalization. Codes and models will be available at here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EDCFlow_Exploring_Temporally_Dense_Difference_Maps_for_Event-based_Optical_Flow_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_EDCFlow_Exploring_Temporally_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Daikun and Cheng, Lei and Wang, Teng and Sun, Changyin},
    title     = {EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1984-1993}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19283-19293
cities: ["Nanjing", "Beijing", "Wuhan", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Point2RBox-v2_Rethinking_Point-supervised_Oriented_Object_Detection_with_Spatial_Layout_Among_CVPR_2025_paper.html

title:
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances

authors:
Yi Yu, Botao Ren, Peiyuan Zhang, Mingxin Liu, Junwei Luo, Shaofeng Zhang, Feipeng Da, Junchi Yan, Xue Yang

abstract:
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained great attention. In this paper, we rethink this challenging task setting with the layout among instances and present Point2RBox-v2. At the core are three principles: 1) Gaussian overlap loss. It learns an upper bound for each instance by treating objects as 2D Gaussian distributions and minimizing their overlap. 2) Voronoi watershed loss. It learns a lower bound for each instance through watershed on Voronoi tessellation. 3) Consistency loss. It learns the size/rotation variation between two output sets with respect to an input image and its augmented view. Supplemented by a few devised techniques, e.g. edge loss and copy-paste, the detector is further enhanced. To our best knowledge, Point2RBox-v2 is the first approach to explore the spatial layout among instances for learning point-supervised OOD. Our solution is elegant and lightweight, yet it is expected to give a competitive performance especially in densely packed scenes: 62.61%/86.15%/34.71% on DOTA/HRSC/FAIR1M.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Point2RBox-v2_Rethinking_Point-supervised_Oriented_Object_Detection_with_Spatial_Layout_Among_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Yi and Ren, Botao and Zhang, Peiyuan and Liu, Mingxin and Luo, Junwei and Zhang, Shaofeng and Da, Feipeng and Yan, Junchi and Yang, Xue},
    title     = {Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19283-19293}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Department of Automation & CSE & SAI, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Southeast University", "Tsinghua University", "Wuhan University", "Department of Automation & CSE & SAI, Shanghai Jiao Tong University"]


title: LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions
authors: Faridoun Mehri, Mahdieh Soleymani Baghshah, Mohammad Taher Pilehvar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 67-78
affiliations: ["Sharif University of Technology, Iran", "Cardiff University, UK"]
cities: ["Tehran", "Cardiff"]
countries: ["Iran", "UK"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mehri_LibraGrad_Balancing_Gradient_Flow_for_Universally_Better_Vision_Transformer_Attributions_CVPR_2025_paper.html

abstract:
Why do gradient-based explanations struggle with Transformers, and how can we improve them? We identify gradient flow imbalances in Transformers that violate FullGrad-completeness, a critical property for attribution faithfulness that CNNs naturally possess. To address this issue, we introduce LibraGrad--a theoretically grounded post-hoc approach that corrects gradient imbalances through pruning and scaling of backward paths, without changing the forward pass or adding computational overhead. We evaluate LibraGrad using three metric families: Faithfulness, which quantifies prediction changes under perturbations of the most and least relevant features; Completeness Error, which measures attribution conservation relative to model outputs; and Segmentation AP, which assesses alignment with human perception. Extensive experiments across 8 architectures, 4 model sizes, and 5 datasets show that LibraGrad universally enhances gradient-based methods, outperforming existing white-box methods--including Transformer-specific approaches--across all metrics. We demonstrate superior qualitative results through two complementary evaluations: precise text-prompted region highlighting on CLIP models and accurate class discrimination between co-occurring animals on ImageNet-finetuned models--two settings on which existing methods often struggle. LibraGrad is effective even on the attention-free MLP-Mixer architecture, indicating potential for extension to other modern architectures. Our code is freely available at https://nightmachinery.github.io/LibraGrad/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mehri_LibraGrad_Balancing_Gradient_Flow_for_Universally_Better_Vision_Transformer_Attributions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mehri_LibraGrad_Balancing_Gradient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mehri_2025_CVPR,
    author    = {Mehri, Faridoun and Baghshah, Mahdieh Soleymani and Pilehvar, Mohammad Taher},
    title     = {LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {67-78}
}

raw_affiliation:
[{"affiliation": "Sharif University of Technology, Iran", "city": "Tehran", "country": "Iran"}, {"affiliation": "Cardiff University, UK", "city": "Cardiff", "country": "UK"}]


title: Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model
authors: Shuyun Wang, Hu Zhang, Xin Shen, Dadong Wang, Xin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22975-22984
affiliations: ["The University of Queensland", "Data61, CSIRO"]
cities: ["Brisbane", "Sydney"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Blind_Bitstream-corrupted_Video_Recovery_via_Metadata-guided_Diffusion_Model_CVPR_2025_paper.html

abstract:
Bitstream-corrupted video recovery aims to fill in realistic video content due to bitstream corruption during video storage or transmission. Most existing methods typically assume that the predefined masks of the corrupted regions are known in advance. However, manually annotating these masks is laborious and time-consuming, limiting the applicability of existing methods in real-world scenarios. Therefore, we expect to relax this assumption by defining a new blind video recovery setting where the recovery of corrupted regions does not rely on predefined masks. There are two significant challenges in this setting: (i) without predefined masks, how accurately can a model identify the regions requiring recovery? (ii) how to recover contents from extensive and irregular regions, especially when large portions of frames are severely degraded? To address these challenges, we introduce a Metadata-Guided Diffusion Model, dubbed M-GDM. To enable a diffusion model focusing on the corrupted regions, we leverage intrinsic video metadata as a corruption indicator and design a dual-stream metadata encoder. This encoder first embeds the motion vectors and frame types of a video separately and then merges them into a unified metadata representation. The metadata representation will interact with the corrupted latent feature through cross-attention mechanisms at each diffusion step. Meanwhile, to preserve the intact regions, we propose a prior-driven mask predictor that generates pseudo masks for the corrupted regions by leveraging the metadata prior and diffusion prior. These pseudo masks enable the separation and recombination of intact and recovered regions through hard masking. However, imperfections in pseudo mask predictions and hard masking processes often result in boundary artifacts. Thus, we introduce a post-refinement module that refines the hard-masked outputs, enhancing the consistency between intact and recovered regions. Extensive experiment results validate the effectiveness of our method and demonstrate its superiority in the blind video recovery task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Blind_Bitstream-corrupted_Video_Recovery_via_Metadata-guided_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Blind_Bitstream-corrupted_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shuyun and Zhang, Hu and Shen, Xin and Wang, Dadong and Yu, Xin},
    title     = {Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22975-22984}
}

raw_affiliation:
[{"affiliation": "The University of Queensland", "city": "Brisbane", "country": "Australia"}, {"affiliation": "Data61, CSIRO", "city": "Sydney", "country": "Australia"}]


title: Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
authors: Junxi Chen, Junhao Dong, Xiaohua Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23785-23794
cities: ["Guangzhou", "Singapore", "Guangzhou"]
countries: ["China", "Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Mind_the_Trojan_Horse_Image_Prompt_Adapter_Enabling_Scalable_and_CVPR_2025_paper.html

abstract:
Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability. However, in this paper, we reveal that T2I-DMs equipped with the IP-Adapter (T2I-IP-DMs) enable a new jailbreak attack named the hijacking attack. We demonstrate that, by uploading imperceptible image-space adversarial examples (AEs), the adversary can hijack massive benign users to jailbreak an Image Generation Service (IGS) driven by T2I-IP-DMs and mislead the public to discredit the service provider. Worse still, the IP-Adapter's dependency on open-source image encoders reduces the knowledge required to craft AEs. Extensive experiments verify the technical feasibility of the hijacking attack. In light of the revealed threat, we investigate several existing defenses and explore combining the IP-Adapter with adversarially trained models to overcome existing defenses' limitations. Our code is available at https://github.com/fhdnskfbeuv/attackIPA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Mind_the_Trojan_Horse_Image_Prompt_Adapter_Enabling_Scalable_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Mind_the_Trojan_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Junxi and Dong, Junhao and Xie, Xiaohua},
    title     = {Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23785-23794}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-Sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Guangdong Province Key Laboratory of Information Security Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-Sen University", "Nanyang Technological University", "Guangdong Province Key Laboratory of Information Security Technology"]


title: Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues
authors: Youngjoon Jang, Haran Raajesh, Liliane Momeni, Gül Varol, Andrew Zisserman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8742-8752
cities: ["Oxford", "Daejeon", "Paris", "Hyderabad"]
countries: ["UK", "Republic of Korea", "France", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Lost_in_Translation_Found_in_Context_Sign_Language_Translation_with_CVPR_2025_paper.html

abstract:
Our objective is to translate continuous sign language into spoken language text. Inspired by the way human interpreters rely on context for accurate translation, we incorporate additional contextual cues together with the signing video, into a new translation framework. Specifically, besides visual sign recognition features that encode the input video, we integrate complementary textual information from (i) captions describing the background show, (ii) translation of previous sentences, as well as (iii) pseudo-glosses transcribing the signing. These are automatically extracted and inputted along with the visual features to a pre-trained large language model (LLM), which we fine-tune to generate spoken language translations in text form. Through extensive ablation studies, we show the positive contribution of each input cue to the translation performance. We train and evaluate our approach on BOBSL -- the largest British Sign Language dataset currently available. We show that our contextual approach significantly enhances the quality of the translations compared to previously reported results on BOBSL, and also to state-of-the-art methods that we implement as baselines. Furthermore, we demonstrate the generality of our approach by applying it also to How2Sign, an American Sign Language dataset, and achieve competitive results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Lost_in_Translation_Found_in_Context_Sign_Language_Translation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Lost_in_Translation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Youngjoon and Raajesh, Haran and Momeni, Liliane and Varol, G\"ul and Zisserman, Andrew},
    title     = {Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8742-8752}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group, University of Oxford, UK", "city": "Oxford", "country": "UK"}, {"affiliation": "KAIST, Daejeon, Republic of Korea", "city": "Daejeon", "country": "Republic of Korea"}, {"affiliation": "LIGM, \u00b4Ecole des Ponts, IP Paris, Univ Gustave Eiffel, CNRS, France", "city": "Paris", "country": "France"}, {"affiliation": "CVIT, IIIT Hyderabad, India", "city": "Hyderabad", "country": "India"}]

affiliations:
["Visual Geometry Group, University of Oxford, UK", "KAIST, Daejeon, Republic of Korea", "LIGM, \u00b4Ecole des Ponts, IP Paris, Univ Gustave Eiffel, CNRS, France", "CVIT, IIIT Hyderabad, India"]


title: CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16101-16110
affiliations: ["School of Electrical and Electronic Engineering, Yonsei University", "NA VER Cloud"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_CoCoGaussian_Leveraging_Circle_of_Confusion_for_Gaussian_Splatting_from_Defocused_CVPR_2025_paper.html

authors:
Jungho Lee, Suhwan Cho, Taeoh Kim, Ho-Deok Jang, Minhyeok Lee, Geonho Cha, Dongyoon Wee, Dogyoon Lee, Sangyoun Lee

abstract:
3D Gaussian Splatting (3DGS) has attracted significant attention for its high-quality novel view rendering, inspiring research to address real-world challenges. While conventional methods depend on sharp images for accurate scene reconstruction, real-world scenarios are often affected by defocus blur due to finite depth of field, making it essential to account for realistic 3D scene representation. In this study, we propose CoCoGaussian, a Circle of Confusion-aware Gaussian Splatting that enables precise 3D scene representation using only defocused images. CoCoGaussian addresses the challenge of defocus blur by modeling the Circle of Confusion (CoC) through a physically grounded approach based on the principles of photographic defocus. Exploiting 3D Gaussians, we compute the CoC diameter from depth and learnable aperture information, generating multiple Gaussians to precisely capture the CoC shape. Furthermore, we introduce a learnable scaling factor to enhance robustness and provide more flexibility in handling unreliable depth in scenes with reflective or refractive surfaces. Experiments on both synthetic and real-world datasets demonstrate that CoCoGaussian achieves state-of-the-art performance across multiple benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_CoCoGaussian_Leveraging_Circle_of_Confusion_for_Gaussian_Splatting_from_Defocused_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_CoCoGaussian_Leveraging_Circle_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jungho and Cho, Suhwan and Kim, Taeoh and Jang, Ho-Deok and Lee, Minhyeok and Cha, Geonho and Wee, Dongyoon and Lee, Dogyoon and Lee, Sangyoun},
    title     = {CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16101-16110}
}

raw_affiliation:
[{"affiliation": "School of Electrical and Electronic Engineering, Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "NA VER Cloud", "city": "Seoul", "country": "South Korea"}]


title: Semantic and Sequential Alignment for Referring Video Object Segmentation
authors: Feiyu Pan, Hao Fang, Fangkai Li, Yanyu Xu, Yawei Li, Luca Benini, Xiankai Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19067-19076
affiliations: ["Shandong University", "ETH Z\u00a8urich", "University of Bologna"]
cities: ["Jinan", "Z\u00fcrich", "Bologna"]
countries: ["China", "Switzerland", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_Semantic_and_Sequential_Alignment_for_Referring_Video_Object_Segmentation_CVPR_2025_paper.html

abstract:
Referring video object segmentation (RVOS) seeks to segment the objects within a video referred by linguistic expressions. Existing RVOS solutions follow a "fuse then select" paradigm: establishing semantic correlation between visual and linguistic feature, and performing frame-level query interaction to select the instance mask per frame with instance segmentation module. This paradigm overlooks the challenge of semantic gap between the linguistic descriptor and the video object as well as the underlying clutters in the video. This paper proposes a novel Semantic and Sequential Alignment (SSA) paradigm to handle these challenges. We first insert a lightweight adapter after the vision language model (VLM) to perform the semantic alignment. Then, prior to selecting mask per frame, we exploit the trajectory-to-instance enhancement for each frame via sequential alignment. This paradigm leverages the visual-language alignment inherent in VLM during adaptation and tries to capture global information by ensembling trajectories. This helps understand videos and the corresponding descriptors by mitigating the discrepancy with intricate activity semantics, particularly when facing occlusion or similar interference. SSA demonstrates competitive performance while maintaining fewer learnable parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_Semantic_and_Sequential_Alignment_for_Referring_Video_Object_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_Semantic_and_Sequential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Feiyu and Fang, Hao and Li, Fangkai and Xu, Yanyu and Li, Yawei and Benini, Luca and Lu, Xiankai},
    title     = {Semantic and Sequential Alignment for Referring Video Object Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19067-19076}
}

raw_affiliation:
[{"affiliation": "Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "ETH Z\u00a8urich", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "University of Bologna", "city": "Bologna", "country": "Italy"}]


title: Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition
authors: Juncheng Wang, Chao Xu, Cheng Yu, Lei Shang, Zhe Hu, Shujun Wang, Liefeng Bo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3111-3120
affiliations: ["The Hong Kong Polytechnic University", "Tongyi Lab, Alibaba Group"]
cities: ["Hong Kong", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Synchronized_Video-to-Audio_Generation_via_Mel_Quantization-Continuum_Decomposition_CVPR_2025_paper.html

abstract:
Video-to-audio generation is essential for synthesizing realistic audio tracks that synchronize effectively with silent videos.Following the perspective of extracting essential signals from videos that can precisely control the mature text-to-audio generative diffusion models, this paper presents how to balance the representation of mel-spectrograms in terms of completeness and complexity through a new approach called Mel Quantization-Continuum Decomposition (Mel-QCD).We decompose the mel-spectrogram into three distinct types of signals, employing quantization or continuity to them, we can effectively predict them from video by a devised video-to-all (V2X) predictor.Then, the predicted signals are recomposed and fed into a ControlNet, along with a textual inversion design, to control the audio generation process.Our proposed Mel-QCD method demonstrates state-of-the-art performance across eight metrics, evaluating dimensions such as quality, synchronization, and semantic consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Synchronized_Video-to-Audio_Generation_via_Mel_Quantization-Continuum_Decomposition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Synchronized_Video-to-Audio_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Juncheng and Xu, Chao and Yu, Cheng and Shang, Lei and Hu, Zhe and Wang, Shujun and Bo, Liefeng},
    title     = {Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3111-3120}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}]


title: Continual SFT Matches Multimodal RLHF with Negative Supervision
authors: Ke Zhu, Yu Wang, Yanpeng Sun, Qiang Chen, Jiangjiang Liu, Gang Zhang, Jingdong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14615-14624
affiliations: ["Nanjing University", "Baidu", "Nanjing University of Science and Technology"]
cities: ["Nanjing", "Beijing", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Continual_SFT_Matches_Multimodal_RLHF_with_Negative_Supervision_CVPR_2025_paper.html

abstract:
Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiority over continual SFT during this preference alignment stage. In this paper, we observe that the inherent value of multimodal RLHF lies in its negative supervision, the logit of the rejected responses. We thus propose a novel negative supervised finetuning (nSFT) approach that fully excavates these information resided. Our nSFT disentangles this negative supervision in RLHF paradigm, and continually aligns VLMs with a simple SFT loss. This is more memory efficient than multimodal RLHF where 2 (e.g., DPO) or 4 (e.g., PPO) large VLMs are strictly required. The effectiveness of nSFT is rigorously proved by comparing it with various multimodal RLHF approaches, across different dataset sources, base VLMs and evaluation metrics. Besides, fruitful of ablations are provided to support our hypothesis. Code will be found in https://github.com/Kevinz-code/nSFT/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Continual_SFT_Matches_Multimodal_RLHF_with_Negative_Supervision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Continual_SFT_Matches_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Ke and Wang, Yu and Sun, Yanpeng and Chen, Qiang and Liu, Jiangjiang and Zhang, Gang and Wang, Jingdong},
    title     = {Continual SFT Matches Multimodal RLHF with Negative Supervision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14615-14624}
}

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


title: Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition
authors: Anqi Zhu, Jingmin Zhu, James Bailey, Mingming Gong, Qiuhong Ke
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13876-13885
affiliations: ["The University of Melbourne", "Monash University"]
cities: ["Parkville", "Clayton"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Semantic-guided_Cross-Modal_Prompt_Learning_for_Skeleton-based_Zero-shot_Action_Recognition_CVPR_2025_paper.html

abstract:
Skeleton-based human action recognition is promising due to its privacy preservation, robustness to visual challenges, and computational efficiency. Especially, the practical necessity to recognize unseen actions has led to increased interest in zero-shot skeleton-based action recognition (ZSSAR). Existing ZSSAR approaches often rely on manually crafted action descriptions or visual assumptions to enhance knowledge transfer, which is limited in flexibility and prone to inaccuracies and noise. To overcome this, we introduce Semantic-guided Cross-Modal Prompt Learning (SCoPLe), a novel framework that replaces manual guidance with data-driven  prompt learning for refinement and alignment of skeletal and textual features. Specifically, we introduce a dual-stream language prompting module that preserves the original semantic context from the pre-trained text encoder while still effectively tuning its ouput for ZSSAR task adaptation. We also introduce a joint-shaped prompting module that learns tuning for skeleton features and incorporate an adaptive visual representation sampler that leverages text semantics to strengthen the cross-modal prompting interactions during skeleton-to-text embedding projection. Experimental results on the NTU-RGB+D and PKU-MMD datasets demonstrate the state-of-the-art performance of our method in both ZSSAR and generalized ZSSAR scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Semantic-guided_Cross-Modal_Prompt_Learning_for_Skeleton-based_Zero-shot_Action_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Semantic-guided_Cross-Modal_Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Anqi and Zhu, Jingmin and Bailey, James and Gong, Mingming and Ke, Qiuhong},
    title     = {Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13876-13885}
}

raw_affiliation:
[{"affiliation": "The University of Melbourne", "city": "Parkville", "country": "Australia"}, {"affiliation": "Monash University", "city": "Clayton", "country": "Australia"}]


title: FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video
authors: Jiawei Zhang, Zijian Wu, Zhiyang Liang, Yicheng Gong, Dongfang Hu, Yao Yao, Xun Cao, Hao Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5535-5545
affiliations: ["Nanjing University", "OPPO"]
cities: ["Nanjing", "Dongguan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_FATE_Full-head_Gaussian_Avatar_with_Textural_Editing_from_Monocular_Video_CVPR_2025_paper.html

abstract:
Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient Gaussian representation. To address these challenges, we introduce FATE -- a novel method for reconstructing an editable full-head avatar from a single monocular video. FATE integrates a sampling-based densification strategy to ensure optimal positional distribution of points, improving rendering efficiency.  A neural baking technique is introduced to convert discrete Gaussian representations into continuous attribute maps, facilitating intuitive appearance editing.  Furthermore, we propose a universal completion framework to recover non-frontal appearance, culminating in a 360^\circ-renderable 3D head avatar. FATE outperforms previous approaches in both qualitative and quantitative evaluations, achieving state-of-the-art performance. To the best of our knowledge, FATE is the first animatable and 360^\circ full-head monocular reconstruction method for a 3D head avatar. Project page and code are available at this \href https://zjwfufu.github.io/FATE-page/  link .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_FATE_Full-head_Gaussian_Avatar_with_Textural_Editing_from_Monocular_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_FATE_Full-head_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jiawei and Wu, Zijian and Liang, Zhiyang and Gong, Yicheng and Hu, Dongfang and Yao, Yao and Cao, Xun and Zhu, Hao},
    title     = {FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5535-5545}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "OPPO", "city": "Dongguan", "country": "China"}]


title: ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
authors: Chengyou Jia, Changliang Xia, Zhuohang Dang, Weijia Wu, Hangwei Qian, Minnan Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13284-13293
cities: ["Xi'an", "Singapore", "Singapore"]
countries: ["China", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_ChatGen_Automatic_Text-to-Image_Generation_From_FreeStyle_Chatting_CVPR_2025_paper.html

abstract:
Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific arguments, making users resort to labor-intensive attempts for desired images. This paper proposes Automatic T2I generation, which aims to automate these tedious steps, allowing users to simply describe their needs in a freestyle chatting way. To systematically study this problem, we first introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It features high-quality paired data with diverse freestyle inputs, enabling comprehensive evaluation of automatic T2I models across all steps. Additionally, recognizing Automatic T2I as a complex multi-step reasoning task, we propose ChatGen-Evo, a multi-stage evolution strategy that progressively equips models with essential automation skills. Through extensive evaluation across step-wise accuracy and image quality, ChatGen-Evo significantly enhances performance over various baselines. Our evaluation also uncovers valuable insights for advancing automatic T2I. All our data, code and models will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_ChatGen_Automatic_Text-to-Image_Generation_From_FreeStyle_Chatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_ChatGen_Automatic_Text-to-Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Chengyou and Xia, Changliang and Dang, Zhuohang and Wu, Weijia and Qian, Hangwei and Luo, Minnan},
    title     = {ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13284-13293}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, MOEKLINNS Lab, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CFAR, A*STAR", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Computer Science and Technology, MOEKLINNS Lab, Xi\u2019an Jiaotong University", "National University of Singapore", "CFAR, A*STAR"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22404-22415
cities: ["Lausanne", "Bern", "Zurich", "Zurich", "Zurich"]
countries: ["Switzerland", "Switzerland", "Switzerland", "Switzerland", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hassan_GEM_A_Generalizable_Ego-Vision_Multimodal_World_Model_for_Fine-Grained_Ego-Motion_CVPR_2025_paper.html

title:
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control

authors:
Mariam Hassan, Sebastian Stapf, Ahmad Rahimi, Pedro M B Rezende, Yasaman Haghighi, David Brüggemann, Isinsu Katircioglu, Lin Zhang, Xiaoran Chen, Suman Saha, Marco Cannici, Elie Aljalbout, Botao Ye, Xi Wang, Aram Davtyan, Mathieu Salzmann, Davide Scaramuzza, Marc Pollefeys, Paolo Favaro, Alexandre Alahi

abstract:
We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer spatial understanding. We introduce autoregressive noise schedules to enable stable long-horizon generations. Our dataset is comprised of 4000+ hours of multimodal data across domains like autonomous driving, egocentric human activities, and drone flights. Pseudo-labels are used to get depth maps, ego-trajectories, and human poses. We use a comprehensive evaluation framework, including a new Control of Object Manipulation (COM) metric, to assess controllability. Experiments show GEM excels at generating diverse, controllable scenarios and temporal consistency over long generations. Code, models, and datasets are fully open-sourced.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hassan_GEM_A_Generalizable_Ego-Vision_Multimodal_World_Model_for_Fine-Grained_Ego-Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hassan_GEM_A_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hassan_2025_CVPR,
    author    = {Hassan, Mariam and Stapf, Sebastian and Rahimi, Ahmad and Rezende, Pedro M B and Haghighi, Yasaman and Br\"uggemann, David and Katircioglu, Isinsu and Zhang, Lin and Chen, Xiaoran and Saha, Suman and Cannici, Marco and Aljalbout, Elie and Ye, Botao and Wang, Xi and Davtyan, Aram and Salzmann, Mathieu and Scaramuzza, Davide and Pollefeys, Marc and Favaro, Paolo and Alahi, Alexandre},
    title     = {GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22404-22415}
}

raw_affiliation:
[{"affiliation": "Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "University of Bern", "city": "Bern", "country": "Switzerland"}, {"affiliation": "Swiss Data Science Center", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]

affiliations:
["Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)", "University of Bern", "Swiss Data Science Center", "University of Zurich", "ETH Zurich"]


title: VEU-Bench: Towards Comprehensive Understanding of Video Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13671-13680
cities: ["Unknown", "Providence", "Nanjing", "Toronto", "Shanghai"]
countries: ["Unknown", "USA", "China", "Canada", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VEU-Bench_Towards_Comprehensive_Understanding_of_Video_Editing_CVPR_2025_paper.html

authors:
Bozheng Li, Yongliang Wu, Yi Lu, Jiashuo Yu, Licheng Tang, Jiawang Cao, Wenqing Zhu, Yuyang Sun, Jay Wu, Wenbo Zhu

abstract:
Widely shared videos on the internet are often edited. Recently, although Video Large Language Models (Vid-LLMs) have made great progress in general video understanding tasks, their capabilities in video editing understanding (VEU) tasks remain unexplored. To address this gap, in this paper, we introduce VEU-Bench (Video Editing Understanding Benchmark), a comprehensive benchmark that categorizes video editing components across various dimensions, from intra-frame features like shot size to inter-shot attributes such as cut types and transitions. Unlike previous video editing understanding benchmarks that focus mainly on editing element classification, VEU-Bench encompasses 19 fine-grained tasks across three stages: recognition, reasoning, and judging. To enhance the annotation of VEU automatically, we built an annotation pipeline integrated with an ontology-based knowledge base. Through extensive experiments with 11 state-of-the-art Vid-LLMs, our findings reveal that current Vid-LLMs face significant challenges in VEU tasks, with some performing worse than random choice. To alleviate this issue, we develop Oscars(Named after the Academy Awards.), a VEU expert model fine-tuned on the curated VEU-Bench dataset. It outperforms existing open-source Vid-LLMs on VEU-Bench by over 28.3% in accuracy and achieves performance comparable to commercial models like GPT-4o. We also demonstrate that incorporating VEU data significantly enhances the performance of Vid-LLMs on general video understanding benchmarks, with an average improvement of 8.3% across nine reasoning tasks. The code and data will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VEU-Bench_Towards_Comprehensive_Understanding_of_Video_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_VEU-Bench_Towards_Comprehensive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Bozheng and Wu, Yongliang and Lu, Yi and Yu, Jiashuo and Tang, Licheng and Cao, Jiawang and Zhu, Wenqing and Sun, Yuyang and Wu, Jay and Zhu, Wenbo},
    title     = {VEU-Bench: Towards Comprehensive Understanding of Video Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13671-13680}
}

raw_affiliation:
[{"affiliation": "Opus AI Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]

affiliations:
["Opus AI Research", "Brown University", "Southeast University", "University of Toronto", "Fudan University"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18051-18061
cities: ["Beijing", "Beijing", "Beijing", "Xi'an"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Decouple_Distortion_from_Perception_Region_Adaptive_Diffusion_for_Extreme-low_Bitrate_CVPR_2025_paper.html

title:
Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image Compression

authors:
Jinchang Xu, Shaokang Wang, Jintao Chen, Zhe Li, Peidong Jia, Fei Zhao, Guoqing Xiang, Zhijian Hao, Shanghang Zhang, Xiaodong Xie

abstract:
Leveraging the generative power of diffusion models, generative image compression has achieved impressive perceptual fidelity even at extremely low bitrates. However, current methods often neglect the non-uniform complexity of images, limiting their ability to balance global perceptual quality with local texture consistency and to allocate coding resources efficiently. To address this, we introduce the Map-guided Masking Realism Image Diffusion Codec(MRIDC), designed to optimize the trade-off between local distortion and global perceptual quality in extreme-low bitrate compression. MRIDC integrates a vector-quantized image encoder with a diffusion-based decoder. On the encoding side, we propose a Map-guided Latent Masking(MLM) module, which selectively masks elements in the latent space based on prior information, allowing adaptive resource allocation aligned with image complexity. On the decoding side, masked latents are completed using the Bidirectional Prediction Controllable Generation(BPCG) module, which guides the constrained generation process within the diffusion model to reconstruct the image. Experimental results show that MRIDC achieves state-of-the-art perceptual compression quality at extremely low bitrates, effectively preserving feature consistency in key regions and advancing the rate-distortion-perception performance curve, establishing new benchmarks in balancing compression efficiency with visual fidelity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Decouple_Distortion_from_Perception_Region_Adaptive_Diffusion_for_Extreme-low_Bitrate_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Decouple_Distortion_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jinchang and Wang, Shaokang and Chen, Jintao and Li, Zhe and Jia, Peidong and Zhao, Fei and Xiang, Guoqing and Hao, Zhijian and Zhang, Shanghang and Xie, Xiaodong},
    title     = {Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18051-18061}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software & Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Alibaba Cloud Computing", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology, School of Microelectronics, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "School of Software & Microelectronics, Peking University", "Alibaba Cloud Computing", "State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology, School of Microelectronics, Xidian University"]


title: Yo'Chameleon: Personalized Vision and Language Generation
authors: Thao Nguyen, Krishna Kumar Singh, Jing Shi, Trung Bui, Yong Jae Lee, Yuheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14438-14448
affiliations: ["University of Wisconsin\u2013Madison", "Adobe Research"]
cities: ["Madison", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_YoChameleon_Personalized_Vision_and_Language_Generation_CVPR_2025_paper.html

abstract:
Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a "soft-positive" image generation approach to enhance image quality in a few-shot setting. Our qualitative and quantitative analyses reveal that Yo'Chameleon can learn concepts more efficiently using fewer tokens and effectively encode visual attributes, outperforming prompting baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_YoChameleon_Personalized_Vision_and_Language_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_YoChameleon_Personalized_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Thao and Singh, Krishna Kumar and Shi, Jing and Bui, Trung and Lee, Yong Jae and Li, Yuheng},
    title     = {Yo'Chameleon: Personalized Vision and Language Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14438-14448}
}

raw_affiliation:
[{"affiliation": "University of Wisconsin\u2013Madison", "city": "Madison", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution
authors: Shian Du, Menghan Xia, Chang Liu, Xintao Wang, Jing Wang, Pengfei Wan, Di Zhang, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17799-17809
affiliations: ["Tsinghua University", "Kuaishou Technology", "Beijing Institute of Technology"]
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_PatchVSR_Breaking_Video_Diffusion_Resolution_Limits_with_Patch-wise_Video_Super-Resolution_CVPR_2025_paper.html

abstract:
Pre-trained video generation models hold great potential for generative video super-resolution (VSR). However, adapting them for full-size VSR, as most existing methods do, suffers from unnecessary intensive full-attention computation and fixed output resolution. To overcome these limitations, we make the first exploration into utilizing video diffusion priors for patch-wise VSR.This is non-trivial because pre-trained video diffusion models are not native for patch-level detail generation. To mitigate this challenge, we propose an innovative approach, called PatchVSR, which integrates a dual-stream adapter for conditional guidance. The patch branch extracts features from input patches to maintain content fidelity while the global branch extracts context features from the resized full video to bridge the generation gap caused byincomplete semantics of patches.Particularly, we also inject the patch's location information into the model to better contextualize patch synthesis within the global video frame.Experiments demonstrate that our method can synthesize high-fidelity, high-resolution details at the patch level. A tailor-made multi-patch joint modulation is proposed to ensure visual consistency across individually enhanced patches. Due to the flexibility of our patch-based paradigm, we can achieve highly competitive 4K VSR based on a 512x512 resolution base model, with extremely high efficiency.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_PatchVSR_Breaking_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Shian and Xia, Menghan and Liu, Chang and Wang, Xintao and Wang, Jing and Wan, Pengfei and Zhang, Di and Ji, Xiangyang},
    title     = {PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17799-17809}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]


title: FluxSpace: Disentangled Semantic Editing in Rectified Flow Models
authors: Yusuf Dalva, Kavana Venkatesh, Pinar Yanardag
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13083-13092
raw_affiliation: [{"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}]
affiliations: ["Virginia Tech"]
cities: ["Blacksburg"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dalva_FluxSpace_Disentangled_Semantic_Editing_in_Rectified_Flow_Models_CVPR_2025_paper.html

abstract:
Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiveness in visual generation, rectified flow models often struggle with disentangled editing of images. This limitation prevents the ability to perform precise, attribute-specific modifications without affecting unrelated aspects of the image. In this paper, we introduce FluxSpace, a domain-agnostic image editing method leveraging a representation space with the ability to control the semantics of images generated by rectified flow transformers, such as Flux. By leveraging the representations learned by the transformer blocks within the rectified flow models, we propose a set of semantically interpretable representations that enable a wide range of image editing tasks, from fine-grained image editing to artistic creation. This work offers a scalable and effective image editing approach, along with its disentanglement capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dalva_FluxSpace_Disentangled_Semantic_Editing_in_Rectified_Flow_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dalva_FluxSpace_Disentangled_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dalva_2025_CVPR,
    author    = {Dalva, Yusuf and Venkatesh, Kavana and Yanardag, Pinar},
    title     = {FluxSpace: Disentangled Semantic Editing in Rectified Flow Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13083-13092}
}


title: Scene-Centric Unsupervised Panoptic Segmentation
authors: Oliver Hahn, Christoph Reich, Nikita Araslanov, Daniel Cremers, Christian Rupprecht, Stefan Roth
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24485-24495
affiliations: ["TU Darmstadt", "TU Munich", "University of Oxford", "MCML", "ELIZA", "hessian.AI"]
cities: ["Darmstadt", "Munich", "Oxford", "Unknown", "Unknown", "Unknown"]
countries: ["Germany", "Germany", "UK", "Unknown", "Unknown", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hahn_Scene-Centric_Unsupervised_Panoptic_Segmentation_CVPR_2025_paper.html

abstract:
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised panoptic scene understanding, we eliminate the need for object-centric training data, enabling the unsupervised understanding of complex scenes. To that end,  we present the first unsupervised panoptic method that directly trains on scene-centric imagery. In particular, we propose an approach to obtain high-resolution panoptic pseudo labels on complex scene-centric data, combining visual representations, depth, and motion cues. Utilizing both pseudo-label training and a panoptic self-training strategy yields a novel approach that accurately predicts panoptic segmentation of complex scenes without requiring any human annotations. Our approach significantly improves panoptic quality, e.g., surpassing the recent state of the art in unsupervised panoptic segmentation on Cityscapes by 9.4% points in PQ.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hahn_Scene-Centric_Unsupervised_Panoptic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hahn_Scene-Centric_Unsupervised_Panoptic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hahn_2025_CVPR,
    author    = {Hahn, Oliver and Reich, Christoph and Araslanov, Nikita and Cremers, Daniel and Rupprecht, Christian and Roth, Stefan},
    title     = {Scene-Centric Unsupervised Panoptic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24485-24495}
}

raw_affiliation:
[{"affiliation": "TU Darmstadt", "city": "Darmstadt", "country": "Germany"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "MCML", "city": "Unknown", "country": "Unknown"}, {"affiliation": "ELIZA", "city": "Unknown", "country": "Unknown"}, {"affiliation": "hessian.AI", "city": "Unknown", "country": "Germany"}]


title: Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction
authors: Yuanbo Wang, Zhaoxuan Zhang, Jiajin Qiu, Dilong Sun, Zhengyu Meng, Xiaopeng Wei, Xin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5656-5665
cities: ["Dalian", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Touch2Shape_Touch-Conditioned_3D_Diffusion_for_Shape_Exploration_and_Reconstruction_CVPR_2025_paper.html

abstract:
Diffusion models have made breakthroughs in 3D generation tasks. Current 3D diffusion models focus on reconstructing target shape from images or a set of partial observations. While excelling in global context understanding, they struggle to capture the local details of complex shapes and limited to the occlusion and lighting conditions. To overcome these limitations, we utilize tactile images to capture the local 3D information and propose a Touch2Shape model, which leverages a touch-conditioned diffusion model to explore and reconstruct the target shape from touch. For shape reconstruction, we have developed a touch embedding module to condition the diffusion model in creating a compact representation and a touch shape fusion module to refine the reconstructed shape. For shape exploration, we combine the diffusion model with reinforcement learning to train a policy. This involves using the generated latent vector from the diffusion model to guide the touch exploration policy training through a novel reward design. Experiments validate the reconstruction quality thorough both qualitatively and quantitative analysis, and our touch exploration policy further boosts reconstruction performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Touch2Shape_Touch-Conditioned_3D_Diffusion_for_Shape_Exploration_and_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Touch2Shape_Touch-Conditioned_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuanbo and Zhang, Zhaoxuan and Qiu, Jiajin and Sun, Dilong and Meng, Zhengyu and Wei, Xiaopeng and Yang, Xin},
    title     = {Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5656-5665}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Nanjing University of Posts and Telecommunications", "city": "Nanjing", "country": "China"}]

affiliations:
["Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology", "Nanjing University of Posts and Telecommunications"]


title: VITED: Video Temporal Evidence Distillation
authors: Yujie Lu, Yale Song, William Wang, Lorenzo Torresani, Tushar Nagarajan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8501-8511
affiliations: ["UC Santa Barbara", "FAIR, Meta"]
cities: ["Santa Barbara", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_VITED_Video_Temporal_Evidence_Distillation_CVPR_2025_paper.html

abstract:
We investigate complex video question answering via chain-of-evidence reasoning --- identifying sequences of temporal spans from multiple relevant parts of the video, together with visual evidence within them.Existing models struggle with multi-step reasoning as they uniformly sample a fixed number of frames, which can miss critical evidence distributed nonuniformly throughout the video. Moreover, they lack the ability to temporally localize such evidence in the broader context of the full video, which is required for answering complex questions. We propose a framework to enhance existing VideoQA datasets with evidence reasoning chains, automatically constructed by searching for optimal intervals of interest in the video with supporting evidence, that maximizes the likelihood of answering a given question.We train our model (ViTED) to generate these evidence chains directly, enabling it to both localize evidence windows as well as perform multi-step reasoning across them in long-form video content.We show the value of our evidence-distilled models on a suite of long video QA benchmarks where we outperform state-of-the-art approaches that lack evidence reasoning capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_VITED_Video_Temporal_Evidence_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_VITED_Video_Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Yujie and Song, Yale and Wang, William and Torresani, Lorenzo and Nagarajan, Tushar},
    title     = {VITED: Video Temporal Evidence Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8501-8511}
}

raw_affiliation:
[{"affiliation": "UC Santa Barbara", "city": "Santa Barbara", "country": "USA"}, {"affiliation": "FAIR, Meta", "city": "Menlo Park", "country": "USA"}]


title: Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization
authors: Zhipeng Xu, De Cheng, Xinyang Jiang, Nannan Wang, Dongsheng Li, Xinbo Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18584-18595
cities: ["Xi'an", "Beijing", "Chongqing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Adversarial_Domain_Prompt_Tuning_and_Generation_for_Single_Domain_Generalization_CVPR_2025_paper.html

abstract:
Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One of the promising directions for achieving single-domain generalization is to generate out-of-domain (OOD) training data through data augmentation or image generation. Given the rapid advancements in AI-generated content (AIGC), this paper is the first to propose leveraging powerful pre-trained text-to-image (T2I) foundation models to create the training data. However, manually designing textual prompts to generate images for all possible domains is often impractical, and some domain characteristics may be too abstract to describe with words. To address these challenges, we propose a novel Progressive Adversarial Prompt Tuning (PAPT) framework for pre-trained diffusion models. Instead of relying on static textual domains, our approach learns two sets of abstract prompts as conditions for the diffusion model: one that captures domain-invariant category information and another that models domain-specific styles. This adversarial learning mechanism enables the T2I model to generate images in various domain styles while preserving key categorical features. Extensive experiments demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art single-domain generalization approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Adversarial_Domain_Prompt_Tuning_and_Generation_for_Single_Domain_Generalization_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zhipeng and Cheng, De and Jiang, Xinyang and Wang, Nannan and Li, Dongsheng and Gao, Xinbo},
    title     = {Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18584-18595}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "Chongqing University of Posts and Telecommunications", "city": "Chongqing", "country": "China"}]

affiliations:
["Xidian University", "Microsoft Research Asia", "Chongqing University of Posts and Telecommunications"]


authors: Alejandro Castañeda Garcia, Jan Warchocki, Jan van Gemert, Daan Brinks, Nergis Tomen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27924-27933
raw_affiliation: [{"affiliation": "Delft University of Technology", "city": "Delft", "country": "Netherlands"}]
affiliations: ["Delft University of Technology"]
cities: ["Delft"]
countries: ["Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Garcia_Learning_Physics_From_Video_Unsupervised_Physical_Parameter_Estimation_for_Continuous_CVPR_2025_paper.html

title:
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems

abstract:
Extracting physical dynamical system parameters from recorded observations is key in natural science. Current methods for automatic  parameter estimation from video train supervised deep networks on large datasets. Such datasets require labels, which are difficult to acquire. While some unsupervised techniques--which depend on frame prediction--exist, they suffer from long training times, initialization instabilities, only consider motion-based dynamical systems, and are evaluated mainly on synthetic data. In this work, we propose an unsupervised method to estimate the physical parameters of known, continuous governing equations from single videos suitable for different dynamical systems beyond motion and robust to initialization. Moreover, we remove the need for frame prediction by implementing a KL-divergence-based loss function in the latent space, which avoids convergence to trivial solutions and reduces model size and compute. We first evaluate our model on  synthetic data, as commonly done. After which, we take the field closer to reality by recording Delfys75: our own real-world dataset of 75 videos for five different types of dynamical systems to evaluate our method and others. Our method compares favorably to others. Code and data are available online: https://github.com/Alejandro-neuro/Learning_physics_from_video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Garcia_Learning_Physics_From_Video_Unsupervised_Physical_Parameter_Estimation_for_Continuous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garcia_Learning_Physics_From_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Garcia_2025_CVPR,
    author    = {Garcia, Alejandro Casta\~neda and Warchocki, Jan and van Gemert, Jan and Brinks, Daan and Tomen, Nergis},
    title     = {Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27924-27933}
}


title: Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts
authors: Yu Cao, Zengqun Zhao, Ioannis Patras, Shaogang Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7707-7716
raw_affiliation: [{"affiliation": "Queen Mary University of London", "city": "London", "country": "UK"}]
affiliations: ["Queen Mary University of London"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_Temporal_Score_Analysis_for_Understanding_and_Correcting_Diffusion_Artifacts_CVPR_2025_paper.html

abstract:
Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. In our analysis, we identify three distinct phases in the diffusion generative process: Profiling, Mutation, and Refinement. Artifacts typically emerge during the Mutation phase, where certain regions exhibit anomalous score dynamics over time, causing abrupt disruptions in the normal evolution pattern. This temporal nature explains why existing methods focusing only on spatial uncertainty of the final output fail at effective artifact localization. Based on these insights, we propose ASCED (Abnormal Score Correction for Enhancing Diffusion), that detects artifacts by monitoring abnormal score dynamics during the diffusion process, with a trajectory-aware on-the-fly mitigation strategy that appropriate generation of noise in the detected areas. Unlike most existing methods that apply post hoc corrections, e.g., by applying a noising-denoising scheme after generation, our mitigation strategy operates seamlessly within the existing diffusion process. Extensive experiments demonstrate that our proposed approach effectively reduces artifacts across diverse domains, matching or surpassing existing supervised methods without additional training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_Temporal_Score_Analysis_for_Understanding_and_Correcting_Diffusion_Artifacts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_Temporal_Score_Analysis_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Yu and Zhao, Zengqun and Patras, Ioannis and Gong, Shaogang},
    title     = {Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7707-7716}
}


title: ProAPO: Progressively Automatic Prompt Optimization for Visual Classification
authors: Xiangyan Qu, Gaopeng Gou, Jiamin Zhuang, Jing Yu, Kun Song, Qihao Wang, Yili Li, Gang Xiong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25145-25155
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_ProAPO_Progressively_Automatic_Prompt_Optimization_for_Visual_Classification_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) have made significant progress in image classification by training with large-scale paired image-text data. Their performances largely depend on the prompt quality. While recent methods show that visual descriptions generated by large language models (LLMs) enhance the generalization of VLMs, class-specific prompts may be inaccurate or lack discrimination due to the hallucination in LLMs. In this paper, we aim to find visually discriminative prompts for fine-grained categories with minimal supervision and no human-in-the-loop. An evolution-based algorithm is proposed to progressively optimize language prompts from task-specific templates to class-specific descriptions. Unlike optimizing templates, the search space shows an explosion in class-specific candidate prompts. This increases prompt generation costs, iterative times, and the overfitting problem. To this end, we first introduce several simple yet effective edit-based and evolution-based operations to generate diverse candidate prompts by one-time query of LLMs. Then, two sampling strategies are proposed to find a better initial search point and reduce traversed categories, saving iteration costs. Moreover, we apply a novel fitness score with entropy constraints to mitigate overfitting. In a challenging one-shot image classification setting, our method outperforms existing textual prompt-based methods and improves LLM-generated description methods across 13 datasets. Meanwhile, we demonstrate that our optimal prompts improve adapter-based methods and transfer effectively across different backbones.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_ProAPO_Progressively_Automatic_Prompt_Optimization_for_Visual_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_ProAPO_Progressively_Automatic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Xiangyan and Gou, Gaopeng and Zhuang, Jiamin and Yu, Jing and Song, Kun and Wang, Qihao and Li, Yili and Xiong, Gang},
    title     = {ProAPO: Progressively Automatic Prompt Optimization for Visual Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25145-25155}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Information Engineering, Minzu University of China", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology Beijing", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Security, Chinese Academy of Sciences", "School of Information Engineering, Minzu University of China", "University of Science and Technology Beijing"]


title: ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13305-13314
affiliations: ["UMass Amherst", "CYENS CoE", "University of Cyprus", "TU Crete"]
cities: ["Amherst", "Nicosia", "Nicosia", "Chania"]
countries: ["USA", "Cyprus", "Cyprus", "Greece"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Petrov_ShapeWords_Guiding_Text-to-Image_Synthesis_with_3D_Shape-Aware_Prompts_CVPR_2025_paper.html

authors:
Dmitry Petrov, Pradyumn Goyal, Divyansh Shivashok, Yuanming Tao, Melinos Averkiou, Evangelos Kalogerakis

abstract:
We introduce ShapeWords, an approach for synthesizing images based on 3D shape guidance and text prompts.ShapeWords incorporates target 3D shape information within specialized tokens embedded together with the input text, effectively blending 3D shape awareness with textual context to guide the image synthesis process. Unlike conventional shape guidance methods that rely on depth maps restricted to fixed viewpoints and often overlook full 3D structure or textual context, ShapeWords generates diverse yet consistent images that reflect both the target shape's geometry and the textual description. Experimental results show that ShapeWords produces images that are more text-compliant, aesthetically plausible, while also maintaining 3D shape awareness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Petrov_ShapeWords_Guiding_Text-to-Image_Synthesis_with_3D_Shape-Aware_Prompts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Petrov_ShapeWords_Guiding_Text-to-Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Petrov_2025_CVPR,
    author    = {Petrov, Dmitry and Goyal, Pradyumn and Shivashok, Divyansh and Tao, Yuanming and Averkiou, Melinos and Kalogerakis, Evangelos},
    title     = {ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13305-13314}
}

raw_affiliation:
[{"affiliation": "UMass Amherst", "city": "Amherst", "country": "USA"}, {"affiliation": "CYENS CoE", "city": "Nicosia", "country": "Cyprus"}, {"affiliation": "University of Cyprus", "city": "Nicosia", "country": "Cyprus"}, {"affiliation": "TU Crete", "city": "Chania", "country": "Greece"}]


title: Auto-Encoded Supervision for Perceptual Image Super-Resolution
authors: MinKyu Lee, Sangeek Hyun, Woojin Jun, Jae-Pil Heo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17958-17968
raw_affiliation: [{"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}]
affiliations: ["Sungkyunkwan University"]
cities: ["Suwon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Auto-Encoded_Supervision_for_Perceptual_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
This work tackles the fidelity objective in the perceptual super-resolution (SR) task. Specifically, we address the shortcomings of pixel-level \mathcal L _\text p  loss (\mathcal L _\text pix ) in the GAN-based SR framework. Since \mathcal L _\text pix  is known to have a trade-off relationship against perceptual quality, prior methods often multiply a small scale factor or utilize low-pass filters. However, this work shows that these circumventions fail to address the fundamental factor that induces blurring. Accordingly, we focus on two points: 1) precisely discriminating the subcomponent of \mathcal L _\text pix  that contributes to blurring, and 2) guiding reconstruction only based on the factor that is free from this trade-off relationship. We show that this can be achieved in a surprisingly simple manner, with an Auto-Encoder (AE) pretrained using \mathcal L _\text pix . Based on this insight, we propose the Auto-Encoded Supervision for Optimal Penalization loss (\mathcal L _\text AESOP ), a novel loss function that measures distance in the AE space (the space after the decoder, not the bottleneck), rather than in the raw pixel space. By simply substituting \mathcal L _\text pix  with \mathcal L _\text AESOP , we can provide effective reconstruction guidance without compromising perceptual quality. Designed for simplicity, our method enables easy integration into existing SR frameworks. Extensive experiments demonstrate the effectiveness of AESOP.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Auto-Encoded_Supervision_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, MinKyu and Hyun, Sangeek and Jun, Woojin and Heo, Jae-Pil},
    title     = {Auto-Encoded Supervision for Perceptual Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17958-17968}
}


title: Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events
authors: Aditya Chinchure, Sahithya Ravi, Raymond Ng, Vered Shwartz, Boyang Li, Leonid Sigal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24201-24210
affiliations: ["University of British Columbia", "Vector Institute for AI", "Nanyang Technological University"]
cities: ["Vancouver", "Toronto", "Singapore"]
countries: ["Canada", "Canada", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chinchure_Black_Swan_Abductive_and_Defeasible_Video_Reasoning_in_Unpredictable_Events_CVPR_2025_paper.html

abstract:
The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on typical visual scenarios, making it difficult to discern whether model performance stems from keen perception and reasoning skills, or reliance on pure statistical recall. We argue that by focusing on atypical events in videos, clearer insights can be gained on the core capabilities of VLMs. Explaining and understanding such out-of-distribution events requires models to extend beyond basic pattern recognition and regurgitation of their prior knowledge. To this end, we introduce BlackSwanSuite, a benchmark for evaluating VLMs' ability to reason about unexpected events through abductive and defeasible tasks. Our tasks artificially limit the amount of visual information provided to models while questioning them about hidden unexpected events, or provide new visual information that could change an existing hypothesis about the event. We curate a comprehensive benchmark suite comprising over 3,800 MCQ, 4,900 generative and 6,700 yes/no questions, spanning 1,655 videos. After extensively evaluating various state-of-the-art VLMs, including GPT-4o and Gemini 1.5 Pro, as well as open-source VLMs such as LLaVA-Video, we find significant performance gaps of up to 32% from humans on these tasks. Our findings reveal key limitations in current VLMs, emphasizing the need for enhanced model architectures and training strategies. Our data and leaderboard is available at blackswan.cs.ubc.ca.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chinchure_Black_Swan_Abductive_and_Defeasible_Video_Reasoning_in_Unpredictable_Events_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chinchure_Black_Swan_Abductive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chinchure_2025_CVPR,
    author    = {Chinchure, Aditya and Ravi, Sahithya and Ng, Raymond and Shwartz, Vered and Li, Boyang and Sigal, Leonid},
    title     = {Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24201-24210}
}

raw_affiliation:
[{"affiliation": "University of British Columbia", "city": "Vancouver", "country": "Canada"}, {"affiliation": "Vector Institute for AI", "city": "Toronto", "country": "Canada"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13-23
cities: ["Los Gatos", "Los Gatos", "Stony Brook", "College Park", "Stanford"]
countries: ["USA", "USA", "USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Burgert_Go-with-the-Flow_Motion-Controllable_Video_Diffusion_Models_Using_Real-Time_Warped_Noise_CVPR_2025_paper.html

authors:
Ryan Burgert, Yuancheng Xu, Wenqi Xian, Oliver Pilarski, Pascal Clausen, Mingming He, Li Ma, Yitong Deng, Lingxiao Li, Mohsen Mousavi, Michael Ryoo, Paul Debevec, Ning Yu

abstract:
Generative modeling aims to transform random noise into structured outputs. In this work, we enhance video diffusion models by allowing motion control via structured latent noise sampling. This is achieved by just a change in data: we pre-process training videos to yield structured noise. Consequently, our method is agnostic to diffusion model design, requiring no changes to model architectures or training pipelines. Specifically, we propose a novel noise warping algorithm, fast enough to run in real time, that replaces random temporal Gaussianity with correlated warped noise derived from optical flow fields, while preserving the spatial Gaussianity. The efficiency of our algorithm enables us to fine-tune modern video diffusion base models using warped noise with minimal overhead, and provide a one-stop solution for a wide range of user-friendly motion control: local object motion control, global camera movement control, and motion transfer. The harmonization between temporal coherence and spatial Gaussianity in our warped noise leads to effective motion control while maintaining per-frame pixel quality. Extensive experiments and user studies demonstrate the advantages of our method, making it a robust and scalable approach for controlling motion in video diffusion models. Please see our project webpage: https://eyeline-research.github.io/Go-with-the-Flow/ ; source code and checkpoints are available on GitHub: https://github.com/Eyeline-Research/Go-with-the-Flow

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Burgert_Go-with-the-Flow_Motion-Controllable_Video_Diffusion_Models_Using_Real-Time_Warped_Noise_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Burgert_Go-with-the-Flow_Motion-Controllable_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Burgert_2025_CVPR,
    author    = {Burgert, Ryan and Xu, Yuancheng and Xian, Wenqi and Pilarski, Oliver and Clausen, Pascal and He, Mingming and Ma, Li and Deng, Yitong and Li, Lingxiao and Mousavi, Mohsen and Ryoo, Michael and Debevec, Paul and Yu, Ning},
    title     = {Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13-23}
}

raw_affiliation:
[{"affiliation": "Netflix Eyeline Studios", "city": "Los Gatos", "country": "USA"}, {"affiliation": "Netflix", "city": "Los Gatos", "country": "USA"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]

affiliations:
["Netflix Eyeline Studios", "Netflix", "Stony Brook University", "University of Maryland", "Stanford University"]


authors: Yuan Gan, Jiaxu Miao, Yunze Wang, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13434-13444
cities: ["Hangzhou", "Guangzhou", "Madison"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gan_Silence_is_Golden_Leveraging_Adversarial_Examples_to_Nullify_Audio_Control_CVPR_2025_paper.html

title:
Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation

abstract:
Advances in talking-head animation based on Latent Diffusion Models (LDM) enable the creation of highly realistic, synchronized videos. These fabricated videos are indistinguishable from real ones, increasing the risk of potential misuse for scams, political manipulation, and misinformation. Hence, addressing these ethical concerns has become a pressing issue in AI security. Recent proactive defense studies focused on countering LDM-based models by adding perturbations to portraits. However, these methods are ineffective at protecting reference portraits from advanced image-to-video animation. The limitations are twofold: 1) they fail to prevent images from being manipulated by audio signals, and 2) diffusion-based purification techniques can effectively eliminate protective perturbations. To address these challenges, we propose Silencer, a two-stage method designed to proactively protect the privacy of portraits. First, a nullifying loss is proposed to ignore audio control in talking-head generation. Second, we apply anti-purification loss in LDM to optimize the inverted latent feature to generate robust perturbations. Extensive experiments demonstrate the effectiveness of Silencer in proactively protecting portrait privacy. We hope this work will raise awareness among the AI security community regarding critical ethical issues related to talking-head generation techniques. Code: https://github.com/yuangan/Silencer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gan_Silence_is_Golden_Leveraging_Adversarial_Examples_to_Nullify_Audio_Control_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gan_Silence_is_Golden_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gan_2025_CVPR,
    author    = {Gan, Yuan and Miao, Jiaxu and Wang, Yunze and Yang, Yi},
    title     = {Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13434-13444}
}

raw_affiliation:
[{"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "School of Cyber Science and Technology, Sun Y at-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Statistics, University of Wisconsin\u2013Madison", "city": "Madison", "country": "USA"}]

affiliations:
["ReLER, CCAI, Zhejiang University", "School of Cyber Science and Technology, Sun Y at-sen University", "Department of Statistics, University of Wisconsin\u2013Madison"]


title: Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing
authors: Jiayi Fu, Siyu Liu, Zikun Liu, Chun-Le Guo, Hyunhee Park, Ruiqi Wu, Guoqing Wang, Chongyi Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12700-12709
cities: ["Tianjin", "Shenzhen", "Beijing", "Unknown", "Zhoushan"]
countries: ["China", "China", "China", "South Korea", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Iterative_Predictor-Critic_Code_Decoding_for_Real-World_Image_Dehazing_CVPR_2025_paper.html

abstract:
We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance. Our idea stems from the observations that 1) the degradation of hazy images varies with haze density and scene depth, and 2) clear regions play crucial cues in restoring dense haze regions. However, it is nontrivial to progressively refine the obtained codes in subsequent iterations, owing to the difficulty in determining which codes should be retained or replaced at each iteration. Another key insight of our study is to propose Code-Critic to capture interrelations among codes. The Code-Critic is used to evaluate code correlations and then resample a set of codes with the highest mask scores, i.e., a higher score indicates that the code is more likely to be rejected, which helps retain more accurate codes and predict difficult ones. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods in real-world dehazing. Our code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Iterative_Predictor-Critic_Code_Decoding_for_Real-World_Image_Dehazing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Iterative_Predictor-Critic_Code_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Jiayi and Liu, Siyu and Liu, Zikun and Guo, Chun-Le and Park, Hyunhee and Wu, Ruiqi and Wang, Guoqing and Li, Chongyi},
    title     = {Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12700-12709}
}

raw_affiliation:
[{"affiliation": "VCIP, CS, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "NKIARI, Shenzhen Futian", "city": "Shenzhen", "country": "China"}, {"affiliation": "Samsung R&D Institute China-Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "CIG, Samsung Electronics", "city": "Unknown", "country": "South Korea"}, {"affiliation": "Donghai Laboratory, Zhoushan, Zhejiang", "city": "Zhoushan", "country": "China"}]

affiliations:
["VCIP, CS, Nankai University", "NKIARI, Shenzhen Futian", "Samsung R&D Institute China-Beijing", "CIG, Samsung Electronics", "Donghai Laboratory, Zhoushan, Zhejiang"]


title: RNG: Relightable Neural Gaussians
authors: Jiahui Fan, Fujun Luan, Jian Yang, Milos Hasan, Beibei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26525-26534
cities: ["Nanjing", "Unknown", "Nanjing"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_RNG_Relightable_Neural_Gaussians_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especially for objects with ill-defined shapes (fur, fabric, etc.), remains a challenging task. The decomposition between light, geometry, and material is ambiguous, especially if either smooth surface assumptions or surface-based analytical shading models do not apply. We propose Relightable Neural Gaussians (RNG), a novel 3DGS-based framework that enables the relighting of objects with both hard surfaces or soft boundaries, while avoiding assumptions on the shading model. We condition the radiance at each point on both view and light directions. We also introduce a shadow cue, as well as a depth refinement network to improve shadow accuracy. Finally, we propose a hybrid forward-deferred fitting strategy to balance geometry and appearance quality. Our method achieves significantly faster training (1.3 hours) and rendering (60 frames per second) compared to a prior method based on neural radiance fields and produces higher-quality shadows than a concurrent 3DGS-based method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_RNG_Relightable_Neural_Gaussians_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_RNG_Relightable_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Jiahui and Luan, Fujun and Yang, Jian and Hasan, Milos and Wang, Beibei},
    title     = {RNG: Relightable Neural Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26525-26534}
}

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

affiliations:
["PCA Lab \u2020, Nanjing University of Science and Technology", "Adobe Research", "Nanjing University"]


title: Towards Realistic Example-based Modeling via 3D Gaussian Stitching
authors: Xinyu Gao, Ziyi Yang, Bingchen Gong, Xiaoguang Han, Sipeng Yang, Xiaogang Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26597-26607
cities: ["Hangzhou", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Towards_Realistic_Example-based_Modeling_via_3D_Gaussian_Stitching_CVPR_2025_paper.html

abstract:
Using parts of existing models to rebuild new models, commonly termed as example-based modeling, is a classical methodology in the realm of computer graphics. Previous works mostly focus on shape composition, making them very hard to use for realistic composition of 3D objects captured from real-world scenes. This leads to combining multiple NeRFs into a single 3D scene to achieve seamless appearance blending. However, the current SeamlessNeRF method struggles to achieve interactive editing and harmonious stitching for real-world scenes due to its gradient-based strategy and grid-based representation. To this end, we present an example-based modeling method that combines multiple Gaussian fields in a point-based representation using sample-guided synthesis. Specifically, as for composition, we create a GUI to segment and transform multiple fields in real time, easily obtaining a semantically meaningful composition of models represented by 3D Gaussian Splatting (3DGS). For texture blending, due to the discrete and irregular nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF is not supported. Thus, a novel sampling-based cloning method is proposed to harmonize the blending while preserving the original rich texture and content. Our workflow consists of three steps: 1) real-time segmentation and transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis to identify boundary points in the intersecting area between the source and target models, and 3) two-phase optimization of the target model using sampling-based cloning and gradient constraints. Extensive experimental results validate that our approach significantly outperforms previous works in terms of realistic synthesis, demonstrating its practicality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Towards_Realistic_Example-based_Modeling_via_3D_Gaussian_Stitching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Towards_Realistic_Example-based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Xinyu and Yang, Ziyi and Gong, Bingchen and Han, Xiaoguang and Yang, Sipeng and Jin, Xiaogang},
    title     = {Towards Realistic Example-based Modeling via 3D Gaussian Stitching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26597-26607}
}

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

affiliations:
["Zhejiang University", "The Chinese University of Hong Kong", "The Chinese University of Hong Kong (Shenzhen)"]


authors: Bardia Safaei, Faizan Siddiqui, Jiacong Xu, Vishal M. Patel, Shao-Yuan Lo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14247-14256
affiliations: ["Johns Hopkins University", "Honda Research Institute USA"]
cities: ["Baltimore", "Sunnyvale"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Safaei_Filter_Images_First_Generate_Instructions_Later_Pre-Instruction_Data_Selection_for_CVPR_2025_paper.html

title:
Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning

abstract:
Visual instruction tuning (VIT) for large vision-language models (LVLMs) requires training on expansive datasets of image-instruction pairs, which can be costly. Recent efforts in VIT data selection aim to select a small subset of high-quality image-instruction pairs, reducing VIT runtime while maintaining performance comparable to full-scale training. However, a major challenge often overlooked is that generating instructions from unlabeled images for VIT is highly expensive. Most existing VIT datasets rely heavily on human annotations or paid services like the GPT API, which limits users with constrained resources from creating VIT datasets for custom applications. To address this, we introduce Pre-Instruction Data Selection (PreSel), a more practical data selection paradigm that directly selects the most beneficial unlabeled images and generates instructions only for the selected images. PreSel first estimates the relative importance of each vision task within VIT datasets to derive task-wise sampling budgets. It then clusters image features within each task, selecting the most representative images with the budget. This approach reduces computational overhead for both instruction generation during VIT data formation and LVLM fine-tuning. By generating instructions for only 15% of the images, PreSel achieves performance comparable to full-data VIT on the LLaVA-1.5 and Vision-Flan datasets. The link to our project page: https://bardisafa.github.io/PreSel

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Safaei_Filter_Images_First_Generate_Instructions_Later_Pre-Instruction_Data_Selection_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Safaei_Filter_Images_First_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Safaei_2025_CVPR,
    author    = {Safaei, Bardia and Siddiqui, Faizan and Xu, Jiacong and Patel, Vishal M. and Lo, Shao-Yuan},
    title     = {Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14247-14256}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Honda Research Institute USA", "city": "Sunnyvale", "country": "USA"}]


title: Gradient-Guided Annealing for Domain Generalization
authors: Aristotelis Ballas, Christos Diou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20558-20568
cities: ["Athens", "Athens"]
countries: ["Greece", "Greece"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ballas_Gradient-Guided_Annealing_for_Domain_Generalization_CVPR_2025_paper.html

abstract:
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even state-of-the-art training algorithms. In this paper we observe that the initial iterations of model training play a key role in domain generalization effectiveness, since the loss landscape may be significantly different across the training and test distributions, contrary to the case of i.i.d. data. Conflicts between gradients of the loss components of each domain lead the optimization procedure to undesirable local minima that do not capture the domain-invariant features of the target classes. We propose alleviating domain conflicts in model optimization, by iteratively annealing the parameters of a model in the early stages of training and searching for points where gradients align between domains. By discovering a set of parameter values where gradients are updated towards the same direction for each data distribution present in the training set, the proposed Gradient-Guided Annealing (GGA) algorithm encourages models to seek out minima that exhibit improved robustness against domain shifts. The efficacy of GGA is evaluated on five widely accepted and challenging image classification domain generalization benchmarks, where its use alone is able to establish highly competitive or even state-of-the-art performance. Moreover, when combined with previously proposed domain-generalization algorithms it is able to consistently improve their effectiveness by significant margins.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ballas_Gradient-Guided_Annealing_for_Domain_Generalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ballas_Gradient-Guided_Annealing_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ballas_2025_CVPR,
    author    = {Ballas, Aristotelis and Diou, Christos},
    title     = {Gradient-Guided Annealing for Domain Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20558-20568}
}

raw_affiliation:
[{"affiliation": "Dpt of Informatics and Telematics, Harokopio University of Athens", "city": "Athens", "country": "Greece"}, {"affiliation": "Dpt of Informatics and Telematics, Harokopio Univesity of Athens", "city": "Athens", "country": "Greece"}]

affiliations:
["Dpt of Informatics and Telematics, Harokopio University of Athens", "Dpt of Informatics and Telematics, Harokopio Univesity of Athens"]


title: Generative Sparse-View Gaussian Splatting
authors: Hanyang Kong, Xingyi Yang, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26745-26755
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_Generative_Sparse-View_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Novel view synthesis from limited observations remains a significant challenge due to the lack of information in under-sampled regions, often resulting in noticeable artifacts. We introduce Generative Sparse-view Gaussian Splatting (GS-GS), a general pipeline designed to enhance the rendering quality of 3D/4D Gaussian Splatting (GS) when training views are sparse. Our method generates unseen views using generative models, specifically leveraging pre-trained image diffusion models to iteratively refine view consistency and hallucinate additional images at pseudo views. This approach improves 3D/4D scene reconstruction by explicitly enforcing semantic correspondences during the generation of unseen views, thereby enhancing geometric consistency--unlike purely generative methods that often fail to maintain view consistency. Extensive evaluations on various 3D/4D datasets--including Blender, LLFF, Mip-NeRF360, and Neural 3D Video--demonstrate that our GS-GS outperforms existing state-of-the-art methods in rendering quality without sacrificing efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_Generative_Sparse-View_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_Generative_Sparse-View_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kong_2025_CVPR,
    author    = {Kong, Hanyang and Yang, Xingyi and Wang, Xinchao},
    title     = {Generative Sparse-View Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26745-26755}
}


title: MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19552-19564
cities: ["Stanford", "Beijing", "Chapel Hill", "Princeton", "Stockholm", "San Francisco"]
countries: ["USA", "China", "USA", "USA", "Sweden", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Burgess_MicroVQA_A_Multimodal_Reasoning_Benchmark_for_Microscopy-Based_Scientific_Research_CVPR_2025_paper.html

authors:
James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez, Alejandro Lozano, Sanket Rajan Gupte, Jesus G. Galaz-Montoya, Yuhui Zhang, Yuchang Su, Disha Bhowmik, Zachary Coman, Sarina M Hasan, Alexandra Johannesson, William D. Leineweber, Malvika G Nair, Ridhi Yarlagadda, Connor Zuraski, Wah Chiu, Sarah Cohen, Jan N. Hansen, Manuel D Leonetti, Chad Liu, Emma Lundberg, Serena Yeung-Levy

abstract:
Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based 'RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at https://huggingface.co/datasets/jmhb/microvqa and project at https://jmhb0.github.io/microvqa.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Burgess_MicroVQA_A_Multimodal_Reasoning_Benchmark_for_Microscopy-Based_Scientific_Research_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Burgess_MicroVQA_A_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Burgess_2025_CVPR,
    author    = {Burgess, James and Nirschl, Jeffrey J and Bravo-S\'anchez, Laura and Lozano, Alejandro and Gupte, Sanket Rajan and Galaz-Montoya, Jesus G. and Zhang, Yuhui and Su, Yuchang and Bhowmik, Disha and Coman, Zachary and Hasan, Sarina M and Johannesson, Alexandra and Leineweber, William D. and Nair, Malvika G and Yarlagadda, Ridhi and Zuraski, Connor and Chiu, Wah and Cohen, Sarah and Hansen, Jan N. and Leonetti, Manuel D and Liu, Chad and Lundberg, Emma and Yeung-Levy, Serena},
    title     = {MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19552-19564}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of North Carolina at Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "KTH Royal Institute of Technology", "city": "Stockholm", "country": "Sweden"}, {"affiliation": "Chan Zuckerberg Biohub Network", "city": "San Francisco", "country": "USA"}]

affiliations:
["Stanford University", "Tsinghua University", "University of North Carolina at Chapel Hill", "Princeton University", "KTH Royal Institute of Technology", "Chan Zuckerberg Biohub Network"]


title: Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
authors: Tianyi Zhu, Dongwei Ren, Qilong Wang, Xiaohe Wu, Wangmeng Zuo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27968-27978
affiliations: ["Harbin Institute of Technology", "Tianjin University"]
cities: ["Harbin", "Tianjin"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Generative_Inbetweening_through_Frame-wise_Conditions-Driven_Video_Generation_CVPR_2025_paper.html

abstract:
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, generative inbetweening still faces challenges in maintaining temporal stability due to the ambiguous interpolation path between two key frames. This issue becomes particularly severe when there is a large motion gap between input frames. In this paper, we propose a straightforward yet highly effective Frame-wise Conditions-driven Video Generation (FCVG) method that significantly enhances the temporal stability of interpolated video frames. Specifically, our FCVG provides an explicit condition for each frame, making it much easier to identify the interpolation path between two input frames and thus ensuring temporally stable production of visually plausible video frames. To achieve this, we suggest extracting matched lines from two input frames that can then be easily interpolated frame by frame, serving as frame-wise conditions seamlessly integrated into existing video generation models. In extensive evaluations covering diverse scenarios such as natural landscapes, complex human poses, camera movements and animations, existing methods often exhibit incoherent transitions across frames. In contrast, our FCVG demonstrates the capability to generate temporally stable videos using both linear and non-linear interpolation curves. Our project page and code are available at https://fcvg-inbetween.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Generative_Inbetweening_through_Frame-wise_Conditions-Driven_Video_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Tianyi and Ren, Dongwei and Wang, Qilong and Wu, Xiaohe and Zuo, Wangmeng},
    title     = {Generative Inbetweening through Frame-wise Conditions-Driven Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27968-27978}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}]


title: DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
authors: Yiming Zhong, Qi Jiang, Jingyi Yu, Yuexin Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22584-22594
affiliations: ["Computer Vision Foundation", "IEEE"]
cities: ["Unknown", "New York"]
countries: ["Unknown", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_DexGrasp_Anything_Towards_Universal_Robotic_Dexterous_Grasping_with_Physics_Awareness_CVPR_2025_paper.html

abstract:
A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Anything, a method that effectively integrates physical constraints into both the training and sampling phases of a diffusion-based generative model, achieving state-of-the-art performance across nearly all open datasets. Additionally, we present a new dexterous grasping dataset containing over 3.4 million diverse grasping poses for more than 15k different objects, demonstrating its potential to advance universal dexterous grasping. The code of our method and our dataset will be publicly released soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_DexGrasp_Anything_Towards_Universal_Robotic_Dexterous_Grasping_with_Physics_Awareness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_DexGrasp_Anything_Towards_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Yiming and Jiang, Qi and Yu, Jingyi and Ma, Yuexin},
    title     = {DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22584-22594}
}

raw_affiliation:
[{"affiliation": "Computer Vision Foundation", "city": "Unknown", "country": "Unknown"}, {"affiliation": "IEEE", "city": "New York", "country": "USA"}]


title: CustAny: Customizing Anything from A Single Example
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20916-20925
cities: ["Shanghai", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_CustAny_Customizing_Anything_from_A_Single_Example_CVPR_2025_paper.html

authors:
Lingjie Kong, Kai Wu, Chengming Xu, Xiaobin Hu, Wenhui Han, Jinlong Peng, Donghao Luo, Mengtian Li, Jiangning Zhang, Chengjie Wang, Yanwei Fu

abstract:
Recent advances in diffusion-based text-to-image models have simplified creating high-fidelity images, but preserving the identity (ID) of specific elements, like a personal dog, is still challenging.Object customization, using reference images and textual descriptions, is key to addressing this issue. Current object customization methods are either object-specific, requiring extensive fine-tuning, or object-agnostic, offering zero-shot customization but limited to specialized domains. The primary issue of promoting zero-shot object customization from specific domains to the general domain is to establish a large-scale general ID dataset for model pre-training, which is time-consuming and labor-intensive. In this paper, we propose a novel pipeline to construct a large dataset of general objects and build the Multi-Category ID-Consistent (MC-IDC) dataset, featuring 315k text-image samples across 10k categories. With the help of MC-IDC, we introduce Customizing Anything (CustAny), a zero-shot framework that maintains ID fidelity and supports flexible text editing for general objects. CustAny features three key components: a general ID extraction module, a dual-level ID injection module, and an ID-aware decoupling module, allowing it to customize any object from a single reference image and text prompt. Experiments demonstrate that CustAny outperforms existing methods in both general object customization and specialized domains like human customization and virtual try-on. Our contributions include a large-scale dataset, the CustAny framework and novel ID processing to advance this field. The official project page is in https://lingjiekong-fdu.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_CustAny_Customizing_Anything_from_A_Single_Example_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_CustAny_Customizing_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kong_2025_CVPR,
    author    = {Kong, Lingjie and Wu, Kai and Xu, Chengming and Hu, Xiaobin and Han, Wenhui and Peng, Jinlong and Luo, Donghao and Li, Mengtian and Zhang, Jiangning and Wang, Chengjie and Fu, Yanwei},
    title     = {CustAny: Customizing Anything from A Single Example},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20916-20925}
}

raw_affiliation:
[{"affiliation": "School of Data Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai Film Academy, Shanghai University", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Data Science, Fudan University", "Youtu Lab, Tencent", "Shanghai Film Academy, Shanghai University"]


title: Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks
authors: Haijin Zeng, Xiangming Wang, Yongyong Chen, Jingyong Su, Jie Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7524-7533
affiliations: ["Harvard University", "Harbin Institute of Technology (Shenzhen)"]
cities: ["Cambridge", "Shenzhen"]
countries: ["USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Vision-Language_Gradient_Descent-driven_All-in-One_Deep_Unfolding_Networks_CVPR_2025_paper.html

abstract:
Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental conditions. Existing Deep Unfolding Networks (DUNs) offer stable restoration performance but require manual selection of degradation matrices for each degradation type, limiting their adaptability across diverse scenarios.To address this issue, we propose the Vision-Language-guided Unfolding Network (VLU-Net), a unified DUN framework for handling multiple degradation types simultaneously.VLU-Net leverages a Vision-Language Model (VLM) refined on degraded image-text pairs to align image features with degradation descriptions, selecting the appropriate transform for target degradation.By integrating an automatic VLM-based gradient estimation strategy into the Proximal Gradient Descent (PGD) algorithm, VLU-Net effectively tackles complex multi-degradation restoration tasks while maintaining interpretability. Furthermore, we design a hierarchical feature unfolding structure to enhance VLU-Net framework, efficiently synthesizing degradation patterns across various levels.VLU-Net is the first all-in-one DUN framework and outperforms current leading one-by-one and all-in-one end-to-end methods by 3.74 dB on the SOTS dehazing dataset and 1.70 dB on the Rain100L deraining dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Vision-Language_Gradient_Descent-driven_All-in-One_Deep_Unfolding_Networks_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Haijin and Wang, Xiangming and Chen, Yongyong and Su, Jingyong and Liu, Jie},
    title     = {Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7524-7533}
}

raw_affiliation:
[{"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}]


title: 3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer
authors: Jiajun Deng, Tianyu He, Li Jiang, Tianyu Wang, Feras Dayoub, Ian Reid
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3772-3782
cities: ["Adelaide", "Redmond", "Shenzhen", "Abu Dhabi"]
countries: ["Australia", "USA", "China", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_3D-LLaVA_Towards_Generalist_3D_LMMs_with_Omni_Superpoint_Transformer_CVPR_2025_paper.html

abstract:
Current 3D Large Multimodal Models (3D LMMs) have shown tremendous potential in 3D-vision-based dialogue and reasoning. However, how to further enhance 3D LMMs to achieve fine-grained scene understanding and facilitate flexible human-agent interaction remains a challenging problem. In this work, we introduce 3D-LLaVA, a simple yet highly powerful 3D LMM designed to act as an intelligent assistant in comprehending, reasoning, and interacting with the 3D world. Unlike existing top-performing methods that rely on complicated pipelines--such as offline multi-view feature extraction or additional task-specific heads--3D-LLaVA adopts a minimalist design with integrated architecture and only takes point clouds as input. At the core of 3D-LLaVA is a new Omni Superpoint Transformer (OST), which integrates three functionalities: (1) a visual feature selector that converts and selects visual tokens, (2) a visual prompt encoder that embeds interactive visual prompts into the visual token space, and (3) a referring mask decoder that produces 3D masks based on text description. This versatile OST is empowered by the hybrid pretraining to obtain perception priors and leveraged as the visual connector that bridges the 3D data to the LLM. After performing unified instruction tuning, our 3D-LLaVA reports impressive results on various benchmarks. The code and model will be released at https://github.com/djiajunustc/3D-LLaVA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_3D-LLaVA_Towards_Generalist_3D_LMMs_with_Omni_Superpoint_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_3D-LLaVA_Towards_Generalist_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Jiajun and He, Tianyu and Jiang, Li and Wang, Tianyu and Dayoub, Feras and Reid, Ian},
    title     = {3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3772-3782}
}

raw_affiliation:
[{"affiliation": "Australian Institute for Machine Learning, The University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["Australian Institute for Machine Learning, The University of Adelaide", "Microsoft Research", "The Chinese University of Hong Kong, Shenzhen", "Mohamed bin Zayed University of AI"]


title: Event-based Video Super-Resolution via State Space Models
authors: Zeyu Xiao, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12564-12574
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Event-based_Video_Super-Resolution_via_State_Space_Models_CVPR_2025_paper.html

abstract:
Exploiting temporal correlations is crucial for video super-resolution (VSR). Recent approaches enhance this by incorporating event cameras. In this paper, we introduce MamEVSR, a Mamba-based network for event-based VSR that leverages the selective state space model, Mamba. MamEVSR stands out by offering global receptive field coverage with linear computational complexity, thus addressing the limitations of convolutional neural networks and Transformers. The key components of MamEVSR include: (1) The interleaved Mamba (iMamba) block, which interleaves tokens from adjacent frames and applies multidirectional selective state space modeling, enabling efficient feature fusion and propagation across bi-directional frames while maintaining linear complexity. (2) The crossmodality Mamba (cMamba) block facilitates further interaction and aggregation between event information and the output from the iMamba block. The cMamba block can leverage complementary spatio-temporal information from both modalities and allows MamEVSR to capture finermotion details. Experimental results show that the proposed MamEVSR achieves superior performance on various datasets quantitatively and qualitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Event-based_Video_Super-Resolution_via_State_Space_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Event-based_Video_Super-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Zeyu and Wang, Xinchao},
    title     = {Event-based Video Super-Resolution via State Space Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12564-12574}
}


title: PoseTraj: Pose-Aware Trajectory Control in Video Diffusion
authors: Longbin Ji, Lei Zhong, Pengfei Wei, Changjian Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22776-22785
affiliations: ["University of Edinburgh", "Nanyang Technological University"]
cities: ["Edinburgh", "Singapore"]
countries: ["UK", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_PoseTraj_Pose-Aware_Trajectory_Control_in_Video_Diffusion_CVPR_2025_paper.html

abstract:
Recent advancements in trajectory-guided video generation have achieved notable progress. However, existing models still face challenges in generating object motions with potentially changing 6D poses under wide-range rotations, due to limited 3D understanding. To address this problem, we introduce PoseTraj, a pose-aware video dragging model for generating 3D-aligned motion from 2D trajectories. Our method adopts a novel two-stage pose-aware pretraining framework, improving 3D understanding across diverse trajectories.  Specifically, we propose a large-scale synthetic dataset PoseTraj-10k, containing 10k videos of objects following rotational trajectories, and enhance the model perception of object pose changes by incorporating 3D bounding boxes as intermediate supervision signals. Following this, we fine-tune the trajectory-controlling module on real-world videos, applying an additional camera-disentanglement module to further refine motion accuracy. Experiments on various benchmark datasets demonstrate that our method not only excels in 3D pose-aligned dragging for rotational trajectories but also outperforms existing baselines in trajectory accuracy and video quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_PoseTraj_Pose-Aware_Trajectory_Control_in_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_PoseTraj_Pose-Aware_Trajectory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Longbin and Zhong, Lei and Wei, Pengfei and Li, Changjian},
    title     = {PoseTraj: Pose-Aware Trajectory Control in Video Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22776-22785}
}

raw_affiliation:
[{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


authors: Pedro Hermosilla, Christian Stippel, Leon Sick
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14835-14844
affiliations: ["TU Wien", "TU Wien", "Ulm University"]
cities: ["Vienna", "Vienna", "Ulm"]
countries: ["Austria", "Austria", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hermosilla_Masked_Scene_Modeling_Narrowing_the_Gap_Between_Supervised_and_Self-Supervised_CVPR_2025_paper.html

title:
Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding

abstract:
Self-supervised learning has transformed 2D computer vision by enabling models trained on large, unannotated datasets to provide versatile off-the-shelf features that perform similarly to models trained with labels. However, in 3D scene understanding, self-supervised methods are typically only used as a weight initialization step for task-specific fine-tuning, limiting their utility for general-purpose feature extraction.  This paper aims to address this shortcoming by proposing a robust evaluation protocol specifically designed to assess the quality of self-supervised features for 3D scene understanding. Our protocol uses multi-resolution feature sampling of hierarchical models to create rich point-level representations that capture the semantic capabilities of the model and, hence, are suitable for evaluation with linear probing and nearest-neighbor methods. Furthermore, we introduce the first self-supervised model that performs similarly to supervised models when only off-the-shelf features are used in a linear probing setup. In particular, our model is trained natively in 3D with a novel self-supervised approach based on a Masked Scene Modeling objective, which reconstructs deep features of masked patches in a bottom-up manner and is specifically tailored to hierarchical 3D models. Our experiments not only demonstrate that our method achieves competitive performance to supervised models, but also surpasses existing self-supervised approaches by a large margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hermosilla_Masked_Scene_Modeling_Narrowing_the_Gap_Between_Supervised_and_Self-Supervised_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hermosilla_Masked_Scene_Modeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hermosilla_2025_CVPR,
    author    = {Hermosilla, Pedro and Stippel, Christian and Sick, Leon},
    title     = {Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14835-14844}
}

raw_affiliation:
[{"affiliation": "TU Wien", "city": "Vienna", "country": "Austria"}, {"affiliation": "TU Wien", "city": "Vienna", "country": "Austria"}, {"affiliation": "Ulm University", "city": "Ulm", "country": "Germany"}]


authors: Jinseong Jang, Chunfei Ma, Byeongwon Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30073-30083
affiliations: ["Vision Lab, AI R&D Center, SK Telecom"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_VL2Lite_Task-Specific_Knowledge_Distillation_from_Large_Vision-Language_Models_to_Lightweight_CVPR_2025_paper.html

title:
VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks

abstract:
Deploying high-performing neural networks in resource-constrained environments poses a significant challenge due to the computational demands of large-scale models. We introduce VL2Lite, a knowledge distillation framework designed to enhance the performance of lightweight neural networks in image classification tasks by leveraging the rich representational knowledge from Vision-Language Models (VLMs). VL2Lite directly integrates multi-modal knowledge from VLMs into compact models during training, effectively compensating for the limited computational and modeling capabilities of smaller networks. By transferring high-level features and complex data representations, our approach improves the accuracy and efficiency of image classification tasks without increasing computational overhead during inference. Experimental evaluations demonstrate that VL2Lite achieves up to a 7% improvement in classification performance across various datasets. This method addresses the challenge of deploying accurate models in environments with constrained computational resources, offering a balanced solution between model complexity and operational efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_VL2Lite_Task-Specific_Knowledge_Distillation_from_Large_Vision-Language_Models_to_Lightweight_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Jinseong and Ma, Chunfei and Lee, Byeongwon},
    title     = {VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30073-30083}
}

raw_affiliation:
[{"affiliation": "Vision Lab, AI R&D Center, SK Telecom", "city": "Seoul", "country": "South Korea"}]


authors: Yuxin Li, Zihao Zhu, Yuxiang Zhang, Yifan Chen, Zhibin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10394-10403
cities: ["Qingdao", "Sanya"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Boost_the_Inference_with_Co-training_A_Depth-guided_Mutual_Learning_Framework_CVPR_2025_paper.html

title:
Boost the Inference with Co-training: A Depth-guided Mutual Learning Framework for Semi-supervised Medical Polyp Segmentation

abstract:
Semi-supervised polyp segmentation has made significant progress in recent years as a potential solution for computer-assisted treatment. Since depth images can provide extra information other than RGB images to help segment these problematic areas, depth-assisted polyp segmentation has gained much attention. However, the utilization of depth information is still worth studying. The existing RGB-D segmentation methods rely on depth data in the inference stage, limiting their clinical applications. To tackle this problem, we propose a semi-supervised polyp segmentation framework based on the mean teacher architecture. We establish an auxiliary student network with depth images as input in the training stage, and we propose a depth-guided cross-modal mutual learning strategy to promote the learning of complementary information between different student networks. Meanwhile, we use the high-confidence pseudo-labels generated by the auxiliary student network to guide the learning progress of the main student network from different perspectives. Our model does not need depth data in the inference phase. In addition, we introduce a depth-guided patch augmentation method to improve the model's learning performance in difficult regions of unlabeled polyp images. Experimental results show that our method achieves state-of-the-art performance under different label conditions on five polyp datasets.  The code is available at https://github.com/pingchuan/RD-Net.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Boost_the_Inference_with_Co-training_A_Depth-guided_Mutual_Learning_Framework_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuxin and Zhu, Zihao and Zhang, Yuxiang and Chen, Yifan and Yu, Zhibin},
    title     = {Boost the Inference with Co-training: A Depth-guided Mutual Learning Framework for Semi-supervised Medical Polyp Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10394-10403}
}

raw_affiliation:
[{"affiliation": "College of Electronic Engineering, Ocean University of China", "city": "Qingdao", "country": "China"}, {"affiliation": "Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China", "city": "Sanya", "country": "China"}]

affiliations:
["College of Electronic Engineering, Ocean University of China", "Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China"]


authors: Chaoyu Li, Eun Woo Im, Pooyan Fazli
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13723-13733
raw_affiliation: [{"affiliation": "Arizona State University", "city": "Tempe", "country": "USA"}]
affiliations: ["Arizona State University"]
cities: ["Tempe"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VidHalluc_Evaluating_Temporal_Hallucinations_in_Multimodal_Large_Language_Models_for_CVPR_2025_paper.html

title:
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding

abstract:
Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building on the observation that MLLM visual encoders often fail to distinguish visually different yet semantically similar video pairs, we introduce VidHalluc, the largest benchmark designed to examine hallucinations in MLLMs for video understanding. It consists of 5,002 videos, paired to highlight cases prone to hallucinations. VidHalluc assesses hallucinations across three critical dimensions: (1) action, (2) temporal sequence, and (3) scene transition. Comprehensive testing shows that most MLLMs are vulnerable to hallucinations across these dimensions. Furthermore, we propose DINO-HEAL, a training-free method that reduces hallucinations by incorporating spatial saliency from DINOv2 to reweight visual features during inference. Our results show that DINO-HEAL consistently improves performance on VidHalluc, achieving an average improvement of 3.02% in mitigating hallucinations across all tasks. Both the VidHalluc benchmark and DINO-HEAL code are available at https://people-robots.github.io/vidhalluc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VidHalluc_Evaluating_Temporal_Hallucinations_in_Multimodal_Large_Language_Models_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_VidHalluc_Evaluating_Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Chaoyu and Im, Eun Woo and Fazli, Pooyan},
    title     = {VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13723-13733}
}


title: StageDesigner: Artistic Stage Generation for Scenography via Theater Scripts
authors: Zhaoxing Gan, Mengtian Li, Ruhua Chen, Zhongxia Ji, Sichen Guo, Huanling Hu, Guangnan Ye, Zuo Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28705-28714
affiliations: ["Fudan University", "Shanghai University", "Shanghai Theatre Academy"]
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gan_StageDesigner_Artistic_Stage_Generation_for_Scenography_via_Theater_Scripts_CVPR_2025_paper.html

abstract:
In this work, we introduce StageDesigner, the first comprehensive framework for artistic stage generation using large language models combined with layout-controlled diffusion models. Given the professional requirements of stage scenography, StageDesigner simulates the workflows of seasoned artists to generate immersive 3D stage scenes. Specifically, our approach is divided into three primary modules: Script Analysis, which extracts thematic and spatial cues from input scripts; Foreground Generation, which constructs and arranges essential 3D objects; and Background Generation, which produces a harmonious background aligned with the narrative atmosphere and maintains spatial coherence by managing occlusions between foreground and background elements. Furthermore, we introduce the StagePro-V1 dataset, a dedicated dataset with 276 unique stage scenes spanning different historical styles and annotated with scripts, images, and detailed 3D layouts, specifically tailored for this task. Finally, evaluations using both standard and newly proposed metrics, along with extensive user studies, demonstrate the effectiveness of StageDesigner, showcasing its ability to produce visually and thematically cohesive stages that meet both artistic and spatial coherence standards. Project can be found at: https://deadsmither5.github.io/2025/01/03/StageDesigner/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gan_StageDesigner_Artistic_Stage_Generation_for_Scenography_via_Theater_Scripts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gan_StageDesigner_Artistic_Stage_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gan_2025_CVPR,
    author    = {Gan, Zhaoxing and Li, Mengtian and Chen, Ruhua and Ji, Zhongxia and Guo, Sichen and Hu, Huanling and Ye, Guangnan and Hu, Zuo},
    title     = {StageDesigner: Artistic Stage Generation for Scenography via Theater Scripts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28705-28714}
}

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


authors: Yan Jiang, Hao Yu, Xu Cheng, Haoyu Chen, Zhaodong Sun, Guoying Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8828-8837
cities: ["Nanjing", "Oulu"]
countries: ["China", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_From_Laboratory_to_Real_World_A_New_Benchmark_Towards_Privacy-Preserved_CVPR_2025_paper.html

title:
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification

abstract:
Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensive research attention and achieved promising results. However, in real-world surveillance contexts, data is distributed across multiple devices/entities, raising privacy and ownership concerns that make existing centralized training impractical for VI-ReID. To tackle these challenges, we propose L2RW, a benchmark that brings VI-ReID closer to real-world applications. The rationale of L2RW is that integrating decentralized training into VI-ReID can address privacy concerns in scenarios with limited data-sharing regulation. Specifically, we design protocols and corresponding algorithms for different privacy sensitivity levels. In our new benchmark, we ensure the model training is done in the conditions that: 1) data from each camera remains completely isolated, or 2) different data entities (e.g., data controllers of a certain region) can selectively share the data. In this way, we simulate scenarios with strict privacy constraints which is closer to real-world conditions. Intensive experiments with various server-side federated algorithms are conducted, showing the feasibility of decentralized VI-ReID training. Notably, when evaluated in unseen domains (i.e., new data entities), our L2RW, trained with isolated data (privacy-preserved), achieves performance comparable to SOTAs trained with shared data (privacy-unrestricted). We hope this work offers a novel research entry for deploying VI-ReID that fits real-world scenarios and can benefit the community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_From_Laboratory_to_Real_World_A_New_Benchmark_Towards_Privacy-Preserved_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_From_Laboratory_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Yan and Yu, Hao and Cheng, Xu and Chen, Haoyu and Sun, Zhaodong and Zhao, Guoying},
    title     = {From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8828-8837}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Nanjing University of Information Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Center for Machine Vision and Signal Analysis, University of Oulu", "city": "Oulu", "country": "Finland"}]

affiliations:
["School of Computer Science, Nanjing University of Information Science and Technology", "Center for Machine Vision and Signal Analysis, University of Oulu"]


title: 4Deform: Neural Surface Deformation for Robust Shape Interpolation
authors: Lu Sang, Zehranaz Canfes, Dongliang Cao, Riccardo Marin, Florian Bernard, Daniel Cremers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6542-6551
affiliations: ["Technical University of Munich", "Munich Center of Machine Learning", "University of Bonn"]
cities: ["Munich", "Munich", "Bonn"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sang_4Deform_Neural_Surface_Deformation_for_Robust_Shape_Interpolation_CVPR_2025_paper.html

abstract:
Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where temporal consistency across frames is lacking, and topologies are changing. Most interpolation methods are designed for structured data (i.e., meshes) and do not apply to real-world point clouds. In contrast, our approach leverages neural implicit representation (NIR) to enable free-topology changing shape deformation. Unlike previous mesh-based methods, which model learns vertex-based deformation fields, our method learns a continuous velocity field in Euclidean space, making it suitable for less structured data such as point clouds.Additionally, our method does not require intermediate-shape supervision during training; instead, we incorporate physical and geometrical constraints to regularize the velocity field. We reconstruct intermediate surfaces using a modified level-set equation, directly linking our NIR with the velocity field. Experiments show that our method significantly outperforms previous NIR approaches across various scenarios (e.g., noisy, partial, topology-changing, non-isometric shapes) and, for the first time, enables new applications like 4D Kinect sequence upsampling and real-world high-resolution mesh deformation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sang_4Deform_Neural_Surface_Deformation_for_Robust_Shape_Interpolation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sang_4Deform_Neural_Surface_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sang_2025_CVPR,
    author    = {Sang, Lu and Canfes, Zehranaz and Cao, Dongliang and Marin, Riccardo and Bernard, Florian and Cremers, Daniel},
    title     = {4Deform: Neural Surface Deformation for Robust Shape Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6542-6551}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center of Machine Learning", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}]


title: Dense Match Summarization for Faster Two-view Estimation
authors: Jonathan Astermark, Anders Heyden, Viktor Larsson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1093-1102
affiliations: ["Centre for Mathematical Sciences, Lund University"]
cities: ["Lund"]
countries: ["Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Astermark_Dense_Match_Summarization_for_Faster_Two-view_Estimation_CVPR_2025_paper.html

abstract:
In this paper, we speed up robust two-view relative pose from dense correspondences. Previous work has shown that dense matchers can significantly improve both accuracy and robustness in the resulting pose. However, the large number of matches comes with a significantly increased runtime during robust estimation in RANSAC. To avoid this, we propose an efficient match summarization scheme which provides comparable accuracy to using the full set of dense matches, while having 10-100x faster runtime. We validate our approach on standard benchmark datasets together with multiple state-of-the-art dense matchers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Astermark_Dense_Match_Summarization_for_Faster_Two-view_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Astermark_Dense_Match_Summarization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Astermark_2025_CVPR,
    author    = {Astermark, Jonathan and Heyden, Anders and Larsson, Viktor},
    title     = {Dense Match Summarization for Faster Two-view Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1093-1102}
}

raw_affiliation:
[{"affiliation": "Centre for Mathematical Sciences, Lund University", "city": "Lund", "country": "Sweden"}]


title: Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2074-2083
cities: ["Shanghai", "Shanghai", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Align-A-Video_Deterministic_Reward_Tuning_of_Image_Diffusion_Models_for_Consistent_CVPR_2025_paper.html

authors:
Shengzhi Wang, Yingkang Zhong, Jiangchuan Mu, Kai Wu, Mingliang Xiong, Wen Fang, Mingqing Liu, Hao Deng, Bin He, Gang Li, Qingwen Liu

abstract:
Due to control limitations in the denoising process and the lack of training, zero-shot video editing methods often struggle to meet user instructions, resulting in generated videos that are visually unappealing and fail to fully satisfy expectations. To address this problem, we propose Align-A-Video, a video editing pipeline that incorporates human feedback through reward fine-tuning. Our approach consists of two key steps: 1) Deterministic Reward Fine-tuning. To reduce optimization costs for expected noise distributions, we propose a deterministic reward tuning strategy. This method improves tuning stability by increasing sample determinism, allowing the tuning process to be completed in minutes; 2) Feature Propagation Across Frames. We optimize a selected anchor frame and propagate its features to the remaining frames, improving both visual quality and semantic fidelity. This approach avoids temporal consistency degradation from reward optimization. Extensive qualitative and quantitative experiments confirm the effectiveness of using reward fine-tuning in Align-A-Video, significantly improving the overall quality of generated videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Align-A-Video_Deterministic_Reward_Tuning_of_Image_Diffusion_Models_for_Consistent_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Align-A-Video_Deterministic_Reward_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shengzhi and Zhong, Yingkang and Mu, Jiangchuan and Wu, Kai and Xiong, Mingliang and Fang, Wen and Liu, Mingqing and Deng, Hao and He, Bin and Li, Gang and Liu, Qingwen},
    title     = {Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2074-2083}
}

raw_affiliation:
[{"affiliation": "Shanghai Research Institute for Autonomous Intelligent Systems, Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]

affiliations:
["Shanghai Research Institute for Autonomous Intelligent Systems, Tongji University", "School of Computer Science and Technology, Tongji University", "ByteDance"]


title: Interpreting Object-level Foundation Models via Visual Precision Search
authors: Ruoyu Chen, Siyuan Liang, Jingzhi Li, Shiming Liu, Maosen Li, Zhen Huang, Hua Zhang, Xiaochun Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30042-30052
cities: ["Beijing", "Beijing", "Singapore", "Shenzhen", "Shenzhen", "Changsha", "Kunming", "Shenzhen"]
countries: ["China", "China", "Singapore", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Interpreting_Object-level_Foundation_Models_via_Visual_Precision_Search_CVPR_2025_paper.html

abstract:
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. However, interpreting these models' decisions has grown increasingly challenging. Existing interpretable attribution methods for object-level task interpretation have notable limitations: (1) gradient-based methods lack precise localization due to visual-textual fusion in foundation models, and (2) perturbation-based methods produce noisy saliency maps, limiting fine-grained interpretability. To address these, we propose a Visual Precision Search method that generates accurate attribution maps with fewer regions. Our method bypasses internal model parameters to overcome attribution issues from multimodal fusion, dividing inputs into sparse sub-regions and using consistency and collaboration scores to accurately identify critical decision-making regions. We also conducted a theoretical analysis of the boundary guarantees and scope of applicability of our method. Experiments on RefCOCO, MS COCO, and LVIS show our approach enhances object-level task interpretability over SOTA for Grounding DINO and Florence-2 across various evaluation metrics, with faithfulness gains of 23.7%, 31.6%, and 20.1% on MS COCO, LVIS, and RefCOCO for Grounding DINO, and 50.7% and 66.9% on MS COCO and RefCOCO for Florence-2. Additionally, our method can interpret failures in visual grounding and object detection tasks, surpassing existing methods across multiple evaluation metrics. The code is released at https://github.com/RuoyuChen10/VPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Interpreting_Object-level_Foundation_Models_via_Visual_Precision_Search_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Interpreting_Object-level_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ruoyu and Liang, Siyuan and Li, Jingzhi and Liu, Shiming and Li, Maosen and Huang, Zhen and Zhang, Hua and Cao, Xiaochun},
    title     = {Interpreting Object-level Foundation Models via Visual Precision Search},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30042-30052}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computing, NUS", "city": "Singapore", "country": "Singapore"}, {"affiliation": "4RAMS Lab, Huawei Technologies Co., Ltd.", "city": "Shenzhen", "country": "China"}, {"affiliation": "5IAS BU, Huawei Technologies Co., Ltd.", "city": "Shenzhen", "country": "China"}, {"affiliation": "College of Computer, NUDT", "city": "Changsha", "country": "China"}, {"affiliation": "Key Lab. of Edu. Inf. for Nationalities (YNNU), Ministry of Education, Kunming, China", "city": "Kunming", "country": "China"}, {"affiliation": "School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China", "School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China", "School of Computing, NUS", "4RAMS Lab, Huawei Technologies Co., Ltd.", "5IAS BU, Huawei Technologies Co., Ltd.", "College of Computer, NUDT", "Key Lab. of Edu. Inf. for Nationalities (YNNU), Ministry of Education, Kunming, China", "School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"]


