# papers

title: ROD-MLLM: Towards More Reliable Object Detection in Multimodal Large Language Models
authors: Heng Yin, Yuqiang Ren, Ke Yan, Shouhong Ding, Yongtao Hao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14358-14368
affiliations: ["Tongji University", "YouTu Lab, Tencent"]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_ROD-MLLM_Towards_More_Reliable_Object_Detection_in_Multimodal_Large_Language_CVPR_2025_paper.html

abstract:
Multimodal large language models (MLLMs) have demonstrated strong language understanding and generation capabilities, excelling in visual tasks like referring and grounding. However, due to task type limitations and dataset scarcity, existing MLLMs only ground objects present in images and cannot reject non-existent objects effectively, resulting in unreliable predictions. In this paper, we introduce ROD-MLLM, a novel MLLM for Reliable Object Detection using free-form language. We propose a query-based localization mechanism to extract low-level object features. By aligning global and object-level visual information with text space, we leverage the large language model (LLM) for high-level comprehension and final localization decisions, overcoming the language understanding limitations of normal detectors. To enhance language-based object detection, we design an automated data annotation pipeline and construct the dataset ROD. This pipeline uses the referring capabilities of existing MLLMs and chain-of-thought techniques to generate diverse expressions corresponding to zero or multiple objects, addressing the shortage of training data. Experiments across various tasks, including referring, grounding, and language-based object detection, show that ROD-MLLM achieves state-of the-art performance among MLLMs. Notably, in language-based object detection, our model achieves +13.7 AP improvement on D3 benchmark over existing MLLMs and surpasses most specialized detection models, especially in scenarios requiring complex language understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_ROD-MLLM_Towards_More_Reliable_Object_Detection_in_Multimodal_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_ROD-MLLM_Towards_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Heng and Ren, Yuqiang and Yan, Ke and Ding, Shouhong and Hao, Yongtao},
    title     = {ROD-MLLM: Towards More Reliable Object Detection in Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14358-14368}
}

raw_affiliation:
[{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "YouTu Lab, Tencent", "city": "Shenzhen", "country": "China"}]


title: RoboGround: Robotic Manipulation with Grounded Vision-Language Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22540-22550
affiliations: ["Zhejiang University", "Shanghai AI Laboratory"]
cities: ["Hangzhou", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_RoboGround_Robotic_Manipulation_with_Grounded_Vision-Language_Priors_CVPR_2025_paper.html

authors:
Haifeng Huang, Xinyi Chen, Yilun Chen, Hao Li, Xiaoshen Han, Zehan Wang, Tai Wang, Jiangmiao Pang, Zhou Zhao

abstract:
Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding masks as an effective intermediate representation, balancing two key advantages: (1) effective spatial guidance that specifies target objects and placement areas while also conveying information about object shape and size, and (2) broad generalization potential driven by large-scale vision-language models pretrained on diverse grounding datasets. We introduce \method, a grounding-aware robotic manipulation policy that leverages grounding masks as an intermediate representation to guide policy networks in object manipulation tasks. To further explore and enhance generalization, we propose an automated pipeline for generating large-scale, simulated data with a diverse set of objects and instructions. Extensive experiments show the value of our dataset and the effectiveness of grounding masks as intermediate guidance, significantly enhancing the generalization abilities of robot policies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_RoboGround_Robotic_Manipulation_with_Grounded_Vision-Language_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_RoboGround_Robotic_Manipulation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Haifeng and Chen, Xinyi and Chen, Yilun and Li, Hao and Han, Xiaoshen and Wang, Zehan and Wang, Tai and Pang, Jiangmiao and Zhao, Zhou},
    title     = {RoboGround: Robotic Manipulation with Grounded Vision-Language Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22540-22550}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide
authors: Dohun Lee, Bryan Sangwoo Kim, Geon Yeong Park, Jong Chul Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2599-2608
cities: ["Daejeon", "Daejeon"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_VideoGuide_Improving_Video_Diffusion_Models_without_Training_Through_a_Teachers_CVPR_2025_paper.html

abstract:
Text-to-image (T2I) diffusion models have revolutionized visual content creation, but extending these capabilities to text-to-video (T2V) generation remains a challenge, particularly in preserving temporal consistency. Existing methods that aim to improve consistency often cause trade-offs such as reduced imaging quality and impractical computational time. To address these issues we introduce VideoGuide, a novel framework that enhances the temporal consistency of pretrained T2V models without the need for additional training or fine-tuning. Instead, VideoGuide leverages any pretrained video diffusion model (VDM) or itself as a guide during the early stages of inference, improving temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process. The proposed method brings about significant improvement in temporal consistency and image fidelity, providing a cost-effective and practical solution that synergizes the strengths of various video diffusion models. Furthermore, we demonstrate prior distillation, revealing that base models can achieve enhanced text coherence by utilizing the superior data prior of the guiding model through the proposed method. Project Page: https://dohunlee1.github.io/videoguide.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_VideoGuide_Improving_Video_Diffusion_Models_without_Training_Through_a_Teachers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_VideoGuide_Improving_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Dohun and Kim, Bryan Sangwoo and Park, Geon Yeong and Ye, Jong Chul},
    title     = {VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2599-2608}
}

raw_affiliation:
[{"affiliation": "Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "South Korea"}]

affiliations:
["Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)", "Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)"]


title: Improving Transferable Targeted Attacks with Feature Tuning Mixup
authors: Kaisheng Liang, Xuelong Dai, Yanjie Li, Dong Wang, Bin Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25802-25811
affiliations: ["Department of Computing, The Hong Kong Polytechnic University"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Improving_Transferable_Targeted_Attacks_with_Feature_Tuning_Mixup_CVPR_2025_paper.html

abstract:
Deep neural networks (DNNs) exhibit vulnerability to adversarial examples that can transfer across different DNN models. A particularly challenging problem is developing transferable targeted attacks that can mislead DNN models into predicting specific target classes. While various methods have been proposed to enhance attack transferability, they often incur substantial computational costs while yielding limited improvements. Recent clean feature mixup methods use random clean features to perturb the feature space but lack optimization for disrupting adversarial examples, overlooking the advantages of attack-specific perturbations. In this paper, we propose Feature Tuning Mixup (FTM), a novel method that enhances targeted attack transferability by combining both random and optimized noises in the feature space. FTM introduces learnable feature perturbations and employs an efficient stochastic update strategy for optimization. These learnable perturbations facilitate the generation of more robust adversarial examples with improved transferability. We further demonstrate that attack performance can be enhanced through an ensemble of multiple FTM-perturbed surrogate models. Extensive experiments on the ImageNet-compatible dataset across various DNN models demonstrate that our method achieves significant improvements over state-of-the-art methods while maintaining low computational cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Improving_Transferable_Targeted_Attacks_with_Feature_Tuning_Mixup_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Improving_Transferable_Targeted_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Kaisheng and Dai, Xuelong and Li, Yanjie and Wang, Dong and Xiao, Bin},
    title     = {Improving Transferable Targeted Attacks with Feature Tuning Mixup},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25802-25811}
}

raw_affiliation:
[{"affiliation": "Department of Computing, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]


title: OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye Cameras
authors: Jiaxi Deng, Yushen Wang, Haitao Meng, Zuoxun Hou, Yi Chang, Gang Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1003-1012
cities: ["Guangzhou", "Munich", "Beijing", "Wuhan"]
countries: ["China", "Germany", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_OmniStereo_Real-time_Omnidireactional_Depth_Estimation_with_Multiview_Fisheye_Cameras_CVPR_2025_paper.html

abstract:
Fast and reliable omnidirectional 3D sensing is essential to many applications such as autonomous driving, robotics and drone navigation. While many well-recognized methods have been developed to produce high-quality omnidirectional 3D information, they are too slow for real-time computation, limiting their feasibility in practical applications. Motivated by these shortcomings, we propose an efficient omnidirectional depth sensing framework, called OmniStereo, which generates high-quality 3D information in real-time. Unlike prior works, OmniStereo employs Cassini projection to simplify the photometric matching and introduces a lightweight stereo matching network to minimize computational overhead. Additionally, OmniStereo proposes a novel fusion method to handle depth discontinuities and invalid pixels complemented by a refinement module to reduce mapping-introduced errors and recover fine details. As a result, OmniStereo achieves state-of-the-art (SOTA) accuracy, surpassing the second-best method over 32% in MAE, while maintaining real-time efficiency. It operates more than 16.5xfaster than the second-best method in accuracy on TITAN RTX, achieving 12.3 FPS on embedded device Jetson AGX Orin, underscoring its suitability for real-world deployment. The code is available at https://github.com/DengJiaxi1/OmniStereo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_OmniStereo_Real-time_Omnidireactional_Depth_Estimation_with_Multiview_Fisheye_Cameras_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_OmniStereo_Real-time_Omnidireactional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Jiaxi and Wang, Yushen and Meng, Haitao and Hou, Zuoxun and Chang, Yi and Chen, Gang},
    title     = {OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1003-1012}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Beijing Institute of Space Mechanics and Electricity", "city": "Beijing", "country": "China"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Technical University of Munich", "Beijing Institute of Space Mechanics and Electricity", "Huazhong University of Science and Technology"]


title: DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery
authors: Jiadong Tang, Yu Gao, Dianyi Yang, Liqi Yan, Yufeng Yue, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 833-843
affiliations: ["Beijing Institute of Technology", "Hangzhou Dianzi University"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_DroneSplat_3D_Gaussian_Splatting_for_Robust_3D_Reconstruction_from_In-the-Wild_CVPR_2025_paper.html

abstract:
Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery.  However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_DroneSplat_3D_Gaussian_Splatting_for_Robust_3D_Reconstruction_from_In-the-Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_DroneSplat_3D_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Jiadong and Gao, Yu and Yang, Dianyi and Yan, Liqi and Yue, Yufeng and Yang, Yi},
    title     = {DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {833-843}
}

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


authors: ZaiPeng Duan, ChenXu Dang, Xuzhong Hu, Pei An, Junfeng Ding, Jie Zhan, YunBiao Xu, Jie Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6751-6760
affiliations: ["Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_SDGOCC_Semantic_and_Depth-Guided_Birds-Eye_View_Transformation_for_3D_Multimodal_CVPR_2025_paper.html

title:
SDGOCC: Semantic and Depth-Guided Bird's-Eye View Transformation for 3D Multimodal Occupancy Prediction

abstract:
Multimodal 3D occupancy prediction has garnered significant attention for its potential in autonomous driving. However, most existing approaches are single-modality: camera-based methods lack depth information, while LiDAR-based methods struggle with occlusions. Current lightweight methods primarily rely on the Lift-Splat-Shoot (LSS) pipeline, which suffers from inaccurate depth estimation and fails to fully exploit the geometric and semantic information of 3D LiDAR points. Therefore, we propose a novel multimodal occupancy prediction network called SDG-OCC, which incorporates a joint semantic and depth-guided view transformation coupled with a fusion-to-occupancy-driven active distillation. The enhanced view transformation constructs accurate depth distributions by integrating pixel semantics and co-point depth through diffusion and bilinear discretization. The fusion-to-occupancy-driven active distillation extracts rich semantic information from multimodal data and selectively transfers knowledge to image features based on LiDAR-identified regions. Finally, for optimal performance, we introduce SDG-Fusion, which uses fusion alone, and SDG-KL, which integrates both fusion and distillation for faster inference. Our method achieves state-of-the-art (SOTA) performance with real-time processing on the Occ3D-nuScenes dataset and shows comparable performance on the more challenging SurroundOcc-nuScenes dataset, demonstrating its effectiveness and robustness. The code will be released at https://github.com/DzpLab/SDGOCC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_SDGOCC_Semantic_and_Depth-Guided_Birds-Eye_View_Transformation_for_3D_Multimodal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_SDGOCC_Semantic_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, ZaiPeng and Dang, ChenXu and Hu, Xuzhong and An, Pei and Ding, Junfeng and Zhan, Jie and Xu, YunBiao and Ma, Jie},
    title     = {SDGOCC: Semantic and Depth-Guided Bird's-Eye View Transformation for 3D Multimodal Occupancy Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6751-6760}
}

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


title: DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27531-27541
cities: ["Macau", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_DrivingSphere_Building_a_High-fidelity_4D_World_for_Closed-loop_Simulation_CVPR_2025_paper.html

authors:
Tianyi Yan, Dongming Wu, Wencheng Han, Junpeng Jiang, Xia Zhou, Kun Zhan, Cheng-zhong Xu, Jianbing Shen

abstract:
Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealistic data, i.e., open-loop simulation usually lacks the ability to assess dynamic decision-making. While the recent efforts of closed-loop simulation offer feedback-driven environments, they cannot process visual sensor inputs or produce outputs that differ from real-world data. To address these challenges, we propose DrivingSphere, a realistic and closed-loop simulation framework. Its core idea is to build 4D world representation and generate real-life and controllable driving scenarios. In specific, our framework includes a Dynamic Environment Composition module that constructs a detailed 4D driving world with a format of occupancy equipping with static backgrounds and dynamic objects, and a Visual Scene Synthesis module that transforms this data into high-fidelity, multi-view video outputs, ensuring spatial and temporal consistency. By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms, ultimately advancing the development of more reliable autonomous cars.The benchmark will be publicly released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_DrivingSphere_Building_a_High-fidelity_4D_World_for_Closed-loop_Simulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_DrivingSphere_Building_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Tianyi and Wu, Dongming and Han, Wencheng and Jiang, Junpeng and Zhou, Xia and Zhan, Kun and Xu, Cheng-zhong and Shen, Jianbing},
    title     = {DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27531-27541}
}

raw_affiliation:
[{"affiliation": "SKL-IOTSC, Computer and Information Science, University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Li Auto Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science, Beijing Institute of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["SKL-IOTSC, Computer and Information Science, University of Macau", "Li Auto Inc.", "School of Computer Science, Beijing Institute of Technology"]


authors: Yanfeng Zhou, Lingrui Li, Le Lu, Minfeng Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20852-20862
affiliations: ["DAMO Academy, Alibaba Group", "School of Computer Science, University of Nottingham"]
cities: ["Hangzhou", "Nottingham"]
countries: ["China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_nnWNet_Rethinking_the_Use_of_Transformers_in_Biomedical_Image_Segmentation_CVPR_2025_paper.html

title:
nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark

abstract:
Semantic segmentation is a crucial prerequisite in clinical applications and computer-aided diagnosis. With the development of deep neural networks, biomedical image segmentation has achieved remarkable success. Encoder-decoder architectures that integrate convolutions and transformers are gaining attention for their potential to capture both global and local features. However, current designs face the contradiction that these two features cannot be continuously transmitted. In addition, some models lack a unified and standardized evaluation benchmark, leading to significant discrepancies in the experimental setup. In this study, we review and summarize these architectures and analyze their contradictions in design. We modify UNet and propose WNet to combine transformers and convolutions, addressing the transmission issue effectively. WNet captures long-range dependencies and local details simultaneously while ensuring their continuous transmission and multi-scale fusion. We integrate WNet into the nnUNet framework for unified benchmarking. Our model achieves state-of-the-art performance in biomedical image segmentation. Extensive experiments demonstrate their effectiveness on four 2D datasets (DRIVE, ISIC-2017, Kvasir-SEG, and CREMI) and four 3D datasets (Parse2022, AMOS22, BTCV, and ImageCAS). The code is available at https://github.com/Yanfeng-Zhou/nnWNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_nnWNet_Rethinking_the_Use_of_Transformers_in_Biomedical_Image_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_nnWNet_Rethinking_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yanfeng and Li, Lingrui and Lu, Le and Xu, Minfeng},
    title     = {nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20852-20862}
}

raw_affiliation:
[{"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "School of Computer Science, University of Nottingham", "city": "Nottingham", "country": "UK"}]


title: Efficient Video Face Enhancement with Enhanced Spatial-Temporal Consistency
authors: Yutong Wang, Jiajie Teng, Jiajiong Cao, Yuming Li, Chenguang Ma, Hongteng Xu, Dixin Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2183-2193
affiliations: ["Beijing Institute of Technology", "Zhejiang University", "Renmin University of China"]
cities: ["Beijing", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Efficient_Video_Face_Enhancement_with_Enhanced_Spatial-Temporal_Consistency_CVPR_2025_paper.html

abstract:
As a very common type of video, face videos often appear in movies, talk shows, live broadcasts, and other scenes. Real-world online videos are often plagued by degradations such as blurring and quantization noise, due to the high compression ratio caused by high communication costs and limited transmission bandwidth. These degradations have a particularly serious impact on face videos because the human visual system is highly sensitive to facial details.Despite the significant advancement in video face enhancement, current methods still suffer from i) long processing time and ii) inconsistent spatial-temporal visual effects (e.g., flickering). This study proposes a novel and efficient blind video face enhancement method to overcome the above two challenges, restoring high-quality videos from their compressed low-quality versions with an effective de-flickering mechanism. In particular, the proposed method develops upon a 3D-VQGAN backbone associated with spatial-temporal codebooks recording high-quality portrait features and residual-based temporal information. We develop a two-stage learning framework for the model. In Stage I, we learn the model with a regularizer mitigating the codebook collapse problem.In Stage II, we learn two transformers to lookup code from the codebooks and further update the encoder of low-quality videos.Experiments conducted on the VFHQ-Test dataset demonstrate that our method surpasses the current state-of-the-art blind face video restoration and de-flickering methods on both efficiency and effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Efficient_Video_Face_Enhancement_with_Enhanced_Spatial-Temporal_Consistency_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yutong and Teng, Jiajie and Cao, Jiajiong and Li, Yuming and Ma, Chenguang and Xu, Hongteng and Luo, Dixin},
    title     = {Efficient Video Face Enhancement with Enhanced Spatial-Temporal Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2183-2193}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}]


title: VELOCITI: Benchmarking Video-Language Compositional Reasoning with Strict Entailment
authors: Darshana Saravanan, Varun Gupta, Darshan Singh, Zeeshan Khan, Vineet Gandhi, Makarand Tapaswi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18914-18924
affiliations: ["CVIT, IIIT Hyderabad, India", "Inria, Paris, France"]
cities: ["Hyderabad", "Paris"]
countries: ["India", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Saravanan_VELOCITI_Benchmarking_Video-Language_Compositional_Reasoning_with_Strict_Entailment_CVPR_2025_paper.html

abstract:
A fundamental aspect of compositional reasoning in a video is associating people and their actions across time. Recent years have seen great progress in general-purpose vision/video models and a move towards long-video understanding. While exciting, we take a step back and ask: are today's models good at compositional reasoning on short videos? To this end, we introduce VELOCITI, a benchmark to study Video-LLMs by disentangling and assessing the comprehension of agents, actions, and their associations across multiple events. We adopt the Video-Language Entailment setup and propose StrictVLE that requires correct classification (rather than ranking) of the positive and negative caption. We evaluate several models and observe that even the best, LLaVA-OneVision (44.5%) and Gemini-1.5-Pro (49.3%), are far from human accuracy at 93.0%. Results show that action understanding lags behind agents, and negative captions created using entities appearing in the video perform worse than those obtained from pure text manipulation. We also present challenges with ClassicVLE and multiple-choice (MC) evaluation, strengthening our preference for StrictVLE. Finally, we validate that our benchmark requires visual inputs of multiple frames making it ideal to study video-language compositional reasoning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Saravanan_VELOCITI_Benchmarking_Video-Language_Compositional_Reasoning_with_Strict_Entailment_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Saravanan_2025_CVPR,
    author    = {Saravanan, Darshana and Gupta, Varun and Singh, Darshan and Khan, Zeeshan and Gandhi, Vineet and Tapaswi, Makarand},
    title     = {VELOCITI: Benchmarking Video-Language Compositional Reasoning with Strict Entailment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18914-18924}
}

raw_affiliation:
[{"affiliation": "CVIT, IIIT Hyderabad, India", "city": "Hyderabad", "country": "India"}, {"affiliation": "Inria, Paris, France", "city": "Paris", "country": "France"}]


title: Seeing is Not Believing: Adversarial Natural Object Optimization for Hard-Label 3D Scene Attacks
authors: Daizong Liu, Wei Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11886-11897
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Seeing_is_Not_Believing_Adversarial_Natural_Object_Optimization_for_Hard-Label_CVPR_2025_paper.html

abstract:
Deep learning models for 3D data have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving and robotic navigation. Existing 3D attackers mainly put effort into attacking the simple 3D classification model by perturbing point cloud objects in the white/black-box setting. However, real-world 3D applications focus on tackling more complicated scene-based data while sharing no information about the model parameters and logits with users. Therefore, directly applying previous naive 3D attack methods to these applications does not work. To this end, this paper attempts to address the challenging hard-label 3D scene attack with access only to the input/output of the 3D models. To make the attack effective and stealthy, we propose to generate universal adversarial objects, which will mislead scene-aware 3D models to predict attacker-chosen labels whenever these objects are placed on any scene input. Specifically, we inject an imperceptible object trigger with further perturbations into all scenes and learn to mislead their reasoning by only querying the 3D model. We start by initializing the trigger pattern with a realistic object and searching for an appropriate location to place it naturally in the scene data. Then, we design a novel weighted gradient estimation strategy to perturb the object trigger with additive slight noise to make them adversarial in an iterative optimization procedure. Extensive experiments demonstrate that our attack can achieve superior performance on seven 3D models and three scene-based datasets, with satisfactory adversarial imperceptibility and strong resistance to defense methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Seeing_is_Not_Believing_Adversarial_Natural_Object_Optimization_for_Hard-Label_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Seeing_is_Not_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Daizong and Hu, Wei},
    title     = {Seeing is Not Believing: Adversarial Natural Object Optimization for Hard-Label 3D Scene Attacks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11886-11897}
}


title: IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation
authors: Yiren Song, Pei Yang, Hai Ci, Mike Zheng Shou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3019-3028
affiliations: ["Show Lab, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_IDProtector_An_Adversarial_Noise_Encoder_to_Protect_Against_ID-Preserving_Image_CVPR_2025_paper.html

abstract:
Recently, zero-shot methods like InstantID have revolutionized identity-preserving generation. Unlike multi-image finetuning approaches such as DreamBooth, these zero-shot methods leverage powerful facial encoders to extract identity information from a single portrait photo, enabling efficient identity-preserving generation through a single inference pass. However, this convenience introduces new threats to the facial identity protection. This paper aims to safeguard portrait photos from unauthorized encoder-based customization. We introduce IDProtector, an adversarial noise encoder that applies imperceptible adversarial noise to portrait photos in a single forward pass. Our approach offers universal protection for portraits against multiple state-of-the-art encoder-based methods, including InstantID, IP-Adapter, and PhotoMaker, while ensuring robustness to common image transformations such as JPEG compression, resizing, and affine transformations. Experiments across diverse portrait datasets and generative models reveal that IDProtector generalizes effectively to unseen data and even closed-source proprietary models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_IDProtector_An_Adversarial_Noise_Encoder_to_Protect_Against_ID-Preserving_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_IDProtector_An_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Yiren and Yang, Pei and Ci, Hai and Shou, Mike Zheng},
    title     = {IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3019-3028}
}

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


title: HuPerFlow: A Comprehensive Benchmark for Human vs. Machine Motion Estimation Comparison
authors: Yung-Hao Yang, Zitang Sun, Taiki Fukiage, Shin'ya Nishida
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22799-22808
cities: ["Kyoto", "Tokyo"]
countries: ["Japan", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_HuPerFlow_A_Comprehensive_Benchmark_for_Human_vs._Machine_Motion_Estimation_CVPR_2025_paper.html

abstract:
As AI models are increasingly integrated into applications involving human interaction, understanding the alignment between human perception and machine vision has become essential. One example is the estimation of visual motion (optical flow) in dynamic applications such as driving assistance. While there are numerous optical flow datasets and benchmarks with ground truth information, human-perceived flow in natural scenes remains underexplored. We introduce HuPerFlow--a benchmark for human-perceived flow, measured at 2,400 locations across ten optical flow datasets, with  38,400 response vectors collected through online psychophysical experiments. Our data demonstrate that human-perceived flow aligns with ground truth in spatiotemporally smooth locations while also showing systematic errors influenced by various environmental properties. Additionally, we evaluated several optical flow algorithms against human-perceived flow, uncovering both similarities and unique aspects of human perception in complex natural scenes. HuPerFlow is the first large-scale human-perceived flow benchmark for alignment between computer vision models and human perception, as well as for scientific exploration of human motion perception in natural scenes. The HuPerFlow benchmark will be available online upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_HuPerFlow_A_Comprehensive_Benchmark_for_Human_vs._Machine_Motion_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_HuPerFlow_A_Comprehensive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yung-Hao and Sun, Zitang and Fukiage, Taiki and Nishida, Shin'ya},
    title     = {HuPerFlow: A Comprehensive Benchmark for Human vs. Machine Motion Estimation Comparison},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22799-22808}
}

raw_affiliation:
[{"affiliation": "Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Japan", "city": "Kyoto", "country": "Japan"}, {"affiliation": "Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan", "city": "Tokyo", "country": "Japan"}]

affiliations:
["Cognitive Informatics Lab, Graduate School of Informatics, Kyoto University, Japan", "Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan"]


title: LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
authors: Christoforos N. Spartalis, Theodoros Semertzidis, Efstratios Gavves, Petros Daras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10046-10055
affiliations: ["University of Amsterdam", "Centre for Research & Technology Hellas", "Archimedes/Athena RC"]
cities: ["Amsterdam", "Thessaloniki", "Athens"]
countries: ["Netherlands", "Greece", "Greece"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Spartalis_LoTUS_Large-Scale_Machine_Unlearning_with_a_Taste_of_Uncertainty_CVPR_2025_paper.html

abstract:
We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from scratch. LoTUS smooths the prediction probabilities of the model up to an information-theoretic bound, mitigating its over-confidence stemming from data memorization. We evaluate LoTUS on Transformer and ResNet18 models against eight baselines across five public datasets. Beyond established MU benchmarks, we evaluate unlearning on ImageNet1k, a large-scale dataset, where retraining is impractical, simulating real-world conditions. Moreover, we introduce the novel Retrain-Free Jensen-Shannon Divergence (RF-JSD) metric to enable evaluation under real-world conditions. The experimental results show that LoTUS outperforms state-of-the-art methods in terms of both efficiency and effectiveness. Code: https://github.com/cspartalis/LoTUS

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Spartalis_LoTUS_Large-Scale_Machine_Unlearning_with_a_Taste_of_Uncertainty_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Spartalis_LoTUS_Large-Scale_Machine_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Spartalis_2025_CVPR,
    author    = {Spartalis, Christoforos N. and Semertzidis, Theodoros and Gavves, Efstratios and Daras, Petros},
    title     = {LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10046-10055}
}

raw_affiliation:
[{"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "Centre for Research & Technology Hellas", "city": "Thessaloniki", "country": "Greece"}, {"affiliation": "Archimedes/Athena RC", "city": "Athens", "country": "Greece"}]


title: SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models
authors: Zilan Wang, Junfeng Guo, Jiacheng Zhu, Yiming Li, Heng Huang, Muhao Chen, Zhengzhong Tu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8213-8224
affiliations: ["NTU", "University of Maryland", "MIT CSAIL", "UC Davis", "Texas A&M University"]
cities: ["Singapore", "College Park", "Cambridge", "Davis", "College Station"]
countries: ["Singapore", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SleeperMark_Towards_Robust_Watermark_against_Fine-Tuning_Text-to-image_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advances in large-scale text-to-image (T2I) diffusion models have enabled a variety of downstream applications. As T2I models require extensive resources for training, they constitute highly valued intellectual property (IP) for their legitimate owners, yet making them incentive targets for unauthorized fine-tuning by adversaries seeking to leverage these models for customized, usually profitable applications. Existing IP protection methods for diffusion models generally involve embedding watermark patterns and then verifying ownership through generated outputs examination, or inspecting the model's feature space. However, these techniques are inherently ineffective in practical scenarios when the watermarked model undergoes fine-tuning, and the feature space is inaccessible during verification (i.e., black-box setting). The model is prone to forgetting the previously learned watermark knowledge when it adapts to a new task. To address this challenge, we propose SleeperMark, a novel framework designed to embed resilient watermarks into T2I diffusion models. SleeperMark explicitly guides the model to disentangle the watermark information from the semantic concepts it learns, allowing the model to retain the embedded watermark while continuing to be adapted to new downstream tasks. Our extensive experiments demonstrate the effectiveness of SleeperMark across various types of diffusion models, including latent diffusion models (e.g., Stable Diffusion) and pixel diffusion models (e.g., DeepFloyd-IF), showing robustness against downstream fine-tuning and various attacks at both the image and model levels, with minimal impact on the model's generative capability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SleeperMark_Towards_Robust_Watermark_against_Fine-Tuning_Text-to-image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SleeperMark_Towards_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zilan and Guo, Junfeng and Zhu, Jiacheng and Li, Yiming and Huang, Heng and Chen, Muhao and Tu, Zhengzhong},
    title     = {SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8213-8224}
}

raw_affiliation:
[{"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "MIT CSAIL", "city": "Cambridge", "country": "USA"}, {"affiliation": "UC Davis", "city": "Davis", "country": "USA"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]


authors: Zheda Mai, Ping Zhang, Cheng-Hao Tu, Hong-You Chen, Quang-Huy Nguyen, Li Zhang, Wei-Lun Chao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14845-14857
affiliations: ["The Ohio State University", "Google Research"]
cities: ["Columbus", "Mountain View"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mai_Lessons_and_Insights_from_a_Unifying_Study_of_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.html

title:
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition

abstract:
Parameter-efficient fine-tuning (PEFT) has attracted significant attention due to the growth of pre-trained model sizes and the need to fine-tune (FT) them for superior downstream performance. Despite a surge in new PEFT methods, a systematic study to understand their performance and suitable application scenarios is lacking, leaving questions like "when to apply PEFT" and "which method to use" largely unanswered, especially in visual recognition. In this paper, we conduct a unifying empirical study of representative PEFT methods with Vision Transformers. We systematically tune their hyperparameters to fairly compare their accuracy on downstream tasks. Our study offers a practical user guide and unveils several new insights. First, if tuned carefully, different PEFT methods achieve similar accuracy in the low-shot benchmark VTAB-1K. This includes simple approaches like FT the bias terms that were reported inferior. Second, despite similar accuracy, we find that PEFT methods make different mistakes and high-confidence predictions, likely due to their different inductive biases. Such an inconsistency (or complementarity) opens up the opportunity for ensemble methods, and we make preliminary attempts at this. Third, going beyond the commonly used low-shot tasks, we find that PEFT is also useful in many-shot regimes, achieving comparable or better accuracy than full FT while using significantly fewer parameters. Lastly, we investigate PEFT's ability to preserve a pre-trained model's robustness to distribution shifts (e.g., CLIP). Perhaps not surprisingly, PEFT approaches outperform full FT alone. However, with weight-space ensembles, full FT  can better balance target distribution and distribution shift performance, suggesting a future research direction for robust PEFT. The code is available at https://github.com/OSU-MLB/ViT_PEFT_Vision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mai_Lessons_and_Insights_from_a_Unifying_Study_of_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mai_Lessons_and_Insights_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mai_2025_CVPR,
    author    = {Mai, Zheda and Zhang, Ping and Tu, Cheng-Hao and Chen, Hong-You and Nguyen, Quang-Huy and Zhang, Li and Chao, Wei-Lun},
    title     = {Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14845-14857}
}

raw_affiliation:
[{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}]


title: Pippo: High-Resolution Multi-View Humans from a Single Image
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16418-16429
affiliations: ["Meta Reality Labs", "University of Toronto", "Vector Institute", "UC Berkeley"]
cities: ["Menlo Park", "Toronto", "Toronto", "Berkeley"]
countries: ["USA", "Canada", "Canada", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kant_Pippo_High-Resolution_Multi-View_Humans_from_a_Single_Image_CVPR_2025_paper.html

authors:
Yash Kant, Ethan Weber, Jin Kyu Kim, Rawal Khirodkar, Su Zhaoen, Julieta Martinez, Igor Gilitschenski, Shunsuke Saito, Timur Bagautdinov

abstract:
We present Pippo, a generative model capable of producing 1K resolution dense turnaround videos of a person from a single casually clicked photo. Pippo is a multi-view diffusion transformer and does not require any additional inputs - e.g., a fitted parametric model or camera parameters of the input image. We pre-train Pippo on 3B human images without captions, and conduct multi-view mid-training and post-training on studio captured humans. During mid-training, to quickly absorb the studio dataset, we denoise several (up to 48) views at low-resolution, and encode target cameras coarsely using a shallow MLP. During post-training, we denoise fewer views at high-resolution and use pixel-aligned controls (e.g., Spatial anchor and Plucker rays) to enable 3D consistent generations. At inference, we propose an attention biasing technique that allows Pippo to simultaneously generate greater than 5 times as many views as seen during training. Finally, we also introduce an improved metric to evaluate 3D consistency of multi-view generations, and show that Pippo outperforms existing works on multi-view human generation from a single image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kant_Pippo_High-Resolution_Multi-View_Humans_from_a_Single_Image_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Kant_2025_CVPR,
    author    = {Kant, Yash and Weber, Ethan and Kim, Jin Kyu and Khirodkar, Rawal and Zhaoen, Su and Martinez, Julieta and Gilitschenski, Igor and Saito, Shunsuke and Bagautdinov, Timur},
    title     = {Pippo: High-Resolution Multi-View Humans from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16418-16429}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]


title: H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detection
authors: Yuhang Liu, Wenjie Zhao, Yunhui Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15413-15423
affiliations: ["University of Electronic Science and Technology of China", "University of Texas at Dallas"]
cities: ["Chengdu", "Richardson"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_H2ST_Hierarchical_Two-Sample_Tests_for_Continual_Out-of-Distribution_Detection_CVPR_2025_paper.html

abstract:
Task Incremental Learning (TIL) is a specialized form of Continual Learning (CL) in which a model incrementally learns from non-stationary data streams. Existing TIL methodologies operate under the closed-world assumption, presuming that incoming data remains in-distribution (ID). However, in an open-world setting, incoming samples may originate from out-of-distribution (OOD) sources, with their task identities inherently unknown. Continually detecting OOD samples presents several challenges for current OOD detection methods: reliance on model outputs leads to excessive dependence on model performance, selecting suitable thresholds is difficult, hindering real-world deployment, and binary ID/OOD classification fails to provide task-level identification. To address these issues, we propose a novel continual OOD detection method called the Hierarchical Two-sample Tests (H2ST). H2ST eliminates the need for threshold selection through hypothesis testing and utilizes feature maps to better exploit model capabilities without excessive dependence on model performance. The proposed hierarchical architecture enables task-level detection with superior performance and lower overhead compared to non-hierarchical classifier two-sample tests. Extensive experiments and analysis validate the effectiveness of H2ST in open-world TIL scenarios and its superiority to the existing methods. Code is available at https://github.com/YuhangLiuu/H2ST.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_H2ST_Hierarchical_Two-Sample_Tests_for_Continual_Out-of-Distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_H2ST_Hierarchical_Two-Sample_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuhang and Zhao, Wenjie and Guo, Yunhui},
    title     = {H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15413-15423}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "University of Texas at Dallas", "city": "Richardson", "country": "USA"}]


title: MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning
authors: Wenhao Gu, Li Gu, Chingyee Yee Suen, Yang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23494-23504
affiliations: ["Department of Computer Science and Software Engineering, Concordia University"]
cities: ["Montreal"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_MetaWriter_Personalized_Handwritten_Text_Recognition_Using_Meta-Learned_Prompt_Tuning_CVPR_2025_paper.html

abstract:
Recent advancements in handwritten text recognition (HTR) have enabled the effective conversion of handwritten text to digital formats. However, achieving robust recognition across diverse writing styles remains challenging. Traditional HTR methods lack writer-specific personalization at test time due to limitations in model architecture and training strategies. Existing attempts to bridge this gap, through gradient-based meta-learning, still require labeled examples and suffer from parameter-inefficient fine-tuning, leading to substantial computational and memory overhead. To overcome these challenges, we propose an efficient framework that formulates personalization as prompt tuning, incorporating an auxiliary image reconstruction task with a self-supervised loss to guide prompt adaptation with unlabeled test-time examples. To ensure self-supervised loss effectively minimizes text recognition error, we leverage meta-learning to learn the optimal initialization of the prompts. As a result, our method allows the model to efficiently capture unique writing styles by updating less than 1% of its parameters and eliminating the need for time-intensive annotation processes. We validate our approach on the RIMES and IAM Handwriting Database benchmarks, where it consistently outperforms previous state-of-the-art methods while using 20x fewer parameters. We believe this represents a significant advancement in personalized handwritten text recognition, paving the way for more reliable and practical deployment in resource-constrained scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_MetaWriter_Personalized_Handwritten_Text_Recognition_Using_Meta-Learned_Prompt_Tuning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Wenhao and Gu, Li and Suen, Chingyee Yee and Wang, Yang},
    title     = {MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23494-23504}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Software Engineering, Concordia University", "city": "Montreal", "country": "Canada"}]


title: Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
authors: Jeimin Jeon, Youngmin Oh, Junghyup Lee, Donghyeon Baek, Dohyung Kim, Chanho Eom, Bumsub Ham
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30137-30146
cities: ["Seoul", "Seoul", "Seoul", "Suwon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeon_Subnet-Aware_Dynamic_Supernet_Training_for_Neural_Architecture_Search_CVPR_2025_paper.html

abstract:
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training strategy (e.g., using the same learning rate (LR) scheduler and optimizer for all subnets). This, however, does not consider that individual subnets have distinct characteristics, leading to two problems: (1) The supernet training is biased towards the low-complexity subnets (unfairness); (2) the momentum update in the supernet is noisy (noisy momentum). We present a dynamic supernet training technique to address these problems by adjusting the training strategy adaptive to the subnets. Specifically, we introduce a complexity-aware LR scheduler (CaLR) that controls the decay ratio of LR adaptive to the complexities of subnets, which alleviates the unfairness problem. We also present a momentum separation technique (MS). It groups the subnets with similar structural characteristics and uses a separate momentum for each group, avoiding the noisy momentum problem. Our approach can be applicable to various N-shot NAS methods with marginal cost, while improving the search performance drastically. We validate the effectiveness of our approach on various search spaces (e.g., NAS-Bench-201, Mobilenet spaces) and datasets (e.g., CIFAR-10/100, ImageNet).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeon_Subnet-Aware_Dynamic_Supernet_Training_for_Neural_Architecture_Search_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeon_Subnet-Aware_Dynamic_Supernet_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeon_2025_CVPR,
    author    = {Jeon, Jeimin and Oh, Youngmin and Lee, Junghyup and Baek, Donghyeon and Kim, Dohyung and Eom, Chanho and Ham, Bumsub},
    title     = {Subnet-Aware Dynamic Supernet Training for Neural Architecture Search},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30137-30146}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Articron Inc.", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Samsung Research", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Samsung Advanced Institute of Technology", "city": "Suwon", "country": "South Korea"}, {"affiliation": "Chung-Ang University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Yonsei University", "Articron Inc.", "Samsung Research", "Samsung Advanced Institute of Technology", "Chung-Ang University"]


title: MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
authors: Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, Shanghang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19846-19856
cities: ["Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_MoVE-KD_Knowledge_Distillation_for_VLMs_with_Mixture_of_Visual_Encoders_CVPR_2025_paper.html

abstract:
Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models. To leverage the various capabilities of these encoders, recent studies incorporate multiple encoders within a single VLM, leading to a considerable increase in computational cost. In this paper, we present Mixture-of-Visual-Encoder Knowledge Distillation (MoVE-KD), a novel framework that distills the unique proficiencies of multiple vision encoders into a single, efficient encoder model. Specifically, to mitigate conflicts and retain the unique characteristics of each teacher encoder, we employ low-rank adaptation (LoRA) and mixture-of-experts (MoEs) to selectively activate specialized knowledge based on input features, enhancing both adaptability and efficiency. To regularize the KD process and enhance performance, we propose an attention-based distillation strategy that adaptively weighs the different encoders and emphasizes valuable visual tokens, reducing the burden of replicating comprehensive but distinct features from multiple teachers. Comprehensive experiments on popular VLMs, such as LLaVA and LLaVA-NeXT, validate the effectiveness of our method. Our code is available at:https://github.com/hey-cjj/MoVE-KD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_MoVE-KD_Knowledge_Distillation_for_VLMs_with_Mixture_of_Visual_Encoders_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Jiajun and Zhang, Yuan and Huang, Tao and Lu, Ming and Zhang, Qizhe and An, Ruichuan and Ma, Ningning and Zhang, Shanghang},
    title     = {MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19846-19856}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Autonomous Driving Development, NIO", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "Autonomous Driving Development, NIO", "Shanghai Jiao Tong University"]


title: CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model
authors: Xiaoding Yuan, Shitao Tang, Kejie Li, Peng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16408-16417
affiliations: ["Johns Hopkins University", "Simon Fraser University", "ByteDance", "ByteDance"]
cities: ["Baltimore", "Burnaby", "Beijing", "Beijing"]
countries: ["USA", "Canada", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_CamFreeDiff_Camera-free_Image_to_Panorama_Generation_with_Diffusion_Model_CVPR_2025_paper.html

abstract:
This paper introduces Camera-free Diffusion (CamFreeDiff) model for 360^\circ image outpainting from a single camera-free image and text description. This method distinguishes itself from existing strategies, such as MVDiffusion, by eliminating the requirement for predefined camera poses. CamFreeDiff seamlessly incorporates a mechanism for predicting homography within the multi-view diffusion framework. The key component of our approach is to formulate camera estimation by directly predicting the homography transformation from the input view to the predefined canonical view. In contrast to the direct two-stage approach of image transformation and outpainting, CamFreeDiff utilizes predicted homography to establish point-level correspondences between the input view and the target panoramic view. This enables consistency through correspondence-aware attention, which is learned in a fully differentiable manner. Qualitative and quantitative experimental results demonstrate the strong robustness and performance of CamFreeDiff for 360^\circ image outpainting in the challenging context of camera-free inputs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_CamFreeDiff_Camera-free_Image_to_Panorama_Generation_with_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_CamFreeDiff_Camera-free_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Xiaoding and Tang, Shitao and Li, Kejie and Wang, Peng},
    title     = {CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16408-16417}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Simon Fraser University", "city": "Burnaby", "country": "Canada"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]


authors: Yuxuan Gu, Haoxuan Wang, Pengyang Ling, Zhixiang Wei, Huaian Chen, Yi Jin, Enhong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16071-16080
affiliations: ["University of Science and Technology of China", "Shanghai AI Laboratory"]
cities: ["Hefei", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_Improving_Visual_and_Downstream_Performance_of_Low-Light_Enhancer_with_Vision_CVPR_2025_paper.html

title:
Improving Visual and Downstream Performance of Low-Light Enhancer with Vision Foundation Models Collaboration

abstract:
In this paper, we observe that the collaboration of various foundation models can perceive semantic and degraded information within images, thereby guiding the low-light enhancement process. Specifically, we propose a self-supervised low-light enhancement framework based on the multiple foundation models collaboration (dubbed FoCo), aimed at improving both the visual quality of enhanced images and the performance in high-level applications. At the feature level, FoCo leverages the rich features from various foundation models to enhance the model's semantic perception during training, thereby reducing the gap between enhanced results and high-quality images from a high-level perspective. At the task level, we exploit the robustness-gap between strong foundation models and weak models, applying high-level task guidance to the low-light enhancement training process. Through the collaboration of multiple foundation models, the proposed framework shows better enhancement performance and adapts better to high-level tasks. Extensive experiments across various enhancement and application benchmarks demonstrate the qualitative and quantitative superiority of the proposed method over numerous state-of-the-art techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_Improving_Visual_and_Downstream_Performance_of_Low-Light_Enhancer_with_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_Improving_Visual_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Yuxuan and Wang, Haoxuan and Ling, Pengyang and Wei, Zhixiang and Chen, Huaian and Jin, Yi and Chen, Enhong},
    title     = {Improving Visual and Downstream Performance of Low-Light Enhancer with Vision Foundation Models Collaboration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16071-16080}
}

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


title: EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance
authors: Yang Yue, Yulin Wang, Haojun Jiang, Pan Liu, Shiji Song, Gao Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25993-26003
affiliations: ["Tsinghua University", "PLA General Hospital"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yue_EchoWorld_Learning_Motion-Aware_World_Models_for_Echocardiography_Probe_Guidance_CVPR_2025_paper.html

abstract:
Echocardiography is crucial for cardiovascular disease detection but relies heavily on experienced sonographers. Echocardiography probe guidance systems, which provide real-time movement instructions for acquiring standard plane images, offer a promising solution for AI-assisted or fully autonomous scanning. However, developing effective machine learning models for this task remains challenging, as they must grasp heart anatomy and the intricate interplay between probe motion and visual signals. To address this, we present EchoWorld, a motion-aware world modeling framework for probe guidance that encodes anatomical knowledge and motion-induced visual dynamics, while effectively leveraging past visual-motion sequences to enhance guidance precision. EchoWorld employs a pre-training strategy inspired by world modeling principles, where the model predicts masked anatomical regions and simulates the visual outcomes of probe adjustments. Built upon this pre-trained model, we introduce a motion-aware attention mechanism in the fine-tuning stage that effectively integrates historical visual-motion data, enabling precise and adaptive probe guidance. Trained on more than one million ultrasound images from over 200 routine scans, EchoWorld effectively captures key echocardiographic knowledge, as validated by qualitative analysis. Moreover, our method significantly reduces guidance errors compared to existing visual backbones and guidance frameworks, excelling in both single-frame and sequential evaluation protocols. Code is available at https://github.com/LeapLabTHU/EchoWorld.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yue_EchoWorld_Learning_Motion-Aware_World_Models_for_Echocardiography_Probe_Guidance_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Yue_2025_CVPR,
    author    = {Yue, Yang and Wang, Yulin and Jiang, Haojun and Liu, Pan and Song, Shiji and Huang, Gao},
    title     = {EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25993-26003}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "PLA General Hospital", "city": "Beijing", "country": "China"}]


title: Controllable Human Image Generation with Personalized Multi-Garments
authors: Yisol Choi, Sangkyung Kwak, Sihyun Yu, Hyungwon Choi, Jinwoo Shin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28736-28747
affiliations: ["KAIST", "OMNIOUS.AI", "Scaled Foundations"]
cities: ["Daejeon", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_Controllable_Human_Image_Generation_with_Personalized_Multi-Garments_CVPR_2025_paper.html

abstract:
We present BootControl, a novel framework based on text-to-image diffusion models for controllable human image generation with multiple reference garments.Here, the main bottleneck is data acquisition for training: collecting a large-scale dataset of high-quality reference garment images per human subject is quite challenging, i.e., ideally, one needs to manually gather every single garment photograph worn by each human.To address this, we propose a data generation pipeline to construct a large synthetic dataset, consisting of human and multiple-garment pairs, by introducing a model to extract any reference garment images from each human image.To ensure data quality, we also propose a filtering strategy to remove undesirable generated data based on measuring perceptual similarities between the garment presented in human image and extracted garment.Finally, by utilizing the constructed synthetic dataset, we train a diffusion model having two parallel denoising paths that use multiple garment images as conditions to generate human images while preserving their fine-grained details.We further show the wide-applicability of our framework by adapting it to different types of reference-based generation in the fashion domain, including virtual try-on, and controllable human image generation with other conditions, e.g., pose, face, etc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_Controllable_Human_Image_Generation_with_Personalized_Multi-Garments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_Controllable_Human_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Yisol and Kwak, Sangkyung and Yu, Sihyun and Choi, Hyungwon and Shin, Jinwoo},
    title     = {Controllable Human Image Generation with Personalized Multi-Garments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28736-28747}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "OMNIOUS.AI", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Scaled Foundations", "city": "Seoul", "country": "South Korea"}]


title: FineLIP: Extending CLIP's Reach via Fine-Grained Alignment with Longer Text Inputs
authors: Mothilal Asokan, Kebin Wu, Fatima Albreiki
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14495-14504
affiliations: ["Technology Innovation Institute (TII)"]
cities: ["Abu Dhabi"]
countries: ["United Arab Emirates"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Asokan_FineLIP_Extending_CLIPs_Reach_via_Fine-Grained_Alignment_with_Longer_Text_CVPR_2025_paper.html

abstract:
As a pioneering vision-language model, CLIP (Contrastive Language-Image Pre-training) has achieved significant success across various domains and a wide range of downstream vision-language tasks. However, the text encoders in popular CLIP models are limited to processing only 77 text tokens, which constrains their ability to effectively handle longer, detail-rich captions. Additionally, CLIP models often struggle to effectively capture detailed visual and textual information, which hampers their performance on tasks that require fine-grained analysis. To address these limitations, we present a novel approach, FineLIP, that extends the capabilities of CLIP. FineLIP enhances cross-modal text-image mapping by incorporating Fine-grained alignment with Longer text input within the CLIP-style framework. FineLIP first extends the positional embeddings to handle longer text, followed by the dynamic aggregation of local image and text tokens. The aggregated results are then used to enforce fine-grained token-to-token cross-modal alignment. We validate our model on datasets with long, detailed captions across two tasks: zero-shot cross-modal retrieval and text-to-image generation. Quantitative and qualitative experimental results demonstrate the effectiveness of FineLIP, outperforming existing state-of-the-art approaches. Furthermore, comprehensive ablation studies validate the benefits of key design elements within FineLIP. The code will be available at https://github.com/tiiuae/FineLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Asokan_FineLIP_Extending_CLIPs_Reach_via_Fine-Grained_Alignment_with_Longer_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Asokan_FineLIP_Extending_CLIPs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Asokan_2025_CVPR,
    author    = {Asokan, Mothilal and Wu, Kebin and Albreiki, Fatima},
    title     = {FineLIP: Extending CLIP's Reach via Fine-Grained Alignment with Longer Text Inputs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14495-14504}
}

raw_affiliation:
[{"affiliation": "Technology Innovation Institute (TII)", "city": "Abu Dhabi", "country": "United Arab Emirates"}]


authors: Hyejin Oh, Woo-Shik Kim, Sangyoon Lee, YungKyung Park, Je-Won Kang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2215-2225
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Oh_Illumination_Spectrum_Estimation_for_Multispectral_Images_via_Surface_Reflectance_Modeling_CVPR_2025_paper.html

title:
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature Generation

abstract:
Multispectral (MS) images contain richer spectral information than RGB images due to their increased number of channels and are widely used for various applications. However, achieving accurate estimation in MS images remains challenging, as previous studies have struggled with spectral diversity and the inherent entanglement between the illuminant and surface reflectance spectra. To tackle these challenges, in this paper, we propose a novel Illumination spectrum estimation technique for MS images via Surface reflectance modeling and Spatial-spectral feature generation (ISS). The proposed technique employs a learnable spectral unmixing (SU) block to enhance surface reflectance modeling, which was unattempted in the illumination spectrum estimation, and a feature mixing block to fuse spectral and spatial features of MS images with cross-attention. The  features are refined iteratively and processed through a decoder to produce an illumination spectrum estimator. Experimental results demonstrate that the proposed technique achieves state-of-the-art performance in illumination spectrum estimation in various MS image datasets. The code is available at https://github.com/heyjinnii/ISS-MSI.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Oh_Illumination_Spectrum_Estimation_for_Multispectral_Images_via_Surface_Reflectance_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Oh_Illumination_Spectrum_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Oh_2025_CVPR,
    author    = {Oh, Hyejin and Kim, Woo-Shik and Lee, Sangyoon and Park, YungKyung and Kang, Je-Won},
    title     = {Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2215-2225}
}

raw_affiliation:
[{"affiliation": "Dept. of Electronic and Electrical Engineering, Ewha W. University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Samsung Advanced Institute of Technology", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Dept.of Color Design, Ewha W. University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Dept. of Electronic and Electrical Engineering, Ewha W. University", "Samsung Advanced Institute of Technology", "Dept.of Color Design, Ewha W. University"]


authors: Yidi Liu, Dong Li, Xueyang Fu, Xin Lu, Jie Huang, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23121-23130
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UHD-processer_Unified_UHD_Image_Restoration_with_Progressive_Frequency_Learning_and_CVPR_2025_paper.html

title:
UHD-processer: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware Prompts

abstract:
We introduce UHD-Processor, a unified and robust framework for all-in-one image restoration, which is particularly resource-efficient for Ultra-High-Definition (UHD) images. To address the limitations of traditional all-in-one methods that rely on complex restoration backbones, our strategy employs a frequency domain decoupling progressive learning technique, motivated by curriculum learning, to incrementally learn restoration mappings from low to high frequencies. This approach incorporates specialized sub-network modules to effectively tackle different frequency bands in a divide-and-conquer manner, significantly enhancing the learning capability of simpler networks. Moreover, to accommodate the high-resolution characteristics of UHD images, we developed a variational autoencoder (VAE)-based framework that reduces computational complexity by modeling a concise latent space. It integrates task-specific degradation awareness in the encoder and frequency selection in the decoder, enhancing task comprehension and generalization. Our unified model is able to handle various degradations such as denoising, deblurring, dehazing, low-lighting, etc. Experimental evaluations extensively showcase the effectiveness of our dual-strategy approach, significantly improving UHD image restoration and achieving cutting-edge performance across diverse conditions. The code will be available at https://github.com/lyd-2022/UHD-processer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_UHD-processer_Unified_UHD_Image_Restoration_with_Progressive_Frequency_Learning_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_UHD-processer_Unified_UHD_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yidi and Li, Dong and Fu, Xueyang and Lu, Xin and Huang, Jie and Zha, Zheng-Jun},
    title     = {UHD-processer: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23121-23130}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Technology and MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, 230026, China", "city": "Hefei", "country": "China"}]

affiliations:
["School of Information Science and Technology and MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, 230026, China"]


title: Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation
authors: Yuying Ge, Yizhuo Li, Yixiao Ge, Ying Shan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13606-13617
raw_affiliation: [{"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}]
affiliations: ["ARC Lab, Tencent PCG"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ge_Divot_Diffusion_Powers_Video_Tokenizer_for_Comprehension_and_Generation_CVPR_2025_paper.html

abstract:
In recent years, there has been a significant surge of interest in unifying image comprehension and generation within Large Language Models (LLMs). This growing interest has prompted us to explore extending this unification to videos. The core challenge lies in developing a versatile video tokenizer that captures both the spatial characteristics and temporal dynamics of videos to obtain representations for LLMs, and the representations can be further decoded into realistic video clips to enable video generation. In this work, we introduce Divot, a Diffusion-Powered Video Tokenizer, which leverages the diffusion process for self-supervised video representation learning. We posit that if a video diffusion model can effectively de-noise video clips by taking the features of a video tokenizer as the condition, then the tokenizer has successfully captured robust spatial and temporal information. Additionally, the video diffusion model inherently functions as a de-tokenizer, decoding videos from their representations. Building upon the Divot tokenizer, we present Divot-LLM through video-to-text autoregression and text-to-video generation by modeling the distributions of continuous-valued Divot features with a Gaussian Mixture Model. Experimental results demonstrate that our diffusion-based video tokenizer, when integrated with a pre-trained LLM, achieves competitive performance across various video comprehension and generation benchmarks. The instruction tuned Divot-LLM also excels in video storytelling, generating interleaved narratives and corresponding videos. Models and codes are available at https://github.com/TencentARC/Divot.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ge_Divot_Diffusion_Powers_Video_Tokenizer_for_Comprehension_and_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ge_Divot_Diffusion_Powers_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ge_2025_CVPR,
    author    = {Ge, Yuying and Li, Yizhuo and Ge, Yixiao and Shan, Ying},
    title     = {Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13606-13617}
}


title: Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models
authors: Jiacong Xu, Shao-Yuan Lo, Bardia Safaei, Vishal M. Patel, Isht Dwivedi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20370-20382
affiliations: ["Johns Hopkins University", "Honda Research Institute USA"]
cities: ["Baltimore", "Mountain View"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Towards_Zero-Shot_Anomaly_Detection_and_Reasoning_with_Multimodal_Large_Language_CVPR_2025_paper.html

abstract:
Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the traditional unsupervised AD setting that requires a large number of normal samples to train a model, ZSAD is more practical for handling data-restricted real-world scenarios. Recently, Multimodal Large Language Models (MLLMs) have shown revolutionary reasoning capabilities in various vision tasks. However, the reasoning of image abnormalities remains underexplored due to the lack of corresponding datasets and benchmarks. To facilitate research in AD & reasoning, we establish the first visual instruction tuning dataset, Anomaly-Instruct-125k, and the evaluation benchmark, VisA-D&R. Through investigation with our benchmark, we reveal that current MLLMs like GPT-4o cannot accurately detect and describe fine-grained anomalous details in images. To address this, we propose Anomaly-OneVision (Anomaly-OV), the first specialist visual assistant for ZSAD and reasoning. Inspired by human behavior in visual inspection, Anomaly-OV leverages a Look-Twice Feature Matching (LTFM) mechanism to adaptively select and emphasize abnormal visual tokens. Extensive experiments demonstrate that Anomaly-OV achieves significant improvements over advanced generalist models in both detection and reasoning. Extensions to medical and 3D AD are provided for future study. The link to our project page: https://xujiacong.github.io/Anomaly-OV/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Towards_Zero-Shot_Anomaly_Detection_and_Reasoning_with_Multimodal_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Towards_Zero-Shot_Anomaly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jiacong and Lo, Shao-Yuan and Safaei, Bardia and Patel, Vishal M. and Dwivedi, Isht},
    title     = {Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20370-20382}
}

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


title: Towards Explainable and Unprecedented Accuracy in Matching Challenging Finger Crease Patterns
authors: Zhenyu Zhou, Chengdong Dong, Ajay Kumar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6212-6221
cities: ["Hong Kong"]
countries: ["Hong Kong"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Towards_Explainable_and_Unprecedented_Accuracy_in_Matching_Challenging_Finger_Crease_CVPR_2025_paper.html

abstract:
The primary obstacle in realizing the full potential of finger crease biometrics is the accurate identification of deformed knuckle patterns, often resulting from completely contactless imaging. Current methods struggle significantly with this task, yet accurate matching is crucial for applications ranging from forensic investigations, such as child abuse cases, to surveillance and mobile security. To address this challenge, our study introduces the largest publicly available dataset of deformed knuckle patterns, comprising 805,768 images from 351 subjects. We also propose a novel framework to accurately match knuckle patterns, even under severe pose deformations, by recovering interpretable knuckle crease keypoint feature templates. These templates can dynamically uncover graph structure and feature similarity among the matched correspondences. Our experiments, using the most challenging protocols, illustrate significantly outperforming results for matching such knuckle images. For the first time, we present and evaluate a theoretical model to estimate the uniqueness of 2D finger knuckle patterns, providing a more interpretable and accurate measure of distinctiveness, which is invaluable for forensic examiners in prosecuting suspects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Towards_Explainable_and_Unprecedented_Accuracy_in_Matching_Challenging_Finger_Crease_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Towards_Explainable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Zhenyu and Dong, Chengdong and Kumar, Ajay},
    title     = {Towards Explainable and Unprecedented Accuracy in Matching Challenging Finger Crease Patterns},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6212-6221}
}

raw_affiliation:
[{"affiliation": "Department of Data Science and Arti\ufb01cial Intelligence, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong"}]

affiliations:
["Department of Data Science and Arti\ufb01cial Intelligence, The Hong Kong Polytechnic University"]


title: Neural Hierarchical Decomposition for Single Image Plant Modeling
authors: Zhihao Liu, Zhanglin Cheng, Naoto Yokoya
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 733-742
affiliations: ["The University of Tokyo", "RIKEN AIP", "SIAT, Chinese Academy of Sciences"]
cities: ["Tokyo", "Tokyo", "Shenzhen"]
countries: ["Japan", "Japan", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Neural_Hierarchical_Decomposition_for_Single_Image_Plant_Modeling_CVPR_2025_paper.html

abstract:
Obtaining high-quality, practically usable 3D models of biological plants remains a significant challenge in computer vision and graphics. In this paper, we present a novel method for generating realistic 3D plant models from single-view photographs. Our approach employs a neural decomposition technique to learn a lightweight hierarchical box representation from the image, effectively capturing the structures and botanical features of plants. Then, this representation can be subsequently refined through a shape-guided parametric modeling module to produce complete 3D plant models. By combining hierarchical learning and parametric modeling, our method generates structured 3D plant assets with fine geometric details. Notably, through learning the decomposition in different levels of detail, our method can adapt to two distinct plant categories: outdoor trees and houseplants, each with unique appearance features. Within the scope of plant modeling, our method is the first comprehensive solution capable of reconstructing both plant categories from single-view images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Neural_Hierarchical_Decomposition_for_Single_Image_Plant_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Neural_Hierarchical_Decomposition_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhihao and Cheng, Zhanglin and Yokoya, Naoto},
    title     = {Neural Hierarchical Decomposition for Single Image Plant Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {733-742}
}

raw_affiliation:
[{"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RIKEN AIP", "city": "Tokyo", "country": "Japan"}, {"affiliation": "SIAT, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}]


title: GBC-Splat: Generalizable Gaussian-Based Clothed Human Digitalization under Sparse RGB Cameras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26377-26387
affiliations: ["Tsinghua University", "Harbin Institute of Technology", "Lenovo Research"]
cities: ["Beijing", "Harbin", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tu_GBC-Splat_Generalizable_Gaussian-Based_Clothed_Human_Digitalization_under_Sparse_RGB_Cameras_CVPR_2025_paper.html

authors:
Hanzhang Tu, Zhanfeng Liao, Boyao Zhou, Shunyuan Zheng, Xilong Zhou, Liuxin Zhang, QianYing Wang, Yebin Liu

abstract:
We present an efficient approach for generalizable clothed human digitalization, termed GBC-Splat. Unlike previous methods that necessitate per-subject optimizations or discount watertight geometry, the proposed method is dedicated to reconstructing complete human shapes and Gaussian Splatting via sparse view RGB inputs in a feed-forward manner. We first extract a fine-grained mesh using a combination of implicit occupancy field regression and explicit disparity estimation between views. Gaussian primitives anchored on the mesh allow 6-DoF photorealistic view synthesis. The reconstructed high-quality geometry allows us to easily anchor Gaussian primitives to mesh surface according to surface normal and texture, which allows 6-DoF photorealistic novel view synthesis. In addition, we introduce a simple yet effective algorithm to subdivide Gaussian primitives in high-frequency areas to further enhance the visual quality. Without the assistance of human parametric models, our method can tackle loose garments, such as dresses and costumes. Our method outperforms state-of-the-art methods in terms of novel view synthesis while keeping high efficiency, enabling the potential of deployment in real-time applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_GBC-Splat_Generalizable_Gaussian-Based_Clothed_Human_Digitalization_under_Sparse_RGB_Cameras_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_GBC-Splat_Generalizable_Gaussian-Based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tu_2025_CVPR,
    author    = {Tu, Hanzhang and Liao, Zhanfeng and Zhou, Boyao and Zheng, Shunyuan and Zhou, Xilong and Zhang, Liuxin and Wang, QianYing and Liu, Yebin},
    title     = {GBC-Splat: Generalizable Gaussian-Based Clothed Human Digitalization under Sparse RGB Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26377-26387}
}

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


title: AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22875-22889
affiliations: ["University of Toronto", "Vector Institute", "Snap Inc.", "SFU"]
cities: ["Toronto", "Toronto", "Santa Monica", "Burnaby"]
countries: ["Canada", "Canada", "USA", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bahmani_AC3D_Analyzing_and_Improving_3D_Camera_Control_in_Video_Diffusion_CVPR_2025_paper.html

authors:
Sherwin Bahmani, Ivan Skorokhodov, Guocheng Qian, Aliaksandr Siarohin, Willi Menapace, Andrea Tagliasacchi, David B. Lindell, Sergey Tulyakov

abstract:
Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation without compromising synthesis quality. First, we determine that motion induced by camera movements in videos is low-frequency in nature. This motivates us to adjust train and test pose conditioning schedules, accelerating training convergence while improving visual and motion quality. Then, by probing the representations of an unconditional video diffusion transformer, we observe that they implicitly perform camera pose estimation under the hood, and only a sub-portion of their layers contain the camera information. This suggested us to limit the injection of camera conditioning to a subset of the architecture to prevent interference with other video features, leading to a 4x reduction of training parameters, improved training speed, and 10% higher visual quality. Finally, we complement the typical dataset for camera control learning with a curated dataset of 20K diverse, dynamic videos with stationary cameras. This helps the model distinguish between camera and scene motion and improves the dynamics of generated pose-conditioned videos. We compound these findings to design the Advanced 3D Camera Control (AC3D) architecture, the new state-of-the-art model for generative video modeling with camera control.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bahmani_AC3D_Analyzing_and_Improving_3D_Camera_Control_in_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bahmani_AC3D_Analyzing_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bahmani_2025_CVPR,
    author    = {Bahmani, Sherwin and Skorokhodov, Ivan and Qian, Guocheng and Siarohin, Aliaksandr and Menapace, Willi and Tagliasacchi, Andrea and Lindell, David B. and Tulyakov, Sergey},
    title     = {AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22875-22889}
}

raw_affiliation:
[{"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "USA"}, {"affiliation": "SFU", "city": "Burnaby", "country": "Canada"}]


title: A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26004-26013
raw_affiliation: [{"affiliation": "California Institute of Technology", "city": "Pasadena", "country": "USA"}]
affiliations: ["California Institute of Technology"]
cities: ["Pasadena"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jatyani_A_Unified_Model_for_Compressed_Sensing_MRI_Across_Undersampling_Patterns_CVPR_2025_paper.html

authors:
Armeet Singh Jatyani, Jiayun Wang, Aditi Chandrashekar, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, Anima Anandkumar

abstract:
Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled measurements, thereby reducing the scan time - the time subjects need to remain still.  Recently, deep learning has shown great potential for reconstructing high-fidelity images from highly undersampled measurements. However, one needs to train multiple models for different undersampling patterns and desired output image resolutions, since most networks operate on a fixed discretization. Such approaches are highly impractical in clinical settings, where undersampling patterns and image resolutions are frequently changed to accommodate different real-time imaging and diagnostic requirements. We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.  Our approach uses neural operators- a discretization-agnostic architecture applied in both image and measurement spaces--to capture local and global features. Empirically, our model improves SSIM by 11% and PSNR by 4dB over a state-of-the-art CNN (End-to-End VarNet), with inference 600x faster than diffusion methods. The resolution-agnostic design also enables zero-shot super-resolution and extended field-of-view reconstruction, offering a versatile and efficient solution for clinical MR imaging. Our unified model offers a versatile solution for MRI, adapting seamlessly to various measurement undersampling and imaging resolutions, making it highly effective for flexible and reliable clinical imaging. Our code is available at https://armeet.ca/nomri.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jatyani_A_Unified_Model_for_Compressed_Sensing_MRI_Across_Undersampling_Patterns_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jatyani_A_Unified_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jatyani_2025_CVPR,
    author    = {Jatyani, Armeet Singh and Wang, Jiayun and Chandrashekar, Aditi and Wu, Zihui and Liu-Schiaffini, Miguel and Tolooshams, Bahareh and Anandkumar, Anima},
    title     = {A Unified Model for Compressed Sensing MRI Across Undersampling Patterns},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26004-26013}
}


title: Video-Guided Foley Sound Generation with Multimodal Controls
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18770-18781
affiliations: ["University of Michigan", "Adobe Research"]
cities: ["Ann Arbor", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Video-Guided_Foley_Sound_Generation_with_Multimodal_Controls_CVPR_2025_paper.html

authors:
Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Nieto, David Bourgin, Andrew Owens, Justin Salamon

abstract:
Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce *MultiFoley*, a model designed for video-guided sound generation that supports multimodal conditioning through text, audio, and video. Given a silent video and a text prompt, MultiFoley allows users to create clean sounds (e.g., skateboard wheels spinning without wind noise) or more whimsical sounds (e.g., making a lion's roar sound like a cat's meow).MultiFoley also allows users to choose reference audio from sound effects (SFX) libraries or partial videos for conditioning. A key novelty of our model lies in its joint training on both internet video datasets with low-quality audio and professional SFX recordings, enabling high-quality, full-bandwidth (48kHz) audio generation.Through automated evaluations and human studies, we demonstrate that *MultiFoley* successfully generates synchronized high-quality sounds across varied conditional inputs and outperforms existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Video-Guided_Foley_Sound_Generation_with_Multimodal_Controls_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Video-Guided_Foley_Sound_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ziyang and Seetharaman, Prem and Russell, Bryan and Nieto, Oriol and Bourgin, David and Owens, Andrew and Salamon, Justin},
    title     = {Video-Guided Foley Sound Generation with Multimodal Controls},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18770-18781}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: Dual-Agent Optimization framework for Cross-Domain Few-Shot Segmentation
authors: Zhaoyang Li, Yuan Wang, Wangkai Li, Tianzhu Zhang, Xiang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9849-9859
cities: ["Hefei", "Hefei", "Dongguan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Dual-Agent_Optimization_framework_for_Cross-Domain_Few-Shot_Segmentation_CVPR_2025_paper.html

abstract:
Cross-Domain Few-Shot Segmentation (CD-FSS) extends the generalization ability of Few-Shot Segmentation (FSS) beyond a single domain, enabling more practical applications. However, directly employing conventional FSS methods suffers from severe performance degradation in cross-domain settings, primarily due to feature sensitivity and support-to-query matching process sensitivity across domains. Existing methods for CD-FSS either focus on domain adaptation of features or delve into designing matching strategies for enhanced cross-domain robustness. Nonetheless, they overlook the fact that these two issues are interdependent and should be addressed jointly. In this work, we tackle these two issues within a unified framework by optimizing features in the frequency domain and enhancing the matching process in the spatial domain, working jointly to handle the deviations introduced by the domain gap. To this end, we propose a coherent Dual-Agent Optimization (DATO) framework, including a consistent mutual aggregation (CMA) and a correlation rectification strategy (CRS). In the consistent mutual aggregation module, we employ a set of agents to learn domain-invariant features across domains, and then use these features to enhance the original representations for feature adaptation. In the correlation rectification strategy, the agent-aggregated domain-invariant features serve as a bridge, transforming the support-to-query matching process into a referable feature space and reducing its domain sensitivity. Extensive experiments demonstrate the efficacy of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Dual-Agent_Optimization_framework_for_Cross-Domain_Few-Shot_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Dual-Agent_Optimization_framework_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhaoyang and Wang, Yuan and Li, Wangkai and Zhang, Tianzhu and Liu, Xiang},
    title     = {Dual-Agent Optimization framework for Cross-Domain Few-Shot Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9849-9859}
}

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

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


title: SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
authors: Xinxing Cheng, Tianyang Zhang, Wenqi Lu, Qingjie Meng, Alejandro F. Frangi, Jinming Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5227-5237
cities: ["Birmingham", "Manchester", "Manchester", "Manchester", "London"]
countries: ["UK", "UK", "UK", "UK", "UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_SACB-Net_Spatial-awareness_Convolutions_for_Medical_Image_Registration_CVPR_2025_paper.html

abstract:
Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimation of deformation fields. In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to enhance the spatial information within feature representations. Our SACB estimates the spatial clusters within feature maps by leveraging feature similarity and subsequently parameterizes the adaptive convolution kernels across diverse regions. This adaptive mechanism generates the convolution kernels (weights and biases) tailored to spatial variations, thereby enabling the network to effectively capture spatially varying information. Building on SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates SACBs to facilitate multi-scale flow composition, particularly addressing large deformations. Experimental results on the brain IXI and LPBA datasets as well as Abdomen CT datasets demonstrate the effectiveness of SACB and the superiority of SACB-Net over the state-of-the-art learning-based registration methods.  The code is available at https://github.com/x-xc/SACB_Net.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_SACB-Net_Spatial-awareness_Convolutions_for_Medical_Image_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_SACB-Net_Spatial-awareness_Convolutions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Xinxing and Zhang, Tianyang and Lu, Wenqi and Meng, Qingjie and Frangi, Alejandro F. and Duan, Jinming},
    title     = {SACB-Net: Spatial-awareness Convolutions for Medical Image Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5227-5237}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, University of Birmingham", "city": "Birmingham", "country": "UK"}, {"affiliation": "Department of Computing and Mathematics, Manchester Metropolitan University", "city": "Manchester", "country": "UK"}, {"affiliation": "Division of Informatics, Imaging and Data Sciences, University of Manchester", "city": "Manchester", "country": "UK"}, {"affiliation": "Centre for Computational Imaging and Modelling in Medicine, University of Manchester", "city": "Manchester", "country": "UK"}, {"affiliation": "Department of Computing, Impeiral College London", "city": "London", "country": "UK"}]

affiliations:
["School of Computer Science, University of Birmingham", "Department of Computing and Mathematics, Manchester Metropolitan University", "Division of Informatics, Imaging and Data Sciences, University of Manchester", "Centre for Computational Imaging and Modelling in Medicine, University of Manchester", "Department of Computing, Impeiral College London"]


authors: Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8031-8040
raw_affiliation: [{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}]
affiliations: ["Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Text_Embedding_is_Not_All_You_Need_Attention_Control_for_CVPR_2025_paper.html

title:
Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps

abstract:
In text-to-image diffusion models, the cross-attention map of each text token indicates the specific image regions attended. Comparing these maps of syntactically related tokens provides insights into how well the generated image reflects the text prompt. For example, in the prompt, "a black car and a white clock", the cross-attention maps for "black" and "car" should focus on overlapping regions to depict a black car, while "car" and "clock" should not. Incorrect overlapping in the maps generally produces generation flaws such as missing objects and incorrect attribute binding. Our study makes the key observations investigating this issue in the existing text-to-image models: (1) the similarity in text embeddings between different tokens---used as conditioning inputs---can cause their cross-attention maps to focus on the same image regions; and (2) text embeddings often fail to faithfully capture syntactic relations already within text attention maps. As a result, such syntactic relationships can be overlooked in cross-attention module, leading to inaccurate image generation. To address this, we propose a method that directly transfers syntactic relations from the text attention maps to the cross-attention module via a test-time optimization. Our approach leverages this inherent yet unexploited information within text attention maps to enhance image-text semantic alignment across diverse prompts, without relying on external guidance. Our project page and code are available at: https://t-sam-diffusion.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Text_Embedding_is_Not_All_You_Need_Attention_Control_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Text_Embedding_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jeeyung and Esmaeili, Erfan and Qiu, Qiang},
    title     = {Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8031-8040}
}


title: DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2226-2235
cities: ["Dalian", "Dalian", "Xi'an", "Dalian"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DCEvo_Discriminative_Cross-Dimensional_Evolutionary_Learning_for_Infrared_and_Visible_Image_CVPR_2025_paper.html

authors:
Jinyuan Liu, Bowei Zhang, Qingyun Mei, Xingyuan Li, Yang Zou, Zhiying Jiang, Long Ma, Risheng Liu, Xin Fan

abstract:
Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each modality. Existing approaches typically treat image fusion and subsequent high-level tasks as separate processes, resulting in fused images that offer only marginal gains in task performance and fail to provide constructive feedback for optimizing the fusion process. To overcome these limitations, we propose a Discriminative Cross-Dimension Evolutionary Learning Framework, termed DCEvo, which simultaneously enhances visual quality and perception accuracy. Leveraging the robust search capabilities of Evolutionary Learning, our approach formulates the optimization of dual tasks as a multi-objective problem by employing an Evolutionary Algorithm (EA) to dynamically balance loss function parameters. Inspired by visual neuroscience, we integrate a Discriminative Enhancer (DE) within both the encoder and decoder, enabling the effective learning of complementary features from different modalities. Additionally, our Cross-Dimensional  Embedding (CDE) block facilitates mutual enhancement between high-dimensional task features and low-dimensional fusion features, ensuring a cohesive and efficient feature integration process. Experimental results on three benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches, achieving an average improvement of  9.32%  in visual quality while also enhancing subsequent high-level tasks. The code is available at https://github.com/Beate-Suy-Zhang/DCEvo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DCEvo_Discriminative_Cross-Dimensional_Evolutionary_Learning_for_Infrared_and_Visible_Image_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jinyuan and Zhang, Bowei and Mei, Qingyun and Li, Xingyuan and Zou, Yang and Jiang, Zhiying and Ma, Long and Liu, Risheng and Fan, Xin},
    title     = {DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2226-2235}
}

raw_affiliation:
[{"affiliation": "School of Mechanical Engineering, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "College of Information Science and Technology, Dalian Maritime University", "city": "Dalian", "country": "China"}]

affiliations:
["School of Mechanical Engineering, Dalian University of Technology", "School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology", "School of Computer Science, Northwestern Polytechnical University", "College of Information Science and Technology, Dalian Maritime University"]


title: TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23174-23184
cities: ["Hangzhou", "Dongguan", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_TSD-SR_One-Step_Diffusion_with_Target_Score_Distillation_for_Real-World_Image_CVPR_2025_paper.html

authors:
Linwei Dong, Qingnan Fan, Yihong Guo, Zhonghao Wang, Qi Zhang, Jinwei Chen, Yawei Luo, Changqing Zou

abstract:
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods such as SinSR and OSEDiff have emerged to condense inference steps via distillation, their performance in image restoration or details recovery is not satisfied. To address this, we propose TSD-SR, a novel distillation framework specifically designed for real-world image super-resolution, aiming to construct an efficient and effective one-step model. We first introduce the Target Score Distillation, which leverages the priors of diffusion models and real image references to achieve more realistic image restoration. Secondly, we propose a Distribution-Aware Sampling Module to make detail-oriented gradients more readily accessible, addressing the challenge of recovering fine details. Extensive experiments demonstrate that our TSD-SR has superior restoration results (most of the metrics perform the best) and the fastest inference speed (e.g. 40 times faster than SeeSR) compared to the past Real-ISR approaches based on pre-trained diffusion priors.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_TSD-SR_One-Step_Diffusion_with_Target_Score_Distillation_for_Real-World_Image_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Linwei and Fan, Qingnan and Guo, Yihong and Wang, Zhonghao and Zhang, Qi and Chen, Jinwei and Luo, Yawei and Zou, Changqing},
    title     = {TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23174-23184}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Vivo Mobile Communication Co. Ltd", "city": "Dongguan", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang University", "Vivo Mobile Communication Co. Ltd", "University of Chinese Academy of Sciences", "Zhejiang Lab"]


title: AIpparel: A Multimodal Foundation Model for Digital Garments
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8138-8149
affiliations: ["Stanford University", "ETH Z\u00a8urich"]
cities: ["Stanford", "Zurich"]
countries: ["USA", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nakayama_AIpparel_A_Multimodal_Foundation_Model_for_Digital_Garments_CVPR_2025_paper.html

authors:
Kiyohiro Nakayama, Jan Ackermann, Timur Levent Kesdogan, Yang Zheng, Maria Korosteleva, Olga Sorkine-Hornung, Leonidas J. Guibas, Guandao Yang, Gordon Wetzstein

abstract:
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming process, largely due to the manual work involved in designing them. To simplify this process, we introduce AIpparel, a multimodal foundation model for generating and editing sewing patterns. Our model fine-tunes state-of-the-art large multimodal models (LMMs) on a custom-curated large-scale dataset of over 120,000 unique garments, each with multimodal annotations including text, images, and sewing patterns. Additionally, we propose a novel tokenization scheme that concisely encodes these complex sewing patterns so that LLMs can learn to predict them efficiently. AIpparel achieves state-of-the-art performance in single-modal tasks, including text-to-garment and image-to-garment prediction, and enables novel multimodal garment generation applications such as interactive garment editing. The project website is at https://georgenakayama.github.io/AIpparel/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nakayama_AIpparel_A_Multimodal_Foundation_Model_for_Digital_Garments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nakayama_AIpparel_A_Multimodal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Nakayama_2025_CVPR,
    author    = {Nakayama, Kiyohiro and Ackermann, Jan and Kesdogan, Timur Levent and Zheng, Yang and Korosteleva, Maria and Sorkine-Hornung, Olga and Guibas, Leonidas J. and Yang, Guandao and Wetzstein, Gordon},
    title     = {AIpparel: A Multimodal Foundation Model for Digital Garments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8138-8149}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}]


title: Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21924-21935
affiliations: ["Meta FAIR", "University of Michigan"]
cities: ["Menlo Park", "Ann Arbor"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Fast3R_Towards_3D_Reconstruction_of_1000_Images_in_One_Forward_CVPR_2025_paper.html

authors:
Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, Matt Feiszli

abstract:
Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives. Current leading methods such as DUSt3R employ a fundamentally pairwise approach, processing images in pairs and necessitating costly global alignment procedures to reconstruct from multiple views. In this work, we propose Fast 3D Reconstruction (Fast3R), a novel multi-view generalization to DUSt3R that achieves efficient and scalable 3D reconstruction by processing many views in parallel. Fast3R's Transformer-based architecture forwards N images in a single forward pass, bypassing the need for iterative alignment. Through extensive experiments on camera pose estimation and 3D reconstruction, Fast3R demonstrates state-of-the-art performance, with significant improvements in inference speed and reduced error accumulation. These results establish Fast3R as a robust alternative for multi-view applications, offering enhanced scalability without compromising reconstruction accuracy. Project website: https://fast3r-3d.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Fast3R_Towards_3D_Reconstruction_of_1000_Images_in_One_Forward_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Fast3R_Towards_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jianing and Sax, Alexander and Liang, Kevin J. and Henaff, Mikael and Tang, Hao and Cao, Ang and Chai, Joyce and Meier, Franziska and Feiszli, Matt},
    title     = {Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21924-21935}
}

raw_affiliation:
[{"affiliation": "Meta FAIR", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}]


title: StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
authors: Mingkun Lei, Xue Song, Beier Zhu, Hao Wang, Chi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23443-23452
cities: ["Hangzhou", "Shanghai", "Singapore", "Guangzhou"]
countries: ["China", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_StyleStudio_Text-Driven_Style_Transfer_with_Selective_Control_of_Style_Elements_CVPR_2025_paper.html

abstract:
Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt.  Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain, particularly with overfitting to reference styles, limiting stylistic control, and misaligning with textual content.In this paper, we propose three complementary strategies to address these issues. First, we introduce a cross-modal Adaptive Instance Normalization (AdaIN) mechanism for better integration of style and text features, enhancing alignment. Second, we develop a Style-based Classifier-Free Guidance (SCFG) approach that enables selective control over stylistic elements, reducing irrelevant influences. Finally, we incorporate a teacher model during early generation stages to stabilize spatial layouts and mitigate artifacts. Our extensive evaluations demonstrate significant improvements in style transfer quality and alignment with textual prompts.  Furthermore, our approach can be integrated into existing style transfer frameworks without fine-tuning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_StyleStudio_Text-Driven_Style_Transfer_with_Selective_Control_of_Style_Elements_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_StyleStudio_Text-Driven_Style_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Mingkun and Song, Xue and Zhu, Beier and Wang, Hao and Zhang, Chi},
    title     = {StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23443-23452}
}

raw_affiliation:
[{"affiliation": "AGI Lab, Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "School of CS, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}]

affiliations:
["AGI Lab, Westlake University", "School of CS, Fudan University", "Nanyang Technological University", "The Hong Kong University of Science and Technology (Guangzhou)"]


title: CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29523-29533
cities: ["Montreal", "Montreal", "Amsterdam", "T\u00fcbingen", "Athens"]
countries: ["Canada", "Canada", "The Netherlands", "Germany", "Greece"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Didolkar_CTRL-O_Language-Controllable_Object-Centric_Visual_Representation_Learning_CVPR_2025_paper.html

authors:
Aniket Didolkar, Andrii Zadaianchuk, Rabiul Awal, Maximilian Seitzer, Efstratios Gavves, Aishwarya Agrawal

abstract:
Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each slot captures a distinct object. Current state-of-the-art object-centric models have shown remarkable success in object discovery in diverse domains including complex real-world scenes. However, these models suffer from a key limitation: they lack controllability. Specifically, current object-centric models learn representations based on their preconceived understanding of objects and parts, without allowing user input to guide which objects are represented. Introducing controllability into object-centric models could unlock a range of useful capabilities, such as the ability to extract instance-specific representations from a scene. In this work, we propose a novel approach for user-directed control over slot representations by conditioning slots on language descriptions. The proposed ConTRoLlable Object-centric representation learning approach, which we term CTRL-O, achieves targeted object-language binding in complex real-world scenes without requiring mask supervision. Next, we apply these controllable slot representations on two downstream vision language tasks: text-to-image generation and visual question answering. We find that the proposed approach enables instance-specific text-to-image generation and also achieves strong performance on visual question answering.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Didolkar_CTRL-O_Language-Controllable_Object-Centric_Visual_Representation_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Didolkar_CTRL-O_Language-Controllable_Object-Centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Didolkar_2025_CVPR,
    author    = {Didolkar, Aniket and Zadaianchuk, Andrii and Awal, Rabiul and Seitzer, Maximilian and Gavves, Efstratios and Agrawal, Aishwarya},
    title     = {CTRL-O: Language-Controllable Object-Centric Visual Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29523-29533}
}

raw_affiliation:
[{"affiliation": "Mila - Quebec AI Institute", "city": "Montreal", "country": "Canada"}, {"affiliation": "Universit\u00e9 de Montr\u00e9al", "city": "Montreal", "country": "Canada"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "The Netherlands"}, {"affiliation": "University of T\u00fcbingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Archimedes/Athena RC", "city": "Athens", "country": "Greece"}]

affiliations:
["Mila - Quebec AI Institute", "Universit\u00e9 de Montr\u00e9al", "University of Amsterdam", "University of T\u00fcbingen", "Archimedes/Athena RC"]


title: PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection
authors: Jianan Ye, Weiguang Zhao, Xi Yang, Guangliang Cheng, Kaizhu Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1353-1362
affiliations: ["Xi\u2019an Jiaotong-Liverpool University", "University of Liverpool", "Duke Kunshan University"]
cities: ["Suzhou", "Liverpool", "Kunshan"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_PO3AD_Predicting_Point_Offsets_toward_Better_3D_Point_Cloud_Anomaly_CVPR_2025_paper.html

abstract:
Point cloud anomaly detection under the anomaly-free setting poses significant challenges as it requires accurately capturing the features of 3D normal data to identify deviations indicative of anomalies. Current efforts focus on devising reconstruction tasks, such as acquiring normal data representations by restoring normal samples from altered, pseudo-anomalous counterparts. Our findings reveal that distributing attention equally across normal and pseudo-anomalous data tends to dilute the model's focus on anomalous deviations. The challenge is further compounded by the inherently disordered and sparse nature of 3D point cloud data. In response to those predicaments, we introduce an innovative approach that emphasizes learning point offsets, targeting more informative pseudo-abnormal points, thus fostering more effective distillation of normal data representations. We also have crafted an augmentation technique that is steered by normal vectors, facilitating the creation of credible pseudo anomalies that enhance the efficiency of the training process. Our comprehensive experimental evaluation on the Anomaly-ShapeNet and Real3D-AD datasets evidences that our proposed method outperforms existing state-of-the-art approaches, achieving an average enhancement of 9.0% and 1.4% in the AUC-ROC detection metric across these datasets, respectively. Code is available at https://github.com/yjnanan/PO3AD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_PO3AD_Predicting_Point_Offsets_toward_Better_3D_Point_Cloud_Anomaly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_PO3AD_Predicting_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Jianan and Zhao, Weiguang and Yang, Xi and Cheng, Guangliang and Huang, Kaizhu},
    title     = {PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1353-1362}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong-Liverpool University", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "Duke Kunshan University", "city": "Kunshan", "country": "China"}]


title: Text Augmented Correlation Transformer For Few-shot Classification & Segmentation
authors: Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng, Josef Kittler, Muhammad Awais
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25357-25366
cities: ["Guildford", "Guildford", "Wuxi"]
countries: ["UK", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nandam_Text_Augmented_Correlation_Transformer_For_Few-shot_Classification__Segmentation_CVPR_2025_paper.html

abstract:
Foundation models like CLIP and ALIGN have transformed few-shot and zero-shot vision applications by fusing visual and textual data, yet the integrative few-shot classification and segmentation (FS-CS) task primarily leverages visual cues, overlooking the potential of textual support. In FS-CS scenarios, ambiguous object boundaries and overlapping classes often hinder model performance, as limited visual data struggles to fully capture high-level semantics. To bridge this gap, we present a novel multi-modal FS-CS framework that integrates textual cues into support data, facilitating enhanced semantic disambiguation and fine-grained segmentation. Our approach first investigates the unique contributions of exclusive text-based support, using only class labels to achieve FS-CS. This strategy alone achieves performance competitive with vision-only methods on FS-CS tasks, underscoring the power of textual cues in few-shot learning. Building on this, we introduce a dual-modal prediction mechanism that synthesizes insights from both textual and visual support sets, yielding robust multi-modal predictions. This integration significantly elevates FS-CS performance, with classification and segmentation improvements of +3.7/6.6% (1-way 1-shot) and +8.0/6.5% (2-way 1-shot) on COCO-20^i, and +2.2/3.8% (1-way 1-shot) and +4.3/4.0% (2-way 1-shot) on Pascal-5^i. Additionally, in weakly supervised FS-CS settings, our method surpasses visual-only benchmarks using textual support exclusively, further enhanced by our dual-modal predictions. By rethinking the role of text in FS-CS, our work establishes new benchmarks for multi-modal few-shot learning and demonstrates the efficacy of textual cues for improving model generalization and segmentation accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nandam_Text_Augmented_Correlation_Transformer_For_Few-shot_Classification__Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nandam_Text_Augmented_Correlation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nandam_2025_CVPR,
    author    = {Nandam, Srinivasa Rao and Atito, Sara and Feng, Zhenhua and Kittler, Josef and Awais, Muhammad},
    title     = {Text Augmented Correlation Transformer For Few-shot Classification \& Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25357-25366}
}

raw_affiliation:
[{"affiliation": "Surrey Institute for People-Centred AI (PAI), University of Surrey, UK", "city": "Guildford", "country": "UK"}, {"affiliation": "CVSSP, University of Surrey, UK", "city": "Guildford", "country": "UK"}, {"affiliation": "School of AI and Computer Science, Jiangnan University, China", "city": "Wuxi", "country": "China"}]

affiliations:
["Surrey Institute for People-Centred AI (PAI), University of Surrey, UK", "CVSSP, University of Surrey, UK", "School of AI and Computer Science, Jiangnan University, China"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20006-20017
cities: ["Oxford", "Oxford", "London", "Birmingham"]
countries: ["United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Saha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.html

title:
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics

authors:
Pramit Saha, Felix Wagner, Divyanshu Mishra, Can Peng, Anshul Thakur, David A. Clifton, Konstantinos Kamnitsas, J. Alison Noble

abstract:
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors, viz., client-specific layer importance score that selects the most important VLM layers for fine-tuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F^3OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method. Project Page: https://pramitsaha.github.io/FOCUS/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Saha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Saha_F3OCUS_-_Federated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Saha_2025_CVPR,
    author    = {Saha, Pramit and Wagner, Felix and Mishra, Divyanshu and Peng, Can and Thakur, Anshul and Clifton, David A. and Kamnitsas, Konstantinos and Noble, J. Alison},
    title     = {F{\textasciicircum}3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20006-20017}
}

raw_affiliation:
[{"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Oxford-Suzhou Centre for Advanced Research", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "United Kingdom"}]

affiliations:
["University of Oxford", "Oxford-Suzhou Centre for Advanced Research", "Imperial College London", "University of Birmingham"]


authors: Junzhe Chen, Tianshu Zhang, Shiyu Huang, Yuwei Niu, Linfeng Zhang, Lijie Wen, Xuming Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4209-4221
cities: ["Beijing", "Guangzhou", "Beijing", "Chongqing", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_ICT_Image-Object_Cross-Level_Trusted_Intervention_for_Mitigating_Object_Hallucination_in_CVPR_2025_paper.html

title:
ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models

abstract:
Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in understanding and responding to complex visual-textual contexts, their inherent hallucination tendencies limit their practical application in real-world scenarios that demand high levels of precision. Existing methods typically either fine-tune the LVLMs using additional data, which incurs extra costs in manual annotation and computational resources or perform comparisons at the decoding stage, which may eliminate useful language priors for reasoning while introducing inference time overhead. Therefore, we propose ICT, a lightweight, training-free method that calculates an intervention direction to shift the model's focus towards different levels of visual information, enhancing its attention to high-level and fine-grained visual details.  During the forward pass stage, the intervention is applied to the attention heads that encode the overall image information and the fine-grained object details, effectively mitigating the phenomenon of overly language priors, and thereby alleviating hallucinations. Extensive experiments demonstrate that ICT achieves strong performance with a small amount of data and generalizes well across different datasets and models. Our code will be public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_ICT_Image-Object_Cross-Level_Trusted_Intervention_for_Mitigating_Object_Hallucination_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_ICT_Image-Object_Cross-Level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Junzhe and Zhang, Tianshu and Huang, Shiyu and Niu, Yuwei and Zhang, Linfeng and Wen, Lijie and Hu, Xuming},
    title     = {ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4209-4221}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Zhipu AI", "city": "Beijing", "country": "China"}, {"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Tsinghua University", "The Hong Kong University of Science and Technology (Guangzhou)", "Zhipu AI", "Chongqing University", "Shanghai Jiao Tong University"]


title: PreciseCam: Precise Camera Control for Text-to-Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2724-2733
affiliations: ["Universidad de Zaragoza, I3A", "Adobe Research"]
cities: ["Zaragoza", "San Jose"]
countries: ["Spain", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bernal-Berdun_PreciseCam_Precise_Camera_Control_for_Text-to-Image_Generation_CVPR_2025_paper.html

authors:
Edurne Bernal-Berdun, Ana Serrano, Belen Masia, Matheus Gadelha, Yannick Hold-Geoffroy, Xin Sun, Diego Gutierrez

abstract:
Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data.We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bernal-Berdun_PreciseCam_Precise_Camera_Control_for_Text-to-Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bernal-Berdun_PreciseCam_Precise_Camera_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bernal-Berdun_2025_CVPR,
    author    = {Bernal-Berdun, Edurne and Serrano, Ana and Masia, Belen and Gadelha, Matheus and Hold-Geoffroy, Yannick and Sun, Xin and Gutierrez, Diego},
    title     = {PreciseCam: Precise Camera Control for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2724-2733}
}

raw_affiliation:
[{"affiliation": "Universidad de Zaragoza, I3A", "city": "Zaragoza", "country": "Spain"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: 3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17134-17144
affiliations: ["Korea University", "Purdue University", "AI Center, DS Division, Samsung Electronics"]
cities: ["Seoul", "West Lafayette", "Suwon"]
countries: ["South Korea", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Oh_3D_Occupancy_Prediction_with_Low-Resolution_Queries_via_Prototype-aware_View_Transformation_CVPR_2025_paper.html

authors:
Gyeongrok Oh, Sungjune Kim, Heeju Ko, Hyung-gun Chi, Jinkyu Kim, Dongwook Lee, Daehyun Ji, Sungjoon Choi, Sujin Jang, Sangpil Kim

abstract:
The resolution of voxel queries significantly influences the quality of view transformation in camera-based 3D occupancy prediction. However, computational constraints and the practical necessity for real-time deployment require smaller query resolutions, which inevitably leads to an information loss. Therefore, it is essential to encode and preserve rich visual details within limited query sizes while ensuring a comprehensive representation of 3D occupancy. To this end, we introduce ProtoOcc, a novel occupancy network that leverages prototypes of clustered image segments in view transformation to enhance low-resolution context. In particular, the mapping of 2D prototypes onto 3D voxel queries encodes high-level visual geometries and complements the loss of spatial information from reduced query resolutions. Additionally, we design a multi-perspective decoding strategy to efficiently disentangle the densely compressed visual cues into a high-dimensional 3D occupancy scene. Experimental results on both Occ3D and SemanticKITTI benchmarks demonstrate the effectiveness of the proposed method, showing clear improvements over the baselines. More importantly, ProtoOcc achieves competitive performance against the baselines even with 75% reduced voxel resolution.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Oh_3D_Occupancy_Prediction_with_Low-Resolution_Queries_via_Prototype-aware_View_Transformation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Oh_3D_Occupancy_Prediction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Oh_2025_CVPR,
    author    = {Oh, Gyeongrok and Kim, Sungjune and Ko, Heeju and Chi, Hyung-gun and Kim, Jinkyu and Lee, Dongwook and Ji, Daehyun and Choi, Sungjoon and Jang, Sujin and Kim, Sangpil},
    title     = {3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17134-17144}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "AI Center, DS Division, Samsung Electronics", "city": "Suwon", "country": "South Korea"}]


title: Unified Dense Prediction of Video Diffusion
authors: Lehan Yang, Lu Qi, Xiangtai Li, Sheng Li, Varun Jampani, Ming-Hsuan Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28963-28973
cities: ["Charlottesville", "Merced", "Singapore", "Palo Alto"]
countries: ["USA", "USA", "Singapore", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Unified_Dense_Prediction_of_Video_Diffusion_CVPR_2025_paper.html

abstract:
We present a unified network for simultaneously generating videos and their corresponding entity segmentation and depth maps from text prompts. We utilize colormap to represent entity masks and depth maps, tightly integrating dense prediction with RGB video generation. Introducing dense prediction information improves video generation's consistency and motion smoothness without increasing computational costs. Incorporating learnable task embeddings brings multiple dense prediction tasks into a single model, enhancing flexibility and further boosting performance. We further propose a large-scale dense prediction video dataset Panda-Dense, addressing the issue that existing datasets do not concurrently contain captions, videos, segmentation, or depth maps. Comprehensive experiments demonstrate the high efficiency of our method, surpassing the state-of-the-art in terms of video quality, consistency, and motion smoothness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Unified_Dense_Prediction_of_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Unified_Dense_Prediction_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Lehan and Qi, Lu and Li, Xiangtai and Li, Sheng and Jampani, Varun and Yang, Ming-Hsuan},
    title     = {Unified Dense Prediction of Video Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28963-28973}
}

raw_affiliation:
[{"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Stability AI", "city": "Palo Alto", "country": "USA"}]

affiliations:
["University of Virginia", "University of California, Merced", "Nanyang Technological University", "Stability AI"]


title: Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?
authors: Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler, David Acuna
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14667-14678
affiliations: ["University of Toronto, Vector Institute", "NVIDIA", "University of Ottawa"]
cities: ["Toronto", "Santa Clara", "Ottawa"]
countries: ["Canada", "USA", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Can_Large_Vision-Language_Models_Correct_Semantic_Grounding_Errors_By_Themselves_CVPR_2025_paper.html

abstract:
Improving semantic grounding in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying the training recipes. In this work, we venture into an orthogonal direction and explore self-correction in VLMs focusing on semantic grounding. We find that VLMs can correct their own semantic grounding mistakes when properly prompted and framed for the task, without any fine-tuning or even access to oracle feedback. We also introduce a self-correction framework in an iterative setting which consistently improves performance across all models investigated. Overall, we show that iterative self-correction consistently improves VLM performance in semantic grounding by up to 8.4 accuracy points across all models investigated, without requiring fine-tuning, additional architectural changes, or external data. Our exploration of self-correction also reveals that, even after several rounds of feedback, strong models like GPT-4V and GPT-4o  retain limited capability in leveraging oracle feedback, suggesting promising directions for further research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Can_Large_Vision-Language_Models_Correct_Semantic_Grounding_Errors_By_Themselves_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Yuan-Hong and Mahmood, Rafid and Fidler, Sanja and Acuna, David},
    title     = {Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14667-14678}
}

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


title: SET: Spectral Enhancement for Tiny Object Detection
authors: Huixin Sun, Runqi Wang, Yanjing Li, Linlin Yang, Shaohui Lin, Xianbin Cao, Baochang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4713-4723
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Beijing", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_SET_Spectral_Enhancement_for_Tiny_Object_Detection_CVPR_2025_paper.html

abstract:
Deep learning has significantly advanced the object detection field. However, tiny object detection (TOD) remains a challenging problem. We provide a new analysis method to examine the TOD challenge through occlusion-based attribution analysis in the frequency domain. We observe that tiny objects become less distinct after feature encoding and can benefit from the removal of high-frequency information. In this paper, we propose a novel approach named Spectral Enhancement for Tiny object detection (SET), which amplifies the frequency signatures of tiny objects in a heterogeneous architecture. SET includes two modules. The Hierarchical Background Smoothing (HBS) module suppresses high-frequency noise in the background through adaptive smoothing operations. The Adversarial Perturbation Injection (API) module leverages adversarial perturbations to increase feature saliency in critical regions and prompt the refinement of object features during training. Extensive experiments on four datasets demonstrate the effectiveness of our method. Especially, SET boosts the prior art RFLA by 3.2% AP on the AI-TOD dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_SET_Spectral_Enhancement_for_Tiny_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_SET_Spectral_Enhancement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Huixin and Wang, Runqi and Li, Yanjing and Yang, Linlin and Lin, Shaohui and Cao, Xianbin and Zhang, Baochang},
    title     = {SET: Spectral Enhancement for Tiny Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4713-4723}
}

raw_affiliation:
[{"affiliation": "School of Electronic Information Engineering, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhongguancun Laboratory", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Media Convergence and Communication, Communication University of China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Technology, East China Normal University", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Electronic Information Engineering, Beihang University", "School of Computer Science and Technology, Beijing Jiaotong University", "School of Arti\ufb01cial Intelligence, Beihang University", "Zhongguancun Laboratory", "State Key Laboratory of Media Convergence and Communication, Communication University of China", "School of Computer Science and Technology, East China Normal University"]


title: g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks
authors: Zihan Wang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14191-14202
affiliations: ["School of Computing, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_g3D-LF_Generalizable_3D-Language_Feature_Fields_for_Embodied_Tasks_CVPR_2025_paper.html

abstract:
We introduce Generalizable 3D-Language Feature Fields (g3D-LF), a 3D representation model pre-trained on large-scale 3D-language dataset for embodied tasks. Our g3D-LF processes posed RGB-D images from agents to encode feature fields for: 1) Novel view representation predictions from any position in the 3D scene; 2) Generations of BEV maps centered on the agent; 3) Querying targets using multi-granularity language within the above-mentioned representations. Our representation can be generalized to unseen environments, enabling real-time construction and dynamic updates. By volume rendering latent features along sampled rays and integrating semantic and spatial relationships through multiscale encoders, our g3D-LF produces representations at different scales and perspectives, aligned with multi-granularity language, via multi-level contrastive learning. Furthermore, we prepare a large-scale 3D-language dataset to align the representations of the feature fields with language. Extensive experiments on Vision-and-Language Navigation under both Panorama and Monocular settings, Zero-shot Object Navigation, and Situated Question Answering tasks highlight the significant advantages and effectiveness of our g3D-LF for embodied tasks. Our source code and dataset will be made open-source upon paper acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_g3D-LF_Generalizable_3D-Language_Feature_Fields_for_Embodied_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_g3D-LF_Generalizable_3D-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zihan and Lee, Gim Hee},
    title     = {g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14191-14202}
}

raw_affiliation:
[{"affiliation": "School of Computing, National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
authors: Yong Xie, Weijie Zheng, Hanxun Huang, Guangnan Ye, Xingjun Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30702-30711
cities: ["Shanghai", "Melbourne"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Towards_Million-Scale_Adversarial_Robustness_Evaluation_With_Stronger_Individual_Attacks_CVPR_2025_paper.html

abstract:
As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of white-box adversarial robustness methods (i.e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods. Despite these advances, challenges remain in conducting meaningful and comprehensive robustness evaluations, particularly when it comes to large-scale testing and ensuring evaluations reflect real-world adversarial risks.In this work, we focus on image classification models and propose a novel individual attack method, Probability Margin Attack (PMA), which defines the adversarial margin in the probability space rather than the logits space. We analyze the relationship between PMA and existing cross-entropy or logits-margin-based attacks, showing that PMA outperforms the current state-of-the-art individual methods.Building on PMA, we propose two types of ensemble attacks that balance effectiveness and efficiency. Furthermore, we create a million-scale dataset, CC1M, derived from the existing CC3M dataset, and use it to conduct the first million-scale white-box adversarial robustness evaluation of adversarially-trained ImageNet models. Our findings provide valuable insights into the robustness gaps between individual versus ensemble attacks and small-scale versus million-scale evaluations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Towards_Million-Scale_Adversarial_Robustness_Evaluation_With_Stronger_Individual_Attacks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Towards_Million-Scale_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yong and Zheng, Weijie and Huang, Hanxun and Ye, Guangnan and Ma, Xingjun},
    title     = {Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30702-30711}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, China", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computing and Information Systems, The University of Melbourne, Australia", "city": "Melbourne", "country": "Australia"}]

affiliations:
["Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, China", "School of Computing and Information Systems, The University of Melbourne, Australia"]


title: Temporal Action Detection Model Compression by Progressive Block Drop
authors: Xiaoyong Chen, Yong Guo, Jiaming Liang, Sitong Zhuang, Runhao Zeng, Xiping Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29225-29236
cities: ["Shenzhen", "Shenzhen", "Guangzhou", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Temporal_Action_Detection_Model_Compression_by_Progressive_Block_Drop_CVPR_2025_paper.html

abstract:
Temporal action detection (TAD) aims to identify and localize action instances in untrimmed videos, which is essential for various video understanding tasks. However, recent improvements in model performance, driven by larger feature extractors and datasets, have led to increased computational demands. This presents a challenge for applications like autonomous driving and robotics, which rely on limited computational resources. While existing channel pruning methods can compress these models, reducing the number of channels often hinders the parallelization efficiency of GPU, due to the inefficient multiplication between small matrices. Instead of pruning channels, we propose a **Progressive Block Drop** method that reduces model depth while retaining layer width. In this way, we still use large matrices for computation but reduce the number of multiplications. Our approach iteratively removes redundant blocks in two steps: first, we drop blocks with minimal impact on model performance; and second, we employ a parameter-efficient cross-depth alignment technique, fine-tuning the pruned model to restore model accuracy. Our method achieves a 25% reduction in computational overhead on two TAD benchmarks (THUMOS14 and ActivityNet-1.3) to achieve lossless compression. More critically, we empirically show that our method is orthogonal to channel pruning methods and can be combined with it to yield further efficiency gains.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Temporal_Action_Detection_Model_Compression_by_Progressive_Block_Drop_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Temporal_Action_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xiaoyong and Guo, Yong and Liang, Jiaming and Zhuang, Sitong and Zeng, Runhao and Hu, Xiping},
    title     = {Temporal Action Detection Model Compression by Progressive Block Drop},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29225-29236}
}

raw_affiliation:
[{"affiliation": "Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University", "Shenzhen University", "South China University of Technology", "Beijing Institute of Technology"]


title: Differentiable Inverse Rendering with Interpretable Basis BRDFs
authors: Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 475-484
raw_affiliation: [{"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]
affiliations: ["POSTECH"]
cities: ["Pohang"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chung_Differentiable_Inverse_Rendering_with_Interpretable_Basis_BRDFs_CVPR_2025_paper.html

abstract:
Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive separation and have limited scalability to scenes of varying complexity. In this paper, we introduce a differentiable inverse rendering method that produces interpretable basis BRDFs. Our approach models a scene using 2D Gaussians, where the reflectance of each Gaussian is defined by a weighted blend of basis BRDFs. We efficiently render an image from the 2D Gaussians and basis BRDFs using differentiable rasterization and impose a specular-weighted rendering loss with the input flash photography images. During this analysis-by-synthesis optimization process of differentiable inverse rendering, we dynamically adjust the number of basis BRDFs to fit the target scene while encouraging sparsity in the basis weights. This ensures that the reflectance of each Gaussian is represented by only a few basis BRDFs. This approach enables the reconstruction of accurate geometry and interpretable basis BRDFs that are spatially separated. Consequently, the resulting scene representation, comprising basis BRDFs and 2D Gaussians, supports physically-based novel-view relighting and intuitive scene editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chung_Differentiable_Inverse_Rendering_with_Interpretable_Basis_BRDFs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chung_Differentiable_Inverse_Rendering_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chung_2025_CVPR,
    author    = {Chung, Hoon-Gyu and Choi, Seokjun and Baek, Seung-Hwan},
    title     = {Differentiable Inverse Rendering with Interpretable Basis BRDFs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {475-484}
}


title: EquiPose: Exploiting Permutation Equivariance for Relative Camera Pose Estimation
authors: Yuzhen Liu, Qiulei Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1127-1137
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EquiPose_Exploiting_Permutation_Equivariance_for_Relative_Camera_Pose_Estimation_CVPR_2025_paper.html

abstract:
Relative camera pose estimation between two images is a fundamental task in 3D computer vision. Recently, many relative pose estimation networks have been explored for learning a mapping from two input images to their corresponding relative pose, however, the estimated relative poses by these methods do not have the intrinsic Pose Permutation Equivariance (PPE) property: the estimated relative pose from Image A to Image B should be the inverse of that from Image B to Image A. It means that permuting the input order of two images would cause these methods to obtain inconsistent relative poses. To address this problem, we firstly introduce the concept of PPE mapping, which indicates such a mapping that captures the intrinsic PPE property of relative poses. Then by enforcing the aforementioned PPE property, we propose a general framework for relative pose estimation, called EquiPose, which could easily accommodate various relative pose estimation networks in literature as its baseline models. We further theoretically prove that the proposed EquiPose framework could guarantee that its obtained mapping is a PPE mapping. Given a pre-trained baseline model, the proposed EquiPose framework could improve its performance even without fine-tuning, and could further boost its performance with fine-tuning. Experimental results on four public datasets demonstrate that EquiPose could significantly improve the performances of various state-of-the-art models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EquiPose_Exploiting_Permutation_Equivariance_for_Relative_Camera_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_EquiPose_Exploiting_Permutation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuzhen and Dong, Qiulei},
    title     = {EquiPose: Exploiting Permutation Equivariance for Relative Camera Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1127-1137}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Multimodal Artificial Intelligence Systems, Chinese Academy of Sciences, Institute of Automation", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Multimodal Artificial Intelligence Systems, Chinese Academy of Sciences, Institute of Automation", "School of Artificial Intelligence, University of Chinese Academy of Sciences"]


title: Face Forgery Video Detection via Temporal Forgery Cue Unraveling
authors: Zonghui Guo, Yingjie Liu, Jie Zhang, Haiyong Zheng, Shiguang Shan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7396-7405
cities: ["Qingdao", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Face_Forgery_Video_Detection_via_Temporal_Forgery_Cue_Unraveling_CVPR_2025_paper.html

abstract:
Face Forgery Video Detection (FFVD) is a critical yet challenging task in determining whether a digital facial video is authentic or forged. Existing FFVD methods typically focus on isolated spatial or coarsely fused spatiotemporal information, failing to leverage temporal forgery cues thus resulting in unsatisfactory performance. We strive to unravel these cues across three progressive levels: momentary anomaly, gradual inconsistency, and cumulative distortion. Accordingly, we design a consecutive correlate module to capture momentary anomaly cues by correlating interactions among consecutive frames. Then, we devise a future guide module to unravel inconsistency cues by iteratively aggregating historical anomaly cues and gradually propagating them into future frames. Finally, we introduce a historical review module that unravels distortion cues via momentum accumulation from future to historical frames. These three modules form our Temporal Forgery Cue Unraveling (TFCU) framework, sequentially highlighting spatial discriminative features by unraveling temporal forgery cues bidirectionally between historical and future frames. Extensive experiments and ablation studies demonstrate the effectiveness of our TFCU method, achieving state-of-the-art performance across diverse unseen datasets and manipulation methods. Code is available at https://github.com/zhenglab/TFCU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Face_Forgery_Video_Detection_via_Temporal_Forgery_Cue_Unraveling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Face_Forgery_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Zonghui and Liu, Yingjie and Zhang, Jie and Zheng, Haiyong and Shan, Shiguang},
    title     = {Face Forgery Video Detection via Temporal Forgery Cue Unraveling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7396-7405}
}

raw_affiliation:
[{"affiliation": "Faculty of Information Science and Engineering, Ocean University of China", "city": "Qingdao", "country": "China"}, {"affiliation": "State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Faculty of Information Science and Engineering, Ocean University of China", "State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences"]


title: Temporally Consistent Object-Centric Learning by Contrasting Slots
authors: Anna Manasyan, Maximilian Seitzer, Filip Radovic, Georg Martius, Andrii Zadaianchuk
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5401-5411
cities: ["T\u00a8ubingen", "Amsterdam", "T\u00a8ubingen"]
countries: ["Germany", "Netherlands", "Germany"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Manasyan_Temporally_Consistent_Object-Centric_Learning_by_Contrasting_Slots_CVPR_2025_paper.html

abstract:
Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream tasks like autonomous control, these representations must be both compositional and temporally consistent. Existing approaches based on recurrent processing often lack long-term stability across frames because their training objective does not enforce temporal consistency. In this work, we introduce a novel object-level temporal contrastive loss for video object-centric models that explicitly promotes temporal consistency. Our method significantly improves the temporal consistency of the learned object-centric representations, yielding more reliable video decompositions that facilitate challenging downstream tasks such as unsupervised object dynamics prediction. Furthermore, the inductive bias added by our loss strongly improves object discovery, leading to state-of-the-art results on both synthetic and real-world datasets, outperforming even weakly-supervised methods that leverage motion masks as additional cues.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Manasyan_Temporally_Consistent_Object-Centric_Learning_by_Contrasting_Slots_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Manasyan_Temporally_Consistent_Object-Centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Manasyan_2025_CVPR,
    author    = {Manasyan, Anna and Seitzer, Maximilian and Radovic, Filip and Martius, Georg and Zadaianchuk, Andrii},
    title     = {Temporally Consistent Object-Centric Learning by Contrasting Slots},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5401-5411}
}

raw_affiliation:
[{"affiliation": "University of T\u00a8ubingen", "city": "T\u00a8ubingen", "country": "Germany"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00a8ubingen", "country": "Germany"}]

affiliations:
["University of T\u00a8ubingen", "University of Amsterdam", "Max Planck Institute for Intelligent Systems"]


title: MC^2: Multi-concept Guidance for Customized Multi-concept Generation
authors: Jiaxiu Jiang, Yabo Zhang, Kailai Feng, Xiaohe Wu, Wenbo Li, Renjing Pei, Fan Li, Wangmeng Zuo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2802-2812
affiliations: ["Harbin Institute of Technology1", "Huawei Noah\u2019s Ark Lab2"]
cities: ["Harbin", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_MC2_Multi-concept_Guidance_for_Customized__Multi-concept_Generation_CVPR_2025_paper.html

abstract:
Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts. However, when extended to multiple concepts, existing methods often struggle with properly integrating different models and avoiding the unintended blending of characteristics from distinct concepts. In this paper, we propose MC^2, a novel approach for multi-concept customization that enhances flexibility and fidelity through inference-time optimization. MC^2 enables the integration of multiple single-concept models with heterogeneous architectures. By adaptively refining attention weights between visual and textual tokens, our method ensures that image regions accurately correspond to their associated concepts while minimizing interference between concepts. Extensive experiments demonstrate that MC^2 outperforms training-based methods in terms of prompt-reference alignment. Furthermore, MC^2 can be seamlessly applied to text-to-image generation, providing robust compositional capabilities. To facilitate the evaluation of multi-concept customization, we also introduce a new benchmark, MC++. The code is available at https://github.com/JIANGJiaXiu/MC-2.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_MC2_Multi-concept_Guidance_for_Customized__Multi-concept_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_MC2_Multi-concept_Guidance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Jiaxiu and Zhang, Yabo and Feng, Kailai and Wu, Xiaohe and Li, Wenbo and Pei, Renjing and Li, Fan and Zuo, Wangmeng},
    title     = {MC{\textasciicircum}2: Multi-concept Guidance for Customized  Multi-concept Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2802-2812}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology1", "city": "Harbin", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab2", "city": "Shenzhen", "country": "China"}]


title: UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12501-12511
affiliations: ["The University of Hong Kong", "Adobe"]
cities: ["Hong Kong", "San Jose"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_UniReal_Universal_Image_Generation_and_Editing_via_Learning_Real-world_Dynamics_CVPR_2025_paper.html

authors:
Xi Chen, Zhifei Zhang, He Zhang, Yuqian Zhou, Soo Ye Kim, Qing Liu, Yijun Li, Jianming Zhang, Nanxuan Zhao, Yilin Wang, Hui Ding, Zhe Lin, Hengshuang Zhao

abstract:
We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles: preserving consistency between inputs and outputs while capturing visual variations. Inspired by recent video generation models that effectively balance consistency and variation across frames, we propose a unifying approach that treats image-level tasks as discontinuous video generation. Specifically, we treat varying numbers of input and output images as frames, enabling seamless support for tasks such as image generation, editing, composition, etc. Although designed for image-level tasks, we leverage videos as a scalable source for universal supervision. UniReal learns world dynamics from large-scale videos, demonstrating advanced capability in handling shadows, reflections, pose variation, and object interaction, while also exhibiting emergent capability for novel applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_UniReal_Universal_Image_Generation_and_Editing_via_Learning_Real-world_Dynamics_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xi and Zhang, Zhifei and Zhang, He and Zhou, Yuqian and Kim, Soo Ye and Liu, Qing and Li, Yijun and Zhang, Jianming and Zhao, Nanxuan and Wang, Yilin and Ding, Hui and Lin, Zhe and Zhao, Hengshuang},
    title     = {UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12501-12511}
}

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


title: Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction
authors: Dong Li, Wenqi Zhong, Wei Yu, Yingwei Pan, Dingwen Zhang, Ting Yao, Junwei Han, Tao Mei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22648-22657
affiliations: ["HiDream.ai Inc.", "Northwest Polytechnical University"]
cities: ["unknown", "Xi'an"]
countries: ["unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Pursuing_Temporal-Consistent_Video_Virtual_Try-On_via_Dynamic_Pose_Interaction_CVPR_2025_paper.html

abstract:
Video virtual try-on aims to seamlessly dress a subject in a video with a specific garment. The primary challenge involves preserving the visual authenticity of the garment while dynamically adapting to the pose and physique of the subject. While existing methods have predominantly focused on image-based virtual try-on, extending these techniques directly to videos often results in temporal inconsistencies. Most current video virtual try-on approaches alleviate this challenge by incorporating temporal modules, yet still overlook the critical spatiotemporal pose interactions between human and garment. Effective pose interactions in videos should not only consider spatial alignment between human and garment poses in each frame but also account for the temporal dynamics of human poses throughout the entire video. With such motivation, we propose a new framework, namely Dynamic Pose Interaction Diffusion Models (DPIDM), to leverage diffusion models to delve into dynamic pose interactions for video virtual try-on. Technically, DPIDM introduces a skeleton-based pose adapter to integrate synchronized human and garment poses into the denoising network. A hierarchical attention module is then exquisitely designed to model intra-frame human-garment pose interactions and long-term human pose dynamics across frames through pose-aware spatial and temporal attention mechanisms. Moreover, DPIDM capitalizes on a temporal regularized attention loss between consecutive frames to enhance temporal consistency. Extensive experiments conducted on VITON-HD, VVT and ViViD datasets demonstrate the superiority of our DPIDM against the baseline methods. Notably, DPIDM achieves VFID score of 0.506 on VVT dataset, leading to 60.5% improvement over the state-of-the-art GPD-VVTO approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Pursuing_Temporal-Consistent_Video_Virtual_Try-On_via_Dynamic_Pose_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Pursuing_Temporal-Consistent_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Dong and Zhong, Wenqi and Yu, Wei and Pan, Yingwei and Zhang, Dingwen and Yao, Ting and Han, Junwei and Mei, Tao},
    title     = {Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22648-22657}
}

raw_affiliation:
[{"affiliation": "HiDream.ai Inc.", "city": "unknown", "country": "unknown"}, {"affiliation": "Northwest Polytechnical University", "city": "Xi'an", "country": "China"}]


title: Exploring Contextual Attribute Density in Referring Expression Counting
authors: Zhicheng Wang, Zhiyu Pan, Zhan Peng, Jian Cheng, Liwen Xiao, Wei Jiang, Zhiguo Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19587-19596
affiliations: ["School of AIA, Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Exploring_Contextual_Attribute_Density_in_Referring_Expression_Counting_CVPR_2025_paper.html

abstract:
Referring expression counting (REC) algorithms are for more flexible and interactive counting ability across varied fine-grained text expressions. However, the requirement for fine-grained attribute understanding poses challenges for prior arts, as they struggle to accurately align attribute information with correct visual patterns. Given the proven importance of "visual density", it is presumed that the limitations of current REC approaches stem from an under-exploration of "contextual attribute density" (CAD). In the scope of REC, we define the CAD as the measure of the information intensity of one certain fine-grained attribute in visual regions. To model the the CAD, we propose a U-shape CAD estimator in which referring expression and multi-scale visual features from GroundingDINO can interact with each other. With additional density supervisions, we can effectively encode CAD, which is subsequently decoded via a novel attention procedure with CAD-refined queries. Integrating all these contributions, our framework significantly outperforms state-of-the-art REC methods, achieves 30% error reduction in counting metics and a 10% improvement in localization accuracy. The surprising results shed lights on the significance of contextual attribute density for REC. Code will be at github.com/Xu3XiWang/CAD-GD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Exploring_Contextual_Attribute_Density_in_Referring_Expression_Counting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Exploring_Contextual_Attribute_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhicheng and Pan, Zhiyu and Peng, Zhan and Cheng, Jian and Xiao, Liwen and Jiang, Wei and Cao, Zhiguo},
    title     = {Exploring Contextual Attribute Density in Referring Expression Counting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19587-19596}
}

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


title: DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24905-24916
cities: ["Menlo Park", "Paris", "Pohang", "Grenoble"]
countries: ["USA", "France", "South Korea", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jose_DINOv2_Meets_Text_A_Unified_Framework_for_Image-_and_Pixel-Level_CVPR_2025_paper.html

authors:
Cijo Jose, Théo Moutakanni, Dahyun Kang, Federico Baldassarre, Timothée Darcet, Hu Xu, Daniel Li, Marc Szafraniec, Michaël Ramamonjisoa, Maxime Oquab, Oriane Siméoni, Huy V. Vo, Patrick Labatut, Piotr Bojanowski

abstract:
Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks. However, unlike vision-language models such as CLIP, self-supervised visual features are not readily aligned with language, hindering their adoption in open-vocabulary tasks. Our method unlocks this new ability for DINOv2, a widely used self-supervised visual encoder. We build upon the LiT training strategy, which trains a text encoder to align with a frozen vision model but leads to unsatisfactory results on dense tasks. We propose several key ingredients to improve performance on both global and dense tasks, such as concatenating the [CLS] token with the patch average to train the alignment and curating data using both text and image modalities. With these, we successfully train a CLIP-like model with only a fraction of the computational cost compared to CLIP while achieving state-of-the-art results in zero-shot classification and open-vocabulary semantic segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jose_DINOv2_Meets_Text_A_Unified_Framework_for_Image-_and_Pixel-Level_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jose_DINOv2_Meets_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jose_2025_CVPR,
    author    = {Jose, Cijo and Moutakanni, Th\'eo and Kang, Dahyun and Baldassarre, Federico and Darcet, Timoth\'ee and Xu, Hu and Li, Daniel and Szafraniec, Marc and Ramamonjisoa, Micha\"el and Oquab, Maxime and Sim\'eoni, Oriane and Vo, Huy V. and Labatut, Patrick and Bojanowski, Piotr},
    title     = {DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24905-24916}
}

raw_affiliation:
[{"affiliation": "Meta FAIR", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Universit\u00b4e Paris-Saclay, CentraleSup\u00b4elec, MICS", "city": "Paris", "country": "France"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP", "city": "Grenoble", "country": "France"}]

affiliations:
["Meta FAIR", "Universit\u00b4e Paris-Saclay, CentraleSup\u00b4elec, MICS", "POSTECH", "Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP"]


title: Learning Affine Correspondences by Integrating Geometric Constraints
authors: Pengju Sun, Banglei Guan, Zhenbao Yu, Yang Shang, Qifeng Yu, Daniel Barath
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27038-27048
cities: ["Changsha", "Changsha", "Zurich", "Budapest"]
countries: ["China", "China", "Switzerland", "Hungary"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Learning_Affine_Correspondences_by_Integrating_Geometric_Constraints_CVPR_2025_paper.html

abstract:
Affine correspondences have received significant attention due to their benefits in tasks like image matching and pose estimation. Existing methods for extracting affine correspondences still have many limitations in terms of performance; thus, exploring a new paradigm is crucial. In this paper, we present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints. Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator. For this purpose, we propose loss functions based on geometric constraints, which can effectively improve accuracy by supervising neural networks to learn feature geometry. The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks. To further demonstrate the effectiveness of the proposed method, we applied it to relative pose estimation. Affine correspondences extracted by our method lead to more accurate poses than the baselines on a range of real-world datasets. The source code will be made public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Learning_Affine_Correspondences_by_Integrating_Geometric_Constraints_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Learning_Affine_Correspondences_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Pengju and Guan, Banglei and Yu, Zhenbao and Shang, Yang and Yu, Qifeng and Barath, Daniel},
    title     = {Learning Affine Correspondences by Integrating Geometric Constraints},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27038-27048}
}

raw_affiliation:
[{"affiliation": "College of Aerospace Science and Engineering, National University of Defense Technology, China.", "city": "Changsha", "country": "China"}, {"affiliation": "Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, China.", "city": "Changsha", "country": "China"}, {"affiliation": "ETH Zurich, Switzerland.", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "HUN-REN SZTAKI, Hungary.", "city": "Budapest", "country": "Hungary"}]

affiliations:
["College of Aerospace Science and Engineering, National University of Defense Technology, China.", "Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, China.", "ETH Zurich, Switzerland.", "HUN-REN SZTAKI, Hungary."]


title: UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning
authors: Weiqi Yan, Lvhai Chen, Huaijia Kou, Shengchuan Zhang, Yan Zhang, Liujuan Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30365-30375
cities: ["Xiamen"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_UCOD-DPL_Unsupervised_Camouflaged_Object_Detection_via_Dynamic_Pseudo-label_Learning_CVPR_2025_paper.html

abstract:
Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-labels using fixed strategies and train 1 x1 convolutional layers as a simple decoder, leading to low performance compared to fully-supervised methods. We emphasize two drawbacks in these approaches: 1). The model is prone to fitting incorrect knowledge due to the pseudo-label containing substantial noise. 2). The simple decoder fails to capture and learn the semantic features of camouflaged objects, especially for small-sized objects, due to the low-resolution pseudo-labels and severe confusion between foreground and background pixels. To this end, we propose a UCOD method with a teacher-student framework via Dynamic Pseudo-label Learning called UCOD-DPL, which contains an Adaptive Pseudo-label Module (APM), a Dual-Branch Adversarial (DBA) decoder, and a Look-Twice mechanism. The APM module adaptively combines pseudo-labels generated by fixed strategies and the teacher model to prevent the model from overfitting incorrect knowledge while preserving the ability for self-correction; the DBA decoder takes adversarial learning of different segmentation objectives, guides the model to overcome the foreground-background confusion of camouflaged objects, and the Look-Twice mechanism mimics the human tendency to zoom in on camouflaged objects and performs secondary refinement on small-sized objects. Extensive experiments show that our method demonstrates outstanding performance, even surpassing some existing fully supervised methods. Our code will be released soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_UCOD-DPL_Unsupervised_Camouflaged_Object_Detection_via_Dynamic_Pseudo-label_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_UCOD-DPL_Unsupervised_Camouflaged_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Weiqi and Chen, Lvhai and Kou, Huaijia and Zhang, Shengchuan and Zhang, Yan and Cao, Liujuan},
    title     = {UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30365-30375}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China.", "city": "Xiamen", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China."]


title: Geometry in Style: 3D Stylization via Surface Normal Deformation
authors: Nam Anh Dinh, Itai Lang, Hyunwoo Kim, Oded Stein, Rana Hanocka
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28456-28467
cities: ["Chicago", "Chicago", "Chicago", "Los Angeles", "Chicago"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dinh_Geometry_in_Style_3D_Stylization_via_Surface_Normal_Deformation_CVPR_2025_paper.html

abstract:
We present Geometry in Style, a new method for identity-preserving mesh stylization. Existing techniques either adhere to the original shape through overly restrictive deformations such as bump maps or significantly modify the input shape using expressive deformations that may introduce artifacts or alter the identity of the source shape. In contrast, we represent a deformation of a triangle mesh as a target normal vector for each vertex neighborhood. The deformations we recover from target normals are expressive enough to enable detailed stylizations yet restrictive enough to preserve the shape's identity. We achieve such deformations using our novel differentiable As-Rigid-As-Possible (dARAP) layer, a neural-network-ready adaptation of the classical ARAP algorithm which we use to solve for per-vertex rotations and deformed vertices. As a differentiable layer, dARAP is paired with a visual loss from a text-to-image model to drive deformations toward style prompts, altogether giving us Geometry in Style.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dinh_Geometry_in_Style_3D_Stylization_via_Surface_Normal_Deformation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dinh_Geometry_in_Style_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dinh_2025_CVPR,
    author    = {Dinh, Nam Anh and Lang, Itai and Kim, Hyunwoo and Stein, Oded and Hanocka, Rana},
    title     = {Geometry in Style: 3D Stylization via Surface Normal Deformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28456-28467}
}

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

affiliations:
["University of Chicago", "University of Chicago", "University of Chicago", "University of Southern California", "University of Chicago"]


title: Multi-modal Vision Pre-training for Medical Image Analysis
authors: Shaohao Rui, Lingzhi Chen, Zhenyu Tang, Lilong Wang, Mianxin Liu, Shaoting Zhang, Xiaosong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5164-5174
affiliations: ["Shanghai Jiao Tong University", "Shanghai AI Laboratory"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rui_Multi-modal_Vision_Pre-training_for_Medical_Image_Analysis_CVPR_2025_paper.html

abstract:
Self-supervised learning has greatly facilitated medical image analysis by suppressing the training data requirement for real-world applications. Current paradigms predominantly rely on self-supervision within uni-modal image data, thereby neglecting the inter-modal correlations essential for effective learning of cross-modal image representations. This limitation is particularly significant for naturally grouped multi-modal data, e.g., multi-parametric MRI scans for a patient undergoing various functional imaging protocols in the same study. To bridge this gap, we conduct a novel multi-modal image pre-training with three proxy tasks to facilitate the learning of cross-modality representations and correlations using multi-modal brain MRI scans (over 2.4 million images in 16,022 scans of 3,755 patients), i.e., cross-modal image reconstruction, modality-aware contrastive learning, and modality template distillation. To demonstrate the generalizability of our pre-trained model, we conduct extensive experiments on various benchmarks with ten downstream tasks. The superior performance of our method is reported in comparison to state-of-the-art pre-training methods, with Dice Score improvement of 0.28%-14.47% across six segmentation benchmarks and a consistent accuracy boost of 0.65%-18.07% in four individual image classification tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rui_Multi-modal_Vision_Pre-training_for_Medical_Image_Analysis_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Rui_2025_CVPR,
    author    = {Rui, Shaohao and Chen, Lingzhi and Tang, Zhenyu and Wang, Lilong and Liu, Mianxin and Zhang, Shaoting and Wang, Xiaosong},
    title     = {Multi-modal Vision Pre-training for Medical Image Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5164-5174}
}

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


authors: Yunxiang Fu, Meng Lou, Yizhou Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19077-19087
affiliations: ["School of Computing and Data Science, The University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_SegMAN_Omni-scale_Context_Modeling_with_State_Space_Models_and_Local_CVPR_2025_paper.html

title:
SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation

abstract:
High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods struggle to possess all these capabilities simultaneously. Hence, we aim to empower segmentation networks to simultaneously carry out efficient global context modeling, high-quality local detail encoding, and rich multi-scale feature representation for varying input resolutions. In this paper, we introduce SegMAN, a novel linear-time model comprising a hybrid feature encoder dubbed SegMAN Encoder, and a decoder based on state space models. Specifically, the SegMAN Encoder synergistically integrates sliding local attention with dynamic state space models, enabling highly efficient global context modeling while preserving fine-grained local details. Meanwhile, the MMSCopE module in our decoder enhances multi-scale context feature extraction and adaptively scales with the input resolution. Our SegMAN-B Encoder achieves 85.1% ImageNet-1k accuracy (+1.5% over VMamba-S with fewer parameters). When paired with our decoder, the full SegMAN-B model achieves 52.6% mIoU on ADE20K (+1.6% over SegNeXt-L with 15% fewer GFLOPs), 83.8% mIoU on Cityscapes (+2.1% over SegFormer-B3 with half the GFLOPs), and 1.6% higher mIoU than VWFormer-B3 on COCO-Stuff with lower GFLOPs. Our code is available at https://github.com/yunxiangfu2001/SegMAN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_SegMAN_Omni-scale_Context_Modeling_with_State_Space_Models_and_Local_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_SegMAN_Omni-scale_Context_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Yunxiang and Lou, Meng and Yu, Yizhou},
    title     = {SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19077-19087}
}

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


title: STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3284-3294
affiliations: ["Zhejiang University", "National University of Singapore"]
cities: ["Hangzhou", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_STEP_Enhancing_Video-LLMs_Compositional_Reasoning_by_Spatio-Temporal_Graph-guided_Self-Training_CVPR_2025_paper.html

authors:
Haiyi Qiu, Minghe Gao, Long Qian, Kaihang Pan, Qifan Yu, Juncheng Li, Wenjie Wang, Siliang Tang, Yueting Zhuang, Tat-Seng Chua

abstract:
Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but struggle with compositional reasoning that requires multi-step spatio-temporal inference across object relations, interactions, and events. The hurdles to enhancing this capability include extensive manual labor, the lack of spatio-temporal compositionality in existing training data and the absence of explicit reasoning supervision. In this paper, we propose STEP, a novel graph-guided self-training method that enables Video-LLMs to generate reasoning-rich fine-tuning data from any raw videos to improve itself. Specifically, we first induce Spatio-Temporal Scene Graph (STSG) representation of diverse videos to capture fine-grained, multi-granular video semantics. Then, the STSGs guide the derivation of multi-step reasoning Question-Answer (QA) data with Chain-of-Thought (CoT) rationales. Both answers and rationales are integrated as training objective, aiming to enhance model's reasoning abilities by supervision over explicit reasoning steps. Experimental results demonstrate the effectiveness of STEP across models of varying scales, with a significant 21.3% improvement in tasks requiring three or more reasoning steps. Furthermore, it achieves superior performance with a minimal amount of self-generated rationale-enriched training samples in both compositional reasoning and comprehensive understanding benchmarks, highlighting the broad applicability and vast potential.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_STEP_Enhancing_Video-LLMs_Compositional_Reasoning_by_Spatio-Temporal_Graph-guided_Self-Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_STEP_Enhancing_Video-LLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Haiyi and Gao, Minghe and Qian, Long and Pan, Kaihang and Yu, Qifan and Li, Juncheng and Wang, Wenjie and Tang, Siliang and Zhuang, Yueting and Chua, Tat-Seng},
    title     = {STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3284-3294}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13178-13188
affiliations: ["UCLA", "Panasonic AI Research", "Salesforce AI Research"]
cities: ["Los Angeles", "Osaka", "San Francisco"]
countries: ["USA", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_OmniFlow_Any-to-Any_Generation_with_Multi-Modal_Rectified_Flows_CVPR_2025_paper.html

authors:
Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Zichun Liao, Yusuke Kato, Kazuki Kozuka, Aditya Grover

abstract:
We introduce OminiFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OminiFlow advances the rectified flow (RF) framework used in text-to-image models to handle the joint distribution of multiple modalities. It outperforms previous any-to-any models on a wide range of tasks, such as text-to-image and text-to-audio synthesis. Our work offers three key contributions: First, we extend RF to a multi-modal setting and introduce a novel guidance mechanism, enabling users to flexibly control the alignment between different modalities in the generated outputs. Second, we propose a novel architecture that extends the text-to-image MMDiT architecture of Stable Diffusion 3 and enables audio and text generation. The extended modules can be efficiently pretrained individually and merged with the vanilla text-to-image MMDiT for fine-tuning. Lastly, we conduct a comprehensive study on the design choices of rectified flow transformers for large-scale audio and text generation, providing valuable insights into optimizing performance across diverse modalities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_OmniFlow_Any-to-Any_Generation_with_Multi-Modal_Rectified_Flows_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_OmniFlow_Any-to-Any_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shufan and Kallidromitis, Konstantinos and Gokul, Akash and Liao, Zichun and Kato, Yusuke and Kozuka, Kazuki and Grover, Aditya},
    title     = {OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13178-13188}
}

raw_affiliation:
[{"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Panasonic AI Research", "city": "Osaka", "country": "Japan"}, {"affiliation": "Salesforce AI Research", "city": "San Francisco", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24939-24949
cities: ["Beijing", "Shanghai", "Hong Kong", "Nanjing", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_PVC_Progressive_Visual_Token_Compression_for_Unified_Image_and_Video_CVPR_2025_paper.html

title:
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models

authors:
Chenyu Yang, Xuan Dong, Xizhou Zhu, Weijie Su, Jiahao Wang, Hao Tian, Zhe Chen, Wenhai Wang, Lewei Lu, Jifeng Dai

abstract:
Large Vision-Language Models (VLMs) have been extended to understand both images and videos. Visual token compression is leveraged to reduce the considerable token length of visual inputs. To meet the needs of different tasks, existing high-performance models usually process images and videos separately with different token compression strategies, limiting the capabilities of combining images and videos. To this end, we extend each image into a "static" video and introduce a unified token compression strategy called Progressive Visual Token Compression (PVC), where the tokens of each frame are progressively encoded and adaptively compressed to supplement the information not extracted from previous frames. Video tokens are efficiently compressed with exploiting the inherent temporal redundancy. Images are repeated as static videos, and the spatial details can be gradually supplemented in multiple frames. PVC unifies the token compressing of images and videos. With a limited number of tokens per frame (64 tokens by default), spatial details and temporal changes can still be preserved. Experiments show that our model achieves state-of-the-art performance across various video understanding benchmarks, including long video tasks and fine-grained short video tasks. Meanwhile, our unified token compression strategy incurs no performance loss on image benchmarks, particularly in detail-sensitive tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_PVC_Progressive_Visual_Token_Compression_for_Unified_Image_and_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_PVC_Progressive_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Chenyu and Dong, Xuan and Zhu, Xizhou and Su, Weijie and Wang, Jiahao and Tian, Hao and Chen, Zhe and Wang, Wenhai and Lu, Lewei and Dai, Jifeng},
    title     = {PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24939-24949}
}

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

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


title: LIM: Large Interpolator Model for Dynamic Reconstruction
authors: Remy Sabathier, Niloy J. Mitra, David Novotny
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6154-6164
affiliations: ["University College London", "Meta", "University College London", "Meta"]
cities: ["London", "Menlo Park", "London", "Menlo Park"]
countries: ["UK", "USA", "UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sabathier_LIM_Large_Interpolator_Model_for_Dynamic_Reconstruction_CVPR_2025_paper.html

abstract:
Reconstructing dynamic assets from video data is central to many in computer vision and graphics tasks. Existing 4D reconstruction approaches are limited by category-specific models or slow optimization-based methods. Inspired by the recent Large Reconstruction Model (LRM), we present the Large Interpolation Model (LIM), a transformer-based feed-forward solution, guided by a novel causal consistency loss, for interpolating implicit 3D representations across time. Given implicit 3D representations at times t_0 and t_1, LIM produces a deformed shape at any continuous time t\in[t_0,t_1] delivering high-quality interpolations in seconds (per frame).Furthermore, LIM allows explicit mesh tracking across time, producing a consistently uv-textured  mesh sequence ready for integration into existing production pipelines. We also use LIM, in conjunction with a diffusion-based multiview generator, to produce dynamic 4D reconstructions from monocular videos. We evaluate LIM on various dynamic datasets, benchmarking against image-space interpolation methods (e.g., FiLM) and direct triplane linear interpolation, and demonstrate clear advantages. In summary, LIM is the first feed-forward model capable of high-speed tracked 4D asset reconstruction across diverse categories.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sabathier_LIM_Large_Interpolator_Model_for_Dynamic_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sabathier_LIM_Large_Interpolator_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sabathier_2025_CVPR,
    author    = {Sabathier, Remy and Mitra, Niloy J. and Novotny, David},
    title     = {LIM: Large Interpolator Model for Dynamic Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6154-6164}
}

raw_affiliation:
[{"affiliation": "University College London", "city": "London", "country": "UK"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University College London", "city": "London", "country": "UK"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


title: Multiple Object Tracking as ID Prediction
authors: Ruopeng Gao, Ji Qi, Limin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27883-27893
cities: ["Nanjing", "Suzhou", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Multiple_Object_Tracking_as_ID_Prediction_CVPR_2025_paper.html

abstract:
Multi-Object Tracking (MOT) has been a long-standing challenge in video understanding. A natural and intuitive approach is to split this task into two parts: object detection and association. Most mainstream methods employ meticulously crafted heuristic techniques to maintain trajectory information and compute cost matrices for object matching. Although these methods can achieve notable tracking performance, they often require a series of elaborate handcrafted modifications while facing complicated scenarios. We believe that manually assumed priors limit the method's adaptability and flexibility in learning optimal tracking capabilities from domain-specific data. Therefore, we introduce a new perspective that treats Multiple Object Tracking as an in-context ID Prediction task, transforming the aforementioned object association into an end-to-end trainable task. Based on this, we propose a simple yet effective method termed MOTIP. Given a set of trajectories carried with ID information, MOTIP directly decodes the ID labels for current detections to accomplish the association process. Without using tailored or sophisticated architectures, our method achieves state-of-the-art results across multiple benchmarks by solely leveraging object-level features as tracking cues. The simplicity and impressive results of MOTIP leave substantial room for future advancements, thereby making it a promising baseline for subsequent research. Our code and checkpoints are released at https://github.com/MCG-NJU/MOTIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Multiple_Object_Tracking_as_ID_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Multiple_Object_Tracking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Ruopeng and Qi, Ji and Wang, Limin},
    title     = {Multiple Object Tracking as ID Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27883-27893}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "China Mobile (Suzhou) Software Technology Co., Ltd.", "city": "Suzhou", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "China Mobile (Suzhou) Software Technology Co., Ltd.", "Shanghai AI Lab"]


title: AutoPresent: Designing Structured Visuals from Scratch
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2902-2911
affiliations: ["University of California, Berkeley", "Carnegie Mellon University"]
cities: ["Berkeley", "Pittsburgh"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ge_AutoPresent_Designing_Structured_Visuals_from_Scratch_CVPR_2025_paper.html

authors:
Jiaxin Ge, Zora Zhiruo Wang, Xuhui Zhou, Yi-Hao Peng, Sanjay Subramanian, Qinyue Tan, Maarten Sap, Alane Suhr, Daniel Fried, Graham Neubig, Trevor Darrell

abstract:
Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first introduce the SlidesBench benchmark, the first benchmark for slide generation with 7k training and 585 testing examples derived from 310 slide decks across 10 domains. SlidesBench supports evaluations that are (i) reference-based to measure similarity to a target slide, and (ii) reference-free to measure the design quality of generated slides alone. We benchmark end-to-end image generation and program generation methods with a variety of models, and find that programmatic methods produce higher-quality slides in user-interactable formats. Built on the success of program generation, we create AutoPresent, an 8B Llama-based model trained on 7k pairs of instructions paired with code for slide generation, and achieve results comparable to the closed-source model GPT-4o. We further explore iterative design refinement where the model is tasked to self-refine its own output, and we found that this process improves the slide's quality. We hope that our work will provide a basis for future work on generating structured visuals.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ge_AutoPresent_Designing_Structured_Visuals_from_Scratch_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ge_AutoPresent_Designing_Structured_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ge_2025_CVPR,
    author    = {Ge, Jiaxin and Wang, Zora Zhiruo and Zhou, Xuhui and Peng, Yi-Hao and Subramanian, Sanjay and Tan, Qinyue and Sap, Maarten and Suhr, Alane and Fried, Daniel and Neubig, Graham and Darrell, Trevor},
    title     = {AutoPresent: Designing Structured Visuals from Scratch},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2902-2911}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos
authors: Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yingda Yin, Yanchao Yang, Qingnan Fan, Baoquan Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16651-16662
affiliations: ["Peking University", "The University of Hong Kong", "Aalto University", "VIVO"]
cities: ["Beijing", "Hong Kong", "Espoo", "Beijing"]
countries: ["China", "China", "Finland", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SLAM3R_Real-Time_Dense_Scene_Reconstruction_from_Monocular_RGB_Videos_CVPR_2025_paper.html

abstract:
In this paper, we introduce SLAM3R, a novel and effective system for real-time, high-quality, dense 3D reconstruction using RGB videos. SLAM3R provides an end-to-end solution by seamlessly integrating local 3D reconstruction and global coordinate registration through feed-forward neural networks. Given an input video, the system first converts it into overlapping clips using a sliding window mechanism. Unlike traditional pose optimization-based methods, SLAM3R directly regresses 3D pointmaps from RGB images in each window and progressively aligns and deforms these local pointmaps to create a globally consistent scene reconstruction - all without explicitly solving any camera parameters. Experiments across datasets consistently show that SLAM3R achieves state-of-the-art reconstruction accuracy and completeness while maintaining real-time performance at 20+ FPS. Code available at: https://github.com/PKU-VCL-3DV/SLAM3R.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SLAM3R_Real-Time_Dense_Scene_Reconstruction_from_Monocular_RGB_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SLAM3R_Real-Time_Dense_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuzheng and Dong, Siyan and Wang, Shuzhe and Yin, Yingda and Yang, Yanchao and Fan, Qingnan and Chen, Baoquan},
    title     = {SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16651-16662}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Aalto University", "city": "Espoo", "country": "Finland"}, {"affiliation": "VIVO", "city": "Beijing", "country": "China"}]


title: PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers
authors: Wooju Lee, Juhye Park, Dasol Hong, Changki Sung, Youngwoo Seo, DongWan Kang, Hyun Myung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21981-21990
affiliations: ["Urban Robotics Lab, School of Elctrical Engineering, KAIST", "Hanwha Aerospace"]
cities: ["Daejeon", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_PIDLoc_Cross-View_Pose_Optimization_Network_Inspired_by_PID_Controllers_CVPR_2025_paper.html

abstract:
Accurate localization is essential for autonomous driving, but GNSS-based methods struggle in challenging environments such as urban canyons. Cross-view pose optimization offers an effective solution for localization by directly estimating vehicle pose using satellite-view images. However, existing methods primarily rely on cross-view features at a given pose, neglecting fine-grained contexts for precision and global contexts for robustness against large initial pose errors. To overcome these limitations, we propose PIDLoc, a novel cross-view pose optimization approach inspired by the proportional-integral-derivative (PID) controller. Using RGB images and LiDAR data, the PIDLoc models cross-view feature relationships through the PID branches and estimates pose via the spatially aware pose estimator (SPE). To enhance localization accuracy, the PID branches leverage feature differences for local context (P), aggregated feature differences for global context (I), and gradients of feature differences for fine-grained context (D). Integrated with the PID branches, the SPE captures spatial relationships within the PID-branch features for consistent localization. Experimental results demonstrate that the PIDLoc achieves state-of-the-art performance in cross-view pose estimation for the KITTI dataset, reducing position error by 37.8% compared with the previous state-of-the-art. Our code is available at https://github.com/url-kaist/PIDLoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_PIDLoc_Cross-View_Pose_Optimization_Network_Inspired_by_PID_Controllers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_PIDLoc_Cross-View_Pose_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Wooju and Park, Juhye and Hong, Dasol and Sung, Changki and Seo, Youngwoo and Kang, DongWan and Myung, Hyun},
    title     = {PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21981-21990}
}

raw_affiliation:
[{"affiliation": "Urban Robotics Lab, School of Elctrical Engineering, KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Hanwha Aerospace", "city": "Seoul", "country": "South Korea"}]


title: VisionArena: 230k Real World User-VLM Conversations with Preference Labels
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3877-3887
cities: ["Stanford", "Berkeley", "Berkeley", "Berkeley", "Berkeley", "Berkeley", "Berkeley", "Berkeley"]
countries: ["USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chou_VisionArena_230k_Real_World_User-VLM_Conversations_with_Preference_Labels_CVPR_2025_paper.html

authors:
Christopher Chou, Lisa Dunlap, Koki Mashita, Krishna Mandal, Trevor Darrell, Ion Stoica, Joseph E. Gonzalez, Wei-Lin Chiang

abstract:
The growing adoption and capabilities of vision-language models (VLMs) demand benchmarks that reflect real-world user interactions. We introduce VisionArena, the largest existing dataset of crowdsourced real-world conversations between users and VLMs. While most visual question-answering datasets focus on close-ended problems or synthetic scenarios, VisionArena contains a wide variety of closed and open ended problems across 230K conversations, 73K unique users, 138 languages, and 45 VLMs. VisionArena consists of VisionArena-Chat, a set of 200k single-turn and multi-turn chat logs with a VLM,  VisionArena-Battle, a set of 30K conversations between a user and 2 anonymous VLMs with preference votes, and VisionArena-Bench, an automatic benchmark consisting of 500 diverse user prompts which can be used to cheaply approximate model rankings.We analyze these datasets and highlight the types of question asked by users, the influence of style on user preference, and areas where models often fall short. We find that more open-ended questions like captioning and humor are heavily influenced by style, which causes certain models like Reka Flash and InternVL which are tuned for style to perform significantly better on these categories compared to other categories. We show that VisionArena-Chat and VisionArena-Battle can be used for post-training to align VLMs to human preferences through supervised fine-tuning. Compared to the popular instruction tuning dataset Llava-Instruct-158K, finetuning the same base model on VisionArena results in a a 17 point improvement on MMMU and a 46 point improvement on the WildVision human preference benchmark. Lastly, we show that running automatic VLM evaluation on VisionArena-Bench results in a model ranking which is largely consistent with major online preference benchmarks. We release both VisionArena and our finetuned model to further VLM development.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chou_VisionArena_230k_Real_World_User-VLM_Conversations_with_Preference_Labels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chou_VisionArena_230k_Real_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chou_2025_CVPR,
    author    = {Chou, Christopher and Dunlap, Lisa and Mashita, Koki and Mandal, Krishna and Darrell, Trevor and Stoica, Ion and Gonzalez, Joseph E. and Chiang, Wei-Lin},
    title     = {VisionArena: 230k Real World User-VLM Conversations with Preference Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3877-3887}
}

raw_affiliation:
[{"affiliation": "Stanford", "city": "Stanford", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]

affiliations:
["Stanford", "UC Berkeley", "UC Berkeley", "UC Berkeley", "UC Berkeley", "UC Berkeley", "UC Berkeley", "UC Berkeley"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2459-2468
affiliations: ["Samsung AI Center", "University of Surrey", "Queen Mary University"]
cities: ["Cambridge", "Guildford", "London"]
countries: ["UK", "UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_FAM_Diffusion_Frequency_and_Attention_Modulation_for_High-Resolution_Image_Generation_CVPR_2025_paper.html

title:
FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion

authors:
Haosen Yang, Adrian Bulat, Isma Hadji, Hai X. Pham, Xiatian Zhu, Georgios Tzimiropoulos, Brais Martinez

abstract:
Diffusion models are proficient at generating high-quality images. They are however effective only when operating at the resolution used during training. Inference at a scaled resolution leads to repetitive patterns and structural distortions.  Retraining at higher resolutions quickly becomes prohibitive. Thus, methods enabling pre-existing diffusion models to operate at flexible test-time resolutions are highly desirable. Previous works suffer from frequent artifacts and often introduce large latency overheads.  We propose two simple modules that combine to solve these issues. We introduce a Frequency Modulation (FM) module that leverages the Fourier domain to improve the global structure consistency, and an Attention Modulation (AM) module which improves the consistency of local texture patterns, a problem largely ignored in prior works. Our method, coined FAM diffusion, can seamlessly integrate into any latent diffusion model and requires no additional training.  Extensive qualitative results highlight the effectiveness of our method in addressing structural and local artifacts, while quantitative results show state-of-the-art performance. Also, our method avoids redundant inference tricks for improved consistency such as patch-based or progressive generation, leading to negligible latency overheads.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_FAM_Diffusion_Frequency_and_Attention_Modulation_for_High-Resolution_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_FAM_Diffusion_Frequency_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Haosen and Bulat, Adrian and Hadji, Isma and Pham, Hai X. and Zhu, Xiatian and Tzimiropoulos, Georgios and Martinez, Brais},
    title     = {FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2459-2468}
}

raw_affiliation:
[{"affiliation": "Samsung AI Center", "city": "Cambridge", "country": "UK"}, {"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}, {"affiliation": "Queen Mary University", "city": "London", "country": "UK"}]


title: DreamOmni: Unified Image Generation and Editing
authors: Bin Xia, Yuechen Zhang, Jingyao Li, Chengyao Wang, Yitong Wang, Xinglong Wu, Bei Yu, Jiaya Jia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28533-28543
affiliations: ["CUHK", "ByteDance Inc", "HKUST"]
cities: ["Hong Kong", "Beijing", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_DreamOmni_Unified_Image_Generation_and_Editing_CVPR_2025_paper.html

abstract:
Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster synergistic benefits across different tasks. However, in computer vision, while text-to-image (T2I) models have significantly improved generation quality through scaling up, their framework design did not initially consider how to unify with downstream tasks, such as various types of editing. To address this, we introduce DreamOmni, a unified model for image generation and editing. We begin by analyzing existing frameworks and the requirements of downstream tasks, proposing a unified framework that integrates both T2I models and various editing tasks. Furthermore, another key challenge is the efficient creation of high-quality editing data, particularly for instruction-based and drag-based editing. To this end, we develop a synthetic data pipeline using sticker-like elements to synthesize accurate, high-quality datasets efficiently, which enables editing data scaling up for unified model training.  For training, DreamOmni jointly trains T2I generation and downstream tasks. T2I training enhances the model's understanding of specific concepts and improves generation quality, while editing training helps the model grasp the nuances of the editing task. This collaboration significantly boosts editing performance. Extensive experiments confirm the effectiveness of DreamOmni. The code and model will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_DreamOmni_Unified_Image_Generation_and_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_DreamOmni_Unified_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Bin and Zhang, Yuechen and Li, Jingyao and Wang, Chengyao and Wang, Yitong and Wu, Xinglong and Yu, Bei and Jia, Jiaya},
    title     = {DreamOmni: Unified Image Generation and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28533-28543}
}

raw_affiliation:
[{"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "ByteDance Inc", "city": "Beijing", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]


title: Hash3D: Training-free Acceleration for 3D Generation
authors: Xingyi Yang, Songhua Liu, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21481-21491
raw_affiliation: [{"affiliation": "National Unversity of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National Unversity of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Hash3D_Training-free_Acceleration_for_3D_Generation_CVPR_2025_paper.html

abstract:
The quality of 3D generative modeling has been notably improved by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per sepresents a critical problem to efficiency.  In this paper, we introduce Hash3D, a universal acceleration for 3D score distillation sampling (SDS) without model training.Central to Hash3D is the observation that images rendered from similar camera positions and diffusion time-steps often have redundant feature maps. By hashing and reusing these feature maps across nearby timesteps and camera angles, Hash3D eliminates unnecessary calculations. We implement this through an adaptive grid-based hashing. As a result, it largely speeds up the process of 3D generation. Surprisingly, this feature-sharing mechanism not only makes generation faster but also improves the smoothness and view consistency of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models, demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency by 1.5~ 4x. Additionally, Hash3D's integration with 3D Gaussian splatting largely speeds up 3D model creation, reducing text-to-3D conversion to about 10 minutes and image-to-3D conversion to 30 seconds. The project page is https://adamdad.github.io/hash3D/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Hash3D_Training-free_Acceleration_for_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Hash3D_Training-free_Acceleration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Xingyi and Liu, Songhua and Wang, Xinchao},
    title     = {Hash3D: Training-free Acceleration for 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21481-21491}
}


title: SemGeoMo: Dynamic Contextual Human Motion Generation with Semantic and Geometric Guidance
authors: Peishan Cong, Ziyi Wang, Yuexin Ma, Xiangyu Yue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17561-17570
affiliations: ["ShanghaiTech University", "The Chinese University of Hong Kong"]
cities: ["Shanghai", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cong_SemGeoMo_Dynamic_Contextual_Human_Motion_Generation_with_Semantic_and_Geometric_CVPR_2025_paper.html

abstract:
Generating reasonable and high-quality human interactive motions in a given dynamic environment is crucial for understanding, modeling, transferring, and applying human behaviors to both virtual and physical robots. In this paper, we introduce an effective method, SemGeoMo, for dynamic contextual human motion generation, which fully leverages the text-affordance-joint multi-level semantic and geometric guidance in the generation process, improving the semantic rationality and geometric correctness of generative motions. Our method achieves state-of-the-art performance on three datasets and demonstrates superior generalization capability for diverse interaction scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cong_SemGeoMo_Dynamic_Contextual_Human_Motion_Generation_with_Semantic_and_Geometric_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cong_SemGeoMo_Dynamic_Contextual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cong_2025_CVPR,
    author    = {Cong, Peishan and Wang, Ziyi and Ma, Yuexin and Yue, Xiangyu},
    title     = {SemGeoMo: Dynamic Contextual Human Motion Generation with Semantic and Geometric Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17561-17570}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: MultiGO: Towards Multi-level Geometry Learning for Monocular 3D Textured Human Reconstruction
authors: Gangjian Zhang, Nanjie Yao, Shunsi Zhang, Hanfeng Zhao, Guoliang Pang, Jian Shu, Hao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 338-347
affiliations: ["HKUST(GZ)", "Guangzhou Quwan Network Technology"]
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MultiGO_Towards_Multi-level_Geometry_Learning_for_Monocular_3D_Textured_Human_CVPR_2025_paper.html

abstract:
This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approaches leverage pre-trained SMPL(-X) estimation models or generative models to provide auxiliary information for human reconstruction. However, these methods capture only the general human body geometry and overlook specific geometric details, leading to inaccurate skeleton reconstruction, incorrect joint positions, and unclear cloth wrinkles. In response to these issues, we propose a multi-level geometry learning framework. Technically, we design three key components: skeleton-level enhancement, joint-level augmentation, and wrinkle-level refinement modules. Specifically, we effectively integrate the projected 3D Fourier features into a Gaussian reconstruction model, introduce perturbations to improve joint depth estimation during training, and refine the human coarse wrinkles by resembling the de-noising process of the diffusion model. Extensive quantitative and qualitative experiments on two test sets show the superior performance of our approach compared to state-of-the-art (SOTA) methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MultiGO_Towards_Multi-level_Geometry_Learning_for_Monocular_3D_Textured_Human_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MultiGO_Towards_Multi-level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Gangjian and Yao, Nanjie and Zhang, Shunsi and Zhao, Hanfeng and Pang, Guoliang and Shu, Jian and Wang, Hao},
    title     = {MultiGO: Towards Multi-level Geometry Learning for Monocular 3D Textured Human Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {338-347}
}

raw_affiliation:
[{"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Guangzhou Quwan Network Technology", "city": "Guangzhou", "country": "China"}]


title: Generative Photomontage
authors: Sean J. Liu, Nupur Kumari, Ariel Shamir, Jun-Yan Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7931-7941
affiliations: ["Carnegie Mellon University", "Reichman University"]
cities: ["Pittsburgh", "Herzliya"]
countries: ["USA", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Generative_Photomontage_CVPR_2025_paper.html

abstract:
Text-to-image models are powerful tools for image creation. However, the generation process is akin to a dice roll and makes it difficult to achieve a single image that captures everything a user wants. In this paper, we propose a framework for creating the desired image by compositing it from various parts of generated images, in essence forming a Generative Photomontage. Given a stack of images generated by ControlNet using the same input condition and different seeds, we let users select desired parts from the generated results using a brush stroke interface. We introduce a novel technique that takes in the user's brush strokes, segments the generated images using a graph-based optimization in diffusion feature space, and then composites the segmented regions via a new feature-space blending method. Our method faithfully preserves the user-selected regions while compositing them harmoniously. We demonstrate that our flexible framework can be used for many applications, including generating new appearance combinations, fixing incorrect shapes and artifacts, and improving prompt alignment. We show compelling results for each application and demonstrate that our method outperforms existing image blending methods and various baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Generative_Photomontage_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Generative_Photomontage_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Sean J. and Kumari, Nupur and Shamir, Ariel and Zhu, Jun-Yan},
    title     = {Generative Photomontage},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7931-7941}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Reichman University", "city": "Herzliya", "country": "Israel"}]


title: Multi-view Reconstruction via SfM-guided Monocular Depth Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5272-5282
affiliations: ["Zhejiang University", "Beijing Normal Univeristy"]
cities: ["Hangzhou", "Beijing"]
countries: ["China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Multi-view_Reconstruction_via_SfM-guided_Monocular_Depth_Estimation_CVPR_2025_paper.html

authors:
Haoyu Guo, He Zhu, Sida Peng, Haotong Lin, Yunzhi Yan, Tao Xie, Wenguan Wang, Xiaowei Zhou, Hujun Bao

abstract:
This paper aims to reconstruct the scene geometry from multi-view images with strong robustness and high quality. Previous learning-based methods incorporate neural networks into the multi-view stereo matching and have shown impressive reconstruction results. However, due to the reliance on matching across input images, they typically suffer from high GPU memory consumption and tend to fail in sparse view scenarios. To overcome this problem, we develop a new pipeline, named Murre, for multi-view geometry reconstruction of 3D scenes based on SfM-guided monocular depth estimation. For input images, Murre first recover the SfM point cloud that captures the global scene structure, and then use it to guide a conditional diffusion model to produce multi-view metric depth maps for the final TSDF fusion. By predicting the depth map from a single image, Murre bypasses the multi-view matching step and naturally resolves the issues of previous MVS-based methods. In addition, the diffusion-based model can easily leverage the powerful priors of 2D foundation models, achieving good generalization ability across diverse real-world scenes. To obtain multi-view consistent depth maps, our key design is providing effective guidance on the diffusion model through the SfM point cloud, which is a condensed form of multi-view information, highlighting the scene's salient structure, and can be readily transformed into point maps to drive the image-space estimation process. We evaluate the reconstruction quality of Murre in various types of real-world datasets including indoor, streetscapes, and aerial scenes, surpassing state-of-the-art MVS-based and implicit neural reconstruction-based methods. The code and supplementary materials are available at https://zju3dv.github.io/murre/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Multi-view_Reconstruction_via_SfM-guided_Monocular_Depth_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Multi-view_Reconstruction_via_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Haoyu and Zhu, He and Peng, Sida and Lin, Haotong and Yan, Yunzhi and Xie, Tao and Wang, Wenguan and Zhou, Xiaowei and Bao, Hujun},
    title     = {Multi-view Reconstruction via SfM-guided Monocular Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5272-5282}
}

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


title: Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
authors: Ruiyi Wang, Yushuo Zheng, Zicheng Zhang, Chunyi Li, Shuaicheng Liu, Guangtao Zhai, Xiaohong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23091-23100
affiliations: ["Shanghai Jiao Tong University", "University of Electronic Science and Technology of China"]
cities: ["Shanghai", "Chengdu"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Learning_Hazing_to_Dehazing_Towards_Realistic_Haze_Generation_for_Real-World_CVPR_2025_paper.html

abstract:
Existing real-world image dehazing methods primarily attempt to fine-tune pre-trained models or adapt their inference procedures, thus heavily relying on the pre-trained models and associated training data. Moreover, restoring heavily distorted information under dense haze requires generative diffusion models, whose potential in dehazing remains underutilized partly due to their lengthy sampling processes. To address these limitations, we introduce a novel hazing-dehazing pipeline consisting of a Realistic Hazy Image Generation framework (HazeGen) and a Diffusion-based Dehazing framework (DiffDehaze). Specifically, HazeGen harnesses robust generative diffusion priors of real-world hazy images embedded in a pre-trained text-to-image diffusion model. By employing specialized hybrid training and blended sampling strategies, HazeGen produces realistic and diverse hazy images as high-quality training data for DiffDehaze. To alleviate the inefficiency and fidelity concerns associated with diffusion-based methods, DiffDehaze adopts an Accelerated Fidelity-Preserving Sampling process (AccSamp). The core of AccSamp is the Tiled Statistical Alignment Operation (AlignOp), which can provide a clean and faithful dehazing estimate within a small fraction of sampling steps to reduce complexity and enable effective fidelity guidance. Extensive experiments demonstrate the superior dehazing performance and visual quality of our approach over existing methods. The code is available at https://github.com/ruiyi-w/Learning-Hazing-to-Dehazing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Learning_Hazing_to_Dehazing_Towards_Realistic_Haze_Generation_for_Real-World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Learning_Hazing_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ruiyi and Zheng, Yushuo and Zhang, Zicheng and Li, Chunyi and Liu, Shuaicheng and Zhai, Guangtao and Liu, Xiaohong},
    title     = {Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23091-23100}
}

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


title: HuMoCon: Concept Discovery for Human Motion Understanding
authors: Qihang Fang, Chengcheng Tang, Bugra Tekin, Shugao Ma, Yanchao Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7179-7190
affiliations: ["The University of Hong Kong", "Meta"]
cities: ["Hong Kong", "Menlo Park"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_HuMoCon_Concept_Discovery_for_Human_Motion_Understanding_CVPR_2025_paper.html

abstract:
We present HuMoCon, a novel motion-video understanding framework designed for advanced human behavior analysis. The core of our method is a human motion concept discovery framework that efficiently trains multi-modal encoders to extract semantically meaningful and generalizable features. HuMoCon addresses key challenges in motion concept discovery for understanding and reasoning, including the lack of explicit multi-modality feature alignment and the loss of high-frequency information in masked autoencoding frameworks. Our approach integrates a feature alignment strategy that leverages video for contextual understanding and motion for fine-grained interaction modeling, further with a velocity reconstruction mechanism to enhance high-frequency feature expression and mitigate temporal over-smoothing. Comprehensive experiments on standard benchmarks demonstrate that HuMoCon enables effective motion concept discovery and significantly outperforms state-of-the-art methods in training large models for human motion understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_HuMoCon_Concept_Discovery_for_Human_Motion_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_HuMoCon_Concept_Discovery_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Qihang and Tang, Chengcheng and Tekin, Bugra and Ma, Shugao and Yang, Yanchao},
    title     = {HuMoCon: Concept Discovery for Human Motion Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7179-7190}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


title: RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges
authors: Thibaut Loiseau, Guillaume Bourmaud
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27070-27080
cities: ["Paris", "Bordeaux"]
countries: ["France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Loiseau_RUBIK_A_Structured_Benchmark_for_Image_Matching_across_Geometric_Challenges_CVPR_2025_paper.html

abstract:
Camera pose estimation is crucial for many computer vision applications, yet existing benchmarks offer limited insight into method limitations across different geometric challenges. We introduce RUBIK, a novel benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels. Using three complementary criteria - overlap, scale ratio, and viewpoint angle - we organize 16.5K image pairs from nuScenes into 33 difficulty levels. Our comprehensive evaluation of 14 methods reveals that while recent detector-free approaches achieve the best performance (>47% success rate), they come with significant computational overhead compared to detector-based methods (150-600ms vs. 40-70ms). Even the best performing method succeeds on only 54.8% of the pairs, highlighting substantial room for improvement, particularly in challenging scenarios combining low overlap, large scale differences, and extreme viewpoint changes. Benchmark will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Loiseau_RUBIK_A_Structured_Benchmark_for_Image_Matching_across_Geometric_Challenges_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Loiseau_RUBIK_A_Structured_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Loiseau_2025_CVPR,
    author    = {Loiseau, Thibaut and Bourmaud, Guillaume},
    title     = {RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27070-27080}
}

raw_affiliation:
[{"affiliation": "LIGM, Ecole des Ponts, Universit\u00b4e Gustave Eiffel, CNRS, France", "city": "Paris", "country": "France"}, {"affiliation": "Laboratoire IMS, Universit\u00b4e de Bordeaux, France", "city": "Bordeaux", "country": "France"}]

affiliations:
["LIGM, Ecole des Ponts, Universit\u00b4e Gustave Eiffel, CNRS, France", "Laboratoire IMS, Universit\u00b4e de Bordeaux, France"]


authors: Jiuyang Dong, Junjun Jiang, Kui Jiang, Jiahan Li, Yongbing Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30818-30828
affiliations: ["Harbin Institute of Technology", "Harbin Institute of Technology, Shenzhen"]
cities: ["Harbin", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Fast_and_Accurate_Gigapixel_Pathological_Image_Classification_with_Hierarchical_Distillation_CVPR_2025_paper.html

title:
Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning

abstract:
Although multi-instance learning (MIL) has succeeded in pathological image classification, it faces the challenge of high inference costs due to processing numerous patches from gigapixel whole slide images (WSIs).To address this, we propose HDMIL, a hierarchical distillation multi-instance learning framework that achieves fast and accurate classification by eliminating irrelevant patches.HDMIL consists of two key components: the dynamic multi-instance network (DMIN) and the lightweight instance pre-screening network (LIPN). DMIN operates on high-resolution WSIs, while LIPN operates on the corresponding low-resolution counterparts.During training, DMIN are trained for WSI classification while generating attention-score-based masks that indicate irrelevant patches.These masks then guide the training of LIPN to predict the relevance of each low-resolution patch.During testing, LIPN first determines the useful regions within low-resolution WSIs, which indirectly enables us to eliminate irrelevant regions in high-resolution WSIs, thereby reducing inference time without causing performance degradation.In addition, we further design the first Chebyshev-polynomials-based Kolmogorov-Arnold classifier in computational pathology, which enhances the performance of HDMIL through learnable activation layers.Extensive experiments on three public datasets demonstrate that HDMIL outperforms previous state-of-the-art methods, e.g., achieving improvements of 3.13% in AUC while reducing inference time by 28.6% on the Camelyon16 dataset.The project will be available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Fast_and_Accurate_Gigapixel_Pathological_Image_Classification_with_Hierarchical_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Fast_and_Accurate_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Jiuyang and Jiang, Junjun and Jiang, Kui and Li, Jiahan and Zhang, Yongbing},
    title     = {Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30818-30828}
}

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


title: FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts
authors: Tongyuan Bai, Wangyuanfan Bai, Dong Chen, Tieru Wu, Manyi Li, Rui Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5893-5903
cities: ["Changchun", "Jinan", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_FreeScene_Mixed_Graph_Diffusion_for_3D_Scene_Synthesis_from_Free_CVPR_2025_paper.html

abstract:
Controllability plays a crucial role in the practical applications of 3D indoor scene synthesis. Existing works either allow rough language-based control, that is convenient but lacks fine-grained scene customization, or employ graph-based control, which offers better controllability but demands considerable knowledge for the cumbersome graph design process. To address these challenges, we present FreeScene, a user-friendly framework that enables both convenient and effective control for indoor scene synthesis. Specifically, FreeScene supports free-form user inputs including text description and/or reference images, allowing users to express versatile design intentions. The user inputs are adequately analyzed and integrated into a graph representation by a VLM-based Graph Designer. We then propose MG-DiT, a Mixed Graph Diffusion Transformer, which performs graph-aware denoising to enhance scene generation. Our MG-DiT not only excels at preserving graph structure but also offers broad applicability to various tasks, including, but not limited to, text-to-scene, graph-to-scene, and rearrangement, all within a single model. Extensive experiments demonstrate that FreeScene provides an efficient and user-friendly solution that unifies text-based and graph-based scene synthesis, outperforming state-of-the-art methods in terms of both generation quality and controllability in a range of applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_FreeScene_Mixed_Graph_Diffusion_for_3D_Scene_Synthesis_from_Free_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_FreeScene_Mixed_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Tongyuan and Bai, Wangyuanfan and Chen, Dong and Wu, Tieru and Li, Manyi and Ma, Rui},
    title     = {FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5893-5903}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Jilin University", "School of Software, Shandong University", "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China"]


title: Rethinking Correspondence-based Category-Level Object Pose Estimation
authors: Huan Ren, Wenfei Yang, Shifeng Zhang, Tianzhu Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1170-1179
cities: ["Hefei", "Hefei", "Hefei", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Rethinking_Correspondence-based_Category-Level_Object_Pose_Estimation_CVPR_2025_paper.html

abstract:
Category-level object pose estimation aims to determine the pose and size of arbitrary objects within given categories. Existing two-stage correspondence-based methods first establish correspondences between camera and object coordinates, and then acquire the object pose using a pose fitting algorithm. In this paper, we conduct a comprehensive analysis of this paradigm and introduce two crucial essentials: 1) shape-sensitive and pose-invariant feature extraction for accurate correspondence prediction, and 2) outlier correspondence removal for robust pose fitting. Based on these insights, we propose a simple yet effective correspondence-based method called SpotPose, which includes two stages. During the correspondence prediction stage, pose-invariant geometric structure of objects is thoroughly exploited to facilitate shape-sensitive holistic interaction among keypoint-wise features. During the pose fitting stage, outlier scores of correspondences are explicitly predicted to facilitate efficient identification and removal of outliers. Experimental results on CAMERA25, REAL275 and HouseCat6D benchmarks demonstrate that the proposed SpotPose outperforms state-of-the-art approaches by a large margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_Rethinking_Correspondence-based_Category-Level_Object_Pose_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Huan and Yang, Wenfei and Zhang, Shifeng and Zhang, Tianzhu},
    title     = {Rethinking Correspondence-based Category-Level Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1170-1179}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "city": "Hefei", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence", "city": "Hefei", "country": "China"}, {"affiliation": "Sangfor Technologies", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Science and Technology of China", "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "State Key Laboratory of General Artificial Intelligence", "Sangfor Technologies"]


title: Curriculum Direct Preference Optimization for Diffusion and Consistency Models
authors: Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Nicu Sebe, Mubarak Shah
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2824-2834
affiliations: ["University of Bucharest", "University of Trento", "University of Central Florida"]
cities: ["Bucharest", "Trento", "Orlando"]
countries: ["Romania", "Italy", "US"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Croitoru_Curriculum_Direct_Preference_Optimization_for_Diffusion_and_Consistency_Models_CVPR_2025_paper.html

abstract:
Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). In this paper, we propose a novel and enhanced version of DPO based on curriculum learning for text-to-image generation. Our method is divided into two training stages. First, a ranking of the  examples generated for each prompt is obtained by employing a reward model. Then, increasingly difficult pairs of examples are sampled and provided to a text-to-image generative (diffusion or consistency) model. Generated samples that are far apart in the ranking are considered to form easy pairs, while those that are close in the ranking form hard pairs. In other words, we use the rank difference between samples as a measure of difficulty. The sampled pairs are split into batches according to their difficulty levels, which are gradually used to train the generative model. Our approach, Curriculum DPO, is compared against state-of-the-art fine-tuning approaches on nine benchmarks, outperforming the competing methods in terms of text alignment, aesthetics and human preference. Our code is available at https://github.com/CroitoruAlin/Curriculum-DPO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Croitoru_Curriculum_Direct_Preference_Optimization_for_Diffusion_and_Consistency_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Croitoru_Curriculum_Direct_Preference_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Croitoru_2025_CVPR,
    author    = {Croitoru, Florinel-Alin and Hondru, Vlad and Ionescu, Radu Tudor and Sebe, Nicu and Shah, Mubarak},
    title     = {Curriculum Direct Preference Optimization for Diffusion and Consistency Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2824-2834}
}

raw_affiliation:
[{"affiliation": "University of Bucharest", "city": "Bucharest", "country": "Romania"}, {"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}, {"affiliation": "University of Central Florida", "city": "Orlando", "country": "US"}]


title: IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera
authors: Jian Huang, Chengrui Dong, Xuanhua Chen, Peidong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26933-26942
affiliations: ["Zhejiang University", "Westlake University", "Northeastern University"]
cities: ["Hangzhou", "Hangzhou", "Shenyang"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_IncEventGS_Pose-Free_Gaussian_Splatting_from_a_Single_Event_Camera_CVPR_2025_paper.html

abstract:
Implicit neural representation and explicit 3D Gaussian Splatting (3D-GS) for novel view synthesis have achieved remarkable progress with frame-based camera (e.g. RGB and RGB-D cameras) recently. Compared to frame-based camera, a novel type of bio-inspired visual sensor, i.e. event camera, has demonstrated advantages in high temporal resolution, high dynamic range, low power consumption and low latency, which make it being favored for many robotic applications. In this work, we present IncEventGS, an incremental 3D Gaussian Splatting reconstruction algorithm with a single event camera, without the assumption of known camera poses. To recover the 3D scene representation incrementally, we exploit the tracking and mapping paradigm of conventional SLAM pipelines for IncEventGS. Given the incoming event stream, the tracker first estimates an initial camera motion based on prior reconstructed 3D-GS scene representation. The mapper then jointly refines both the 3D scene representation and camera motion based on the previously estimated motion trajectory from the tracker. The experimental results demonstrate that IncEventGS delivers superior performance compared to prior NeRF-based methods and other related baselines, even we do not have the ground-truth camera poses. Furthermore, our method can also deliver better performance compared to state-of-the-art event visual odometry methods in terms of camera motion estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_IncEventGS_Pose-Free_Gaussian_Splatting_from_a_Single_Event_Camera_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_IncEventGS_Pose-Free_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Jian and Dong, Chengrui and Chen, Xuanhua and Liu, Peidong},
    title     = {IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26933-26942}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}]


title: OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection
authors: Max Gutbrod, David Rauber, Danilo Weber Nunes, Christoph Palm
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25874-25886
cities: ["Regensburg", "Regensburg"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gutbrod_OpenMIBOOD_Open_Medical_Imaging_Benchmarks_for_Out-Of-Distribution_Detection_CVPR_2025_paper.html

abstract:
The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, near-OOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OOD detection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at https://github.com/remic-othr/OpenMIBOOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gutbrod_OpenMIBOOD_Open_Medical_Imaging_Benchmarks_for_Out-Of-Distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gutbrod_OpenMIBOOD_Open_Medical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gutbrod_2025_CVPR,
    author    = {Gutbrod, Max and Rauber, David and Nunes, Danilo Weber and Palm, Christoph},
    title     = {OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25874-25886}
}

raw_affiliation:
[{"affiliation": "Regensburg Medical Image Computing (ReMIC), OTH Regensburg, Regensburg, 93053, Germany", "city": "Regensburg", "country": "Germany"}, {"affiliation": "Regensburg Center of Health Sciences and Technology (RCHST), OTH Regensburg, Regensburg, 93053, Germany", "city": "Regensburg", "country": "Germany"}]

affiliations:
["Regensburg Medical Image Computing (ReMIC), OTH Regensburg, Regensburg, 93053, Germany", "Regensburg Center of Health Sciences and Technology (RCHST), OTH Regensburg, Regensburg, 93053, Germany"]


title: Detecting Open World Objects via Partial Attribute Assignment
authors: Muli Yang, Gabriel James Goenawan, Huaiyuan Qin, Kai Han, Xi Peng, Yanhua Yang, Hongyuan Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20318-20328
cities: ["Singapore", "Hong Kong", "Chengdu", "Xi'an"]
countries: ["Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Detecting_Open_World_Objects_via_Partial_Attribute_Assignment_CVPR_2025_paper.html

abstract:
Despite being trained on massive data, today's vision foundation models still fall short in detecting open world objects. Apart from recognizing known objects from training, a successful Open World Object Detection (OWOD) system must also be able to detect unknown objects never seen before, without confusing them with the backgrounds. Unlike prevailing prior works that rely on probability models to learn "objectness", we focus on learning fine-grained, class-agnostic attributes, allowing the detection of both known and unknown objects in an explainable manner. In this paper, we propose Partial Attribute Assignment (PASS), aiming to automatically select and optimize a small, relevant subset of attributes from a large attribute pool. Specifically, we model attribute selection as a Partial Optimal Transport (POT) problem between known visual objects and the attribute pool, in which more relevant attributes signify more transported mass. PASS follows a curriculum schedule that progressively selects and optimizes a targeted subset of attributes during training, promoting stability and accuracy. Our method enjoys end-to-end optimization by minimizing the POT distance and the classification loss on known visual objects, demonstrating high training efficiency and superior OWOD performance among extensive experimental evaluations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Detecting_Open_World_Objects_via_Partial_Attribute_Assignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Detecting_Open_World_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Muli and Goenawan, Gabriel James and Qin, Huaiyuan and Han, Kai and Peng, Xi and Yang, Yanhua and Zhu, Hongyuan},
    title     = {Detecting Open World Objects via Partial Attribute Assignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20318-20328}
}

raw_affiliation:
[{"affiliation": "Institute for Infocomm Research (I2R), A*STAR, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Hong Kong, Hong Kong SAR", "city": "Hong Kong", "country": "China"}, {"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["Institute for Infocomm Research (I2R), A*STAR, Singapore", "The University of Hong Kong, Hong Kong SAR", "Sichuan University", "Xidian University"]


title: FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
authors: Alice Heiman, Xiaoman Zhang, Emma Chen, Sung Eun Kim, Pranav Rajpurkar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30787-30796
affiliations: ["Stanford University, USA", "Harvard University, USA"]
cities: ["Stanford", "Cambridge"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heiman_FactCheXcker_Mitigating_Measurement_Hallucinations_in_Chest_X-ray_Report_Generation_Models_CVPR_2025_paper.html

abstract:
Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-hallucinates radiology report measurements by leveraging an improved query-code-update paradigm. Specifically, FactCheXcker employs specialized modules and the code generation capabilities of large language models to solve measurement queries generated based on the original report. After extracting measurable findings, the results are incorporated into an updated report. We evaluate FactCheXcker on endotracheal tube placement, which accounts for an average of 78% of report measurements, using the MIMIC-CXR dataset and 11 medical report-generation models. Our results show that FactCheXcker significantly reduces hallucinations, improves measurement precision, and maintains the quality of the original reports. Specifically, FactCheXcker improves the performance of all 11 models and achieves an average improvement of 135.0% in reducing measurement hallucinations measured by mean absolute error. Code is available at https://github.com/rajpurkarlab/FactCheXcker.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heiman_FactCheXcker_Mitigating_Measurement_Hallucinations_in_Chest_X-ray_Report_Generation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heiman_FactCheXcker_Mitigating_Measurement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Heiman_2025_CVPR,
    author    = {Heiman, Alice and Zhang, Xiaoman and Chen, Emma and Kim, Sung Eun and Rajpurkar, Pranav},
    title     = {FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30787-30796}
}

raw_affiliation:
[{"affiliation": "Stanford University, USA", "city": "Stanford", "country": "USA"}, {"affiliation": "Harvard University, USA", "city": "Cambridge", "country": "USA"}]


title: Neural Inverse Rendering from Propagating Light
authors: Anagh Malik, Benjamin Attal, Andrew Xie, Matthew O'Toole, David B. Lindell
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10534-10544
affiliations: ["University of Toronto", "Vector Institute", "Carnegie Mellon University"]
cities: ["Toronto", "Toronto", "Pittsburgh"]
countries: ["Canada", "Canada", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Malik_Neural_Inverse_Rendering_from_Propagating_Light_CVPR_2025_paper.html

abstract:
We present the first system for physically based, neural inverse rendering from multi-viewpoint videos of propagating light. Our approach relies on a time-resolved extension of neural radiance caching -- a technique that accelerates inverse rendering by storing infinite-bounce radiance arriving at any point from any direction. The resulting model accurately accounts for direct and indirect light transport effects and, when applied to captured measurements from a flash lidar system, enables state-of-the-art 3D reconstruction in the presence of strong indirect light. Further, we demonstrate view synthesis of propagating light, automatic decomposition of captured measurements into direct and indirect components, as well as novel capabilities such as multi-view time-resolved relighting of captured scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Malik_Neural_Inverse_Rendering_from_Propagating_Light_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Malik_Neural_Inverse_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Malik_2025_CVPR,
    author    = {Malik, Anagh and Attal, Benjamin and Xie, Andrew and O'Toole, Matthew and Lindell, David B.},
    title     = {Neural Inverse Rendering from Propagating Light},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10534-10544}
}

raw_affiliation:
[{"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


authors: Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai, Qingming Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24033-24044
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_When_the_Future_Becomes_the_Past_Taming_Temporal_Correspondence_for_CVPR_2025_paper.html

title:
When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning

abstract:
The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to significant progress on multiple video tasks. However, two critical challenges remain: 1) Without human annotations, the random temporal sampling introduces uncertainty, increasing the difficulty of model training. 2) Previous MVM methods primarily recover the masked patches in the pixel space, leading to insufficient information compression for downstream tasks. To address these challenges jointly, we propose a self-supervised framework that leverages Temporal Correspondence for video Representation learning (T-CoRe). For challenge 1), we propose a sandwich sampling strategy that selects two auxiliary frames to reduce reconstruction uncertainty in a two-side-squeezing manner. Addressing challenge 2), we introduce an auxiliary branch into a self-distillation architecture to restore representations in the latent space, generating high-level semantic representations enriched with temporal information. Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning. The code is available at https://github.com/yafeng19/T-CORE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_When_the_Future_Becomes_the_Past_Taming_Temporal_Correspondence_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_When_the_Future_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yang and Xu, Qianqian and Wen, Peisong and Dai, Siran and Huang, Qingming},
    title     = {When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24033-24044}
}

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

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


title: Personalized Preference Fine-tuning of Diffusion Models
authors: Meihua Dang, Anikait Singh, Linqi Zhou, Stefano Ermon, Jiaming Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8020-8030
affiliations: ["Stanford University", "Luma AI"]
cities: ["Stanford", "Unknown"]
countries: ["USA", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dang_Personalized_Preference_Fine-tuning_of_Diffusion_Models_CVPR_2025_paper.html

abstract:
RLHF techniques like DPO can significantly improve the generation quality of text-to-image diffusion models. However, these methods optimize for a single reward that aligns model generation with population-level preferences, neglecting the nuances of individual users' beliefs or values. This lack of personalization limits the efficacy of these models. To bridge this gap, we introduce PPD, a multi-reward optimization objective that aligns diffusion models with personalized preferences. With PPD, a diffusion model learns the individual preferences of a population of users in a few-shot way, enabling generalization to unseen users. Specifically, our approach (1) leverages a vision-language model (VLM) to extract personal preference embeddings from a small set of pairwise preference examples, and then (2) incorporates the embeddings into diffusion models through cross attention. Conditioning on user embeddings, the text-to-image models are fine-tuned with the DPO objective, simultaneously optimizing for alignment with the preferences of multiple users. Empirical results demonstrate that our method effectively optimizes for multiple reward functions and can interpolate between them during inference. In real-world user scenarios, with as few as four preference examples from a new user, our approach achieves an average win rate of 76% over Stable Cascade, generating images that more accurately reflect specific user preferences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dang_Personalized_Preference_Fine-tuning_of_Diffusion_Models_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Dang_2025_CVPR,
    author    = {Dang, Meihua and Singh, Anikait and Zhou, Linqi and Ermon, Stefano and Song, Jiaming},
    title     = {Personalized Preference Fine-tuning of Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8020-8030}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Luma AI", "city": "Unknown", "country": "Unknown"}]


title: DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction
authors: Miaowei Wang, Yibo Zhang, Weiwei Xu, Rui Ma, Changqing Zou, Daniel Morris
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11361-11372
cities: ["Edinburgh", "Hangzhou", "Changchun", "Hangzhou", "East Lansing"]
countries: ["UK", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DecoupledGaussian_Object-Scene_Decoupling_for_Physics-Based_Interaction_CVPR_2025_paper.html

abstract:
We present DecoupledGaussian, a novel system that decouples static objects from their contacted surfaces captured in-the-wild videos, a key prerequisite for realistic Newtonian-based physical simulations. Unlike prior methods focused on synthetic data or elastic jittering along the contact surface, which prevent objects from fully detaching or moving independently, DecoupledGaussian allows for significant positional changes without being constrained by the initial contacted surface. Recognizing the limitations of current 2D inpainting tools for restoring 3D locations, our approach uses joint Poisson fields to repair and expand the Gaussians of both objects and contacted scenes after separation. This is complemented by a multi-carve strategy to refine the object's geometry. Our system enables realistic simulations of decoupling motions, collisions, and fractures driven by user-specified impulses, supporting complex interactions within and across multiple scenes. We validate DecoupledGaussian through a comprehensive user study and quantitative benchmarks. This system enhances digital interaction with objects and scenes in real-world environments, benefiting industries such as VR, robotics, and autonomous driving.  Our project page is at: https://wangmiaowei.github.io/DecoupledGaussian.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DecoupledGaussian_Object-Scene_Decoupling_for_Physics-Based_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DecoupledGaussian_Object-Scene_Decoupling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Miaowei and Zhang, Yibo and Xu, Weiwei and Ma, Rui and Zou, Changqing and Morris, Daniel},
    title     = {DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11361-11372}
}

raw_affiliation:
[{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]

affiliations:
["University of Edinburgh", "Zhejiang Lab", "Jilin University", "State Key Lab of CAD&CG, Zhejiang University", "Michigan State University"]


title: UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing
authors: Yiheng Li, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27805-27815
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_UniPose_A_Unified_Multimodal_Framework_for_Human_Pose_Comprehension_Generation_CVPR_2025_paper.html

abstract:
Human pose plays a crucial role in the digital age. While recent works have achieved impressive progress in understanding and generating human poses, they often support only a single modality of control signals and operate in isolation, limiting their application in real-world scenarios. This paper presents UniPose, a framework employing Large Language Models (LLMs) to comprehend, generate, and edit human poses across various modalities, including images, text, and 3D SMPL poses. Specifically,  we apply a pose tokenizer to convert 3D poses into discrete pose tokens, enabling seamless integration into the LLM within a unified vocabulary. To further enhance the fine-grained pose perception capabilities, we facilitate UniPose with a mixture of visual encoders, among them a pose-specific visual encoder. Benefiting from a unified learning strategy, UniPose effectively transfers knowledge across different pose-relevant tasks, adapts to unseen tasks, and exhibits extended capabilities. This work serves as the first attempt at building a general-purpose framework for pose comprehension, generation, and editing. Extensive experiments highlight UniPose's competitive and even superior performance across various pose-relevant tasks. The code is available at https://github.com/liyiheng23/UniPose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_UniPose_A_Unified_Multimodal_Framework_for_Human_Pose_Comprehension_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_UniPose_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yiheng and Hou, Ruibing and Chang, Hong and Shan, Shiguang and Chen, Xilin},
    title     = {UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27805-27815}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS", "University of Chinese Academy of Sciences"]


title: POMP: Physics-consistent Motion Generative Model through Phase Manifolds
authors: Bin Ji, Ye Pan, Zhimeng Liu, Shuai Tan, Xiaogang Jin, Xiaokang Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22690-22701
cities: ["Shanghai", "Shanghai", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_POMP_Physics-consistent_Motion_Generative_Model_through_Phase_Manifolds_CVPR_2025_paper.html

abstract:
Numerous researches on real-time motion generation primarily focus on kinematic aspects, often resulting in physically implausible outcomes. In this paper, we present POMP ("\underline P hysics-c\underline O nsistent Human \underline M otion \underline P rior through Phase Manifolds"), a novel kinematics-based framework that synthesizes physically consistent motions by leveraging phase manifolds to align motion priors with physics constraints. POMP operates as a frame-by-frame autoregressive model with three core components: a diffusion-based kinematic module, a simulation-based dynamic module, and a phase encoding module. At each timestep, the kinematic module generates an initial target pose, which is subsequently refined by the dynamic module to simulate human-environment interactions. Although the physical simulation ensures adherence to physical laws, it may compromise the kinematic rationality of the posture. Consequently, directly using the simulated result for subsequent frame prediction may lead to cumulative errors. To address this, the phase encoding module performs semantic alignment in the phase manifold. Moreover, we present a pipeline in Unity for generating terrain maps and capturing full-body motion impulses from existing motion capture (MoCap) data. The collected terrain topology and motion impulse data facilitate the training of POMP, enabling it to robustly respond to underlying contactforces and applied dynamics. Extensive evaluations demonstrate the efficacy of POMP across various contexts, terrains, and physical interactions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_POMP_Physics-consistent_Motion_Generative_Model_through_Phase_Manifolds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_POMP_Physics-consistent_Motion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Bin and Pan, Ye and Liu, Zhimeng and Tan, Shuai and Jin, Xiaogang and Yang, Xiaokang},
    title     = {POMP: Physics-consistent Motion Generative Model through Phase Manifolds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22690-22701}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Micro-Nano Engineering Science, Shanghai Jiao Tong University (SJTU)", "city": "Shanghai", "country": "China"}, {"affiliation": "John Hopcroft Center, SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "MoE Key Lab of Artificial Intelligence, SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["State Key Laboratory of Micro-Nano Engineering Science, Shanghai Jiao Tong University (SJTU)", "John Hopcroft Center, SJTU", "MoE Key Lab of Artificial Intelligence, SJTU", "Zhejiang University"]


title: NN-Former: Rethinking Graph Structure in Neural Architecture Representation
authors: Ruihan Xu, Haokui Zhang, Yaowei Wang, Wei Zeng, Shiliang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10004-10014
cities: ["Beijing", "Xi'an", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_NN-Former_Rethinking_Graph_Structure_in_Neural_Architecture_Representation_CVPR_2025_paper.html

abstract:
The growing use of deep learning necessitates efficient network design and deployment, making neural predictors vital for estimating attributes such as accuracy and latency. Recently, Graph Neural Networks (GNNs) and transformers have shown promising performance in representing neural architectures. However, each method has its disadvantages. GNNs lack the capabilities to represent complicated features, while transformers face poor generalization when the depth of architecture grows. To mitigate the above problems, we rethink neural architecture topology and show that sibling nodes are pivotal while overlooked in previous research. Thus we propose a novel predictor leveraging the strengths of GNNs and transformers to learn the enhanced topology. We introduce a novel token mixer that considers siblings, and a new channel mixer named bidirectional graph isomorphism feed-forward network. Our approach consistently achieves promising performance in both accuracy and latency prediction, providing valuable insights for learning Directed Acyclic Graph (DAG) topology. The code is available at https://github.com/XuRuihan/NNFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_NN-Former_Rethinking_Graph_Structure_in_Neural_Architecture_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_NN-Former_Rethinking_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Ruihan and Zhang, Haokui and Wang, Yaowei and Zeng, Wei and Zhang, Shiliang},
    title     = {NN-Former: Rethinking Graph Structure in Neural Architecture Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10004-10014}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}]

affiliations:
["Peking University", "Northwestern Polytechnical University", "Harbin Institute of Technology (Shenzhen)"]


title: DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds
authors: Youyu Chen, Junjun Jiang, Kui Jiang, Xiao Tang, Zhihao Li, Xianming Liu, Yinyu Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11146-11155
affiliations: ["Harbin Institute of Technology", "Huawei Noah\u2019s Ark Lab"]
cities: ["Harbin", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_DashGaussian_Optimizing_3D_Gaussian_Splatting_in_200_Seconds_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, dominate the time cost in primitive optimization. In this paper, we propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS that strips redundant complexity to accelerate 3DGS optimization. Specifically, we formulate 3DGS optimization as progressively fitting 3DGS to higher levels of frequency components in the training views, and propose a dynamic rendering resolution scheme that largely reduces the optimization complexity based on this formulation. Besides, we argue that a specific rendering resolution should cooperate with a proper primitive number for a better balance between computing redundancy and fitting quality, where we schedule the growth of the primitives to synchronize with the rendering resolution. Extensive experiments show that our method accelerates the optimization of various 3DGS backbones by 45.7% on average while preserving the rendering quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_DashGaussian_Optimizing_3D_Gaussian_Splatting_in_200_Seconds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_DashGaussian_Optimizing_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Youyu and Jiang, Junjun and Jiang, Kui and Tang, Xiao and Li, Zhihao and Liu, Xianming and Nie, Yinyu},
    title     = {DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11146-11155}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


title: Reasoning to Attend: Try to Understand How <SEG> Token Works
authors: Rui Qian, Xin Yin, Dejing Dou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24722-24731
cities: ["Shanghai", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_Reasoning_to_Attend_Try_to_Understand_How_SEG_Token_Works_CVPR_2025_paper.html

abstract:
Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on \texttt <SEG>  tokens as a text prompt to jointly optimize the vision-language model (e.g., LLaVA) and the downstream task-specific model (e.g., SAM). However, we observe that little research has looked into how it works. In this work, we first visualize the similarity maps, which are obtained by computing the semantic similarity between the \texttt <SEG>  token and the image token embeddings derived from the last hidden layer in both the LLaVA encoder and SAM decoder. Intriguingly, we have found that a striking consistency holds in terms of activation responses in the similarity map, which reveals that what the \texttt <SEG>  token contributes to is semantic  similarity within image-text pairs. Specifically, the \texttt <SEG>  token, a placeholder expanded in text vocabulary, extensively queries among individual tokenized image patches to match the semantics of an object from text to the paired image, while the Large Language Models (LLMs) are being fine-tuned. Upon the above findings, we present \toolname, which facilitates LMMs' resilient REAsoning capability of where to attenD under the guidance of highly activated points borrowed from similarity maps. Remarkably, READ features an intuitive design, Similarity as Points module (SasP), which can be seamlessly applied to \texttt <SEG> -like paradigms in a plug-and-play fashion. Also, extensive experiments have been conducted on ReasonSeg and RefCOCO(+/g) datasets. To validate whether READ suffers from catastrophic forgetting of previous skills after fine-tuning, we further assess its generation ability on an augmented FP-RefCOCO(+/g) dataset. All codes and models are publicly available at \href https://github.com/rui-qian/READ  https://github.com/rui-qian/READ .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_Reasoning_to_Attend_Try_to_Understand_How_SEG_Token_Works_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qian_Reasoning_to_Attend_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Rui and Yin, Xin and Dou, Dejing},
    title     = {Reasoning to Attend: Try to Understand How \ensuremath{<}SEG\ensuremath{>} Token Works},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24722-24731}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "BEDI Cloud", "city": "Shanghai", "country": "China"}, {"affiliation": "The State Key Laboratory of Blockchain and Data Security, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Computer Science, Fudan University", "BEDI Cloud", "The State Key Laboratory of Blockchain and Data Security, Zhejiang University"]


title: ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29040-29049
affiliations: ["Seoul National University", "LG AI Research", "Allen Institute for AI"]
cities: ["Seoul", "Seoul", "Seattle"]
countries: ["South Korea", "South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_ReSpec_Relevance_and_Specificity_Grounded_Online_Filtering_for_Learning_on_CVPR_2025_paper.html

authors:
Chris Dongjoo Kim, Jihwan Moon, Sangwoo Moon, Heeseung Yun, Sihaeng Lee, Aniruddha Kembhavi, Soonyoung Lee, Gunhee Kim, Sangho Lee, Christopher Clark

abstract:
The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One strategy to enhance the efficiency and effectiveness of learning involves identifying and prioritizing data that enhances performance on target downstream tasks. We propose Relevance and Specificity-based online filtering framework (ReSpec) that selects data based on four criteria: (i) modality alignment for clean data, (ii) task relevance for target focused data, (iii) specificity for informative and detailed data, and (iv) efficiency for low-latency processing. Relevance is determined by the probabilistic alignment of incoming data with downstream tasks, while specificity employs the distance to a root embedding representing the least specific data as an efficient proxy for informativeness. By establishing reference points from target task data, ReSpec filters incoming data in real-time, eliminating the need for extensive storage and compute. Evaluating on large-scale datasets WebVid2M and VideoCC3M, ReSpec attains state-of-the-art performance on five zero-shot video retrieval tasks, using as little as 5% of the data while incurring minimal compute. The source code is available at https://github.com/cdjkim/ReSpec.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ReSpec_Relevance_and_Specificity_Grounded_Online_Filtering_for_Learning_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_ReSpec_Relevance_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Chris Dongjoo and Moon, Jihwan and Moon, Sangwoo and Yun, Heeseung and Lee, Sihaeng and Kembhavi, Aniruddha and Lee, Soonyoung and Kim, Gunhee and Lee, Sangho and Clark, Christopher},
    title     = {ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29040-29049}
}

raw_affiliation:
[{"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "LG AI Research", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}]


title: A Unified Image-Dense Annotation Generation Model for Underwater Scenes
authors: Hongkai Lin, Dingkang Liang, Zhenghao Qi, Xiang Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 961-970
affiliations: ["Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_A_Unified_Image-Dense_Annotation_Generation_Model_for_Underwater_Scenes_CVPR_2025_paper.html

abstract:
Underwater dense prediction, especially depth estimation and semantic segmentation, is crucial for gaining a comprehensive understanding of underwater scenes. Nevertheless, high-quality and large-scale underwater datasets with dense annotations remain scarce because of the complex environment and the exorbitant data collection costs. This paper proposes a unified Text-to-Image and DEnse annotation generation method (TIDE) for underwater scenes. It relies solely on text as input to simultaneously generate realistic underwater images and multiple highly consistent dense annotations. Specifically, we unify the generation of text-to-image and text-to-dense annotations within a single model. The Implicit Layout Sharing mechanism (ILS) and cross-modal interaction method called Time Adaptive Normalization (TAN) are introduced to jointly optimize the consistency between image and dense annotations. We synthesize a large-scale underwater dataset using TIDE to validate the effectiveness of our method in underwater dense prediction tasks. The results demonstrate that our method effectively improves the performance of existing underwater dense prediction models and mitigates the scarcity of underwater data with dense annotations. We hope our method can offer new perspectives on alleviating data scarcity issues in other fields. The code is available at https://github.com/HongkLin/TIDE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_A_Unified_Image-Dense_Annotation_Generation_Model_for_Underwater_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_A_Unified_Image-Dense_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Hongkai and Liang, Dingkang and Qi, Zhenghao and Bai, Xiang},
    title     = {A Unified Image-Dense Annotation Generation Model for Underwater Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {961-970}
}

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


title: PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models
authors: Mohamed Dhouib, Davide Buscaldi, Sonia Vanier, Aymen Shabou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14582-14592
cities: ["Paris", "Paris", "Paris"]
countries: ["France", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dhouib_PACT_Pruning_and_Clustering-Based_Token_Reduction_for_Faster_Visual_Language_CVPR_2025_paper.html

abstract:
Visual Language Models require substantial computational resources for inference due to the additional input tokens needed to represent visual information. However, these visual tokens often contain redundant and unimportant information, resulting in an unnecessarily high number of tokens. To address this, we introduce PACT, a method that reduces inference time and memory usage by pruning irrelevant tokens and merging visually redundant ones at an early layer of the language model. Our approach uses a novel importance metric to identify unimportant tokens without relying on attention scores, making it compatible with FlashAttention. We also propose a novel clustering algorithm, called Distance Bounded Density Peak Clustering, which efficiently clusters visual tokens while constraining the distances between elements within a cluster by a predefined threshold. We demonstrate the effectiveness of PACT through extensive experiments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dhouib_PACT_Pruning_and_Clustering-Based_Token_Reduction_for_Faster_Visual_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dhouib_PACT_Pruning_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dhouib_2025_CVPR,
    author    = {Dhouib, Mohamed and Buscaldi, Davide and Vanier, Sonia and Shabou, Aymen},
    title     = {PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14582-14592}
}

raw_affiliation:
[{"affiliation": "LIX, \u00b4Ecole Polytechnique, IP Paris, France", "city": "Paris", "country": "France"}, {"affiliation": "LIPN, Universit\u00b4e Sorbonne Paris Nord, France", "city": "Paris", "country": "France"}, {"affiliation": "DataLab Groupe, Cr\u00b4edit Agricole S.A, France", "city": "Paris", "country": "France"}]

affiliations:
["LIX, \u00b4Ecole Polytechnique, IP Paris, France", "LIPN, Universit\u00b4e Sorbonne Paris Nord, France", "DataLab Groupe, Cr\u00b4edit Agricole S.A, France"]


title: R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
authors: Xudong Jiang, Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11536-11546
affiliations: ["Department of Computer Science, ETH Zurich", "Microsoft Spatial AI Lab, Zurich"]
cities: ["Zurich", "Zurich"]
countries: ["Switzerland", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_R-SCoRe_Revisiting_Scene_Coordinate_Regression_for_Robust_Large-Scale_Visual_Localization_CVPR_2025_paper.html

abstract:
Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remains a less robust alternative to feature matching methods. This work aims to close the gap. We introduce a covisibility graph-based global encoding learning and data augmentation strategy, along with a depth-adjusted reprojection loss to facilitate implicit triangulation. Additionally, we revisit the network architecture and local feature extraction module. Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision. On Aachen Day-Night, we are 10x more accurate than previous SCR methods with similar map sizes and require at least 5x smaller map sizes than any other SCR method while still delivering superior accuracy. Code is available at: https://github.com/cvg/scrstudio.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_R-SCoRe_Revisiting_Scene_Coordinate_Regression_for_Robust_Large-Scale_Visual_Localization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_R-SCoRe_Revisiting_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Xudong and Wang, Fangjinhua and Galliani, Silvano and Vogel, Christoph and Pollefeys, Marc},
    title     = {R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11536-11546}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Microsoft Spatial AI Lab, Zurich", "city": "Zurich", "country": "Switzerland"}]


title: DynRefer: Delving into Region-level Multimodal Tasks via Dynamic Resolution
authors: Yuzhong Zhao, Feng Liu, Yue Liu, Mingxiang Liao, Chen Gong, Qixiang Ye, Fang Wan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24742-24752
affiliations: ["University of Chinese Academy of Sciences", "University of Virginia"]
cities: ["Beijing", "Charlottesville"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DynRefer_Delving_into_Region-level_Multimodal_Tasks_via_Dynamic_Resolution_CVPR_2025_paper.html

abstract:
One important task of multimodal models is to translate referred image regions to human preferred language descriptions. Existing methods, however, ignore the resolution adaptability needs of different tasks, which hinders them to find out precise language descriptions. In this study, we propose a DynRefer approach, to pursue high-accuracy region-level referring through mimicking the resolution adaptability of human visual cognition. During training, DynRefer stochastically aligns language descriptions of multimodal tasks with images of multiple resolutions, which are constructed by nesting a set of random views around the referred region. This process essentially constructs a set of region representations, where suitable representations for specific tasks can be matched. During inference, DynRefer performs selectively multimodal referring by sampling proper region representations for tasks from the set of views based on image and task priors. This allows the visual information for referring to better match human preferences, thereby improving the representational adaptability of region-level multimodal models. Experiments show that DynRefer brings mutual improvement upon broad tasks including region-level captioning, open-vocabulary region recognition and attribute detection. Furthermore, DynRefer achieves state-of-the-art results on multiple region-level multimodal tasks using a single model. Code is available at https://github.com/callsys/DynRefer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DynRefer_Delving_into_Region-level_Multimodal_Tasks_via_Dynamic_Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DynRefer_Delving_into_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Yuzhong and Liu, Feng and Liu, Yue and Liao, Mingxiang and Gong, Chen and Ye, Qixiang and Wan, Fang},
    title     = {DynRefer: Delving into Region-level Multimodal Tasks via Dynamic Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24742-24752}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}]


title: Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy
authors: Joonhyun Jeong, Seyun Bae, Yeonsung Jung, Jaeryong Hwang, Eunho Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29937-29946
cities: ["Seoul", "Daejeon", "Changwon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_Playing_the_Fool_Jailbreaking_LLMs_and_Multimodal_LLMs_with_Out-of-Distribution_CVPR_2025_paper.html

abstract:
Despite the remarkable versatility of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) to generalize across both language and vision tasks, LLMs and MLLMs have shown vulnerability to jailbreaking, generating textual outputs that undermine safety, ethical, and bias standards when exposed to harmful or sensitive inputs. With the recent advancement of safety alignment via preference-tuning from human feedback, LLMs and MLLMs have been equipped with safety guardrails to yield safe, ethical, and fair responses with regard to harmful inputs. However, despite the significance of safety alignment, research on the vulnerabilities remains largely underexplored. In this paper, we investigate the unexplored vulnerability of the safety alignment, examining its ability to consistently provide safety guarantees for out-of-distribution(OOD)-ifying harmful inputs that may fall outside the aligned data distribution. Our key observation is that OOD-ifying the vanilla harmful inputs highly increases the uncertainty of the model to discern the malicious intent within the input, leading to a higher chance of being jailbroken. Exploiting this vulnerability, we propose JOOD, a new Jailbreak framework via OOD-ifying inputs beyond the safety alignment. We explore various off-the-shelf visual and textual transformation techniques for OOD-ifying the harmful inputs. Notably, we observe that even simple mixing-based techniques such as image mixup prove highly effective in increasing the uncertainty of the model, thereby facilitating the bypass of the safety alignment. Experiments across diverse jailbreak scenarios demonstrate that JOOD effectively jailbreaks recent proprietary LLMs and MLLMs such as GPT-4 and o1 with high attack success rate, which previous attack approaches have consistently struggled to jailbreak. Code is available at https://github.com/naver-ai/JOOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_Playing_the_Fool_Jailbreaking_LLMs_and_Multimodal_LLMs_with_Out-of-Distribution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_Playing_the_Fool_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Joonhyun and Bae, Seyun and Jung, Yeonsung and Hwang, Jaeryong and Yang, Eunho},
    title     = {Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29937-29946}
}

raw_affiliation:
[{"affiliation": "NA VER Cloud, ImageVision", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Republic of Korea Naval Academy", "city": "Changwon", "country": "South Korea"}, {"affiliation": "AITRICS", "city": "Seoul", "country": "South Korea"}]

affiliations:
["NA VER Cloud, ImageVision", "Korea Advanced Institute of Science and Technology (KAIST)", "Republic of Korea Naval Academy", "AITRICS"]


title: Style Evolving along Chain-of-Thought for Unknown-Domain Object Detection
authors: Zihao Zhang, Aming Wu, Yahong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14225-14234
cities: ["Tianjin", "Xi'an"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Style_Evolving_along_Chain-of-Thought_for_Unknown-Domain_Object_Detection_CVPR_2025_paper.html

abstract:
Recently, a task of Single-Domain Generalized Object Detection (Single-DGOD) is proposed,  aiming to generalize a detector to multiple unknown domains never seen before during training. Due to the unavailability of target-domain data, some methods leverage the multimodal capabilities of vision-language models, using textual prompts to estimate cross-domain information, enhancing the model's generalization capability. These methods typically use a single textual prompt, referred to as the one-step prompt method. However, when dealing with complex styles, such as the combination of rain and night, we observe that the performance of the one-step prompt method tends to be relatively weak. The reason may be that many scenes incorporate a single style and a combination of multiple styles. The one-step prompt method may not effectively synthesize combined information involving various styles. To address this limitation, we propose a new method, i.e., Style Evolving along Chain-of-Thought, which aims to progressively integrate and expand style information along the chain of thought, enabling the continual evolution of styles. Specifically, by progressively refining style descriptions and guiding the diverse evolution of styles, this method enhances the simulation of various style characteristics, enabling the model to learn and adapt to subtle differences more effectively. Additionally, it exposes the model to a broader range of style features with different data distributions, thereby enhancing its generalization capability in unseen domains. The significant performance gains over five adverse-weather scenarios and the Real to Art benchmark demonstrate the superiorities of our method. Our code is available at https://github.com/ZZ2490/SE-COT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Style_Evolving_along_Chain-of-Thought_for_Unknown-Domain_Object_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zihao and Wu, Aming and Han, Yahong},
    title     = {Style Evolving along Chain-of-Thought for Unknown-Domain Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14225-14234}
}

raw_affiliation:
[{"affiliation": "College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "School of Electronic Engineering, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["College of Intelligence and Computing, Tianjin University", "School of Electronic Engineering, Xidian University"]


authors: Kun Yang, Yuxiang Liu, Zeyu Cui, Yu Liu, Maojun Zhang, Shen Yan, Qing Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 691-700
affiliations: ["Northwestern Polytechnical University", "National University of Defense Technology"]
cities: ["Xi'an", "Changsha"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_NTR-Gaussian_Nighttime_Dynamic_Thermal_Reconstruction_with_4D_Gaussian_Splatting_Based_CVPR_2025_paper.html

title:
NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics

abstract:
Thermal infrared imaging enables a non-invasive measurement of the surface temperature of objects with all-weather applicability. Leveraging such techniques for 3D reconstruction can accurately reflect the temperature distribution of a scene, thereby supporting applications such as building monitoring and energy management. However, existing approaches predominantly focus on static 3D reconstruction for a single time period, overlooking the dynamic nature of thermal radiation phenomena, and failing to predict or analyze temperature variations over time. In this paper, we introduce a novel method, termed NTR-Gaussian, grounded in thermodynamics to address the challenge of nighttime dynamic thermal reconstruction using 4D Gaussian Splatting. Specifically, We utilize neural networks to predict thermodynamic parameters, such as emissivity, convective heat transfer coefficient, and heat capacity, etc. By means of integration, we numerically solve the infrared temperature of the scene at each moment during the night, so as to predict the temperature of the nighttime scene more accurately. To further advance research in this domain, we release a comprehensive dataset of dynamic thermal reconstruction spanning four distinct regions. Extensive experiments demonstrate that NTR-Gaussian significantly outperforms comparison methods in thermal reconstruction, achieving a predicted temperature error within 1 degree Celsius.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_NTR-Gaussian_Nighttime_Dynamic_Thermal_Reconstruction_with_4D_Gaussian_Splatting_Based_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_NTR-Gaussian_Nighttime_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Kun and Liu, Yuxiang and Cui, Zeyu and Liu, Yu and Zhang, Maojun and Yan, Shen and Wang, Qing},
    title     = {NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {691-700}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}]


authors: Suyoung Lee, Jaeyoung Chung, Kihoon Kim, Jaeyoo Huh, Gunhee Lee, Minsoo Lee, Kyoung Mu Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16356-16365
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["Korea", "Korea", "Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_OmniSplat_Taming_Feed-Forward_3D_Gaussian_Splatting_for_Omnidirectional_Images_with_CVPR_2025_paper.html

title:
OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities

abstract:
Feed-forward 3D Gaussian splatting (3DGS) models have gained significant popularity due to their ability to generate scenes immediately without needing per-scene optimization. Although omnidirectional images are becoming more popular since they reduce the computation required for image stitching to composite a holistic scene, existing feed-forward models are only designed for perspective images. The unique optical properties of omnidirectional images make it difficult for feature encoders to correctly understand the context of the image and make the Gaussian non-uniform in space, which hinders the image quality synthesized from novel views. We propose OmniSplat, a training-free fast feed-forward 3DGS generation framework for omnidirectional images. We adopt a Yin-Yang grid and decompose images based on it to reduce the domain gap between omnidirectional and perspective images. The Yin-Yang grid can use the existing CNN structure as it is, but its quasi-uniform characteristic allows the decomposed image to be similar to a perspective image, so it can exploit the strong prior knowledge of the learned feed-forward network. OmniSplat demonstrates higher reconstruction accuracy than existing feed-forward networks trained on perspective images. The code is available on: https://github.com/esw0116/OmniSplat.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_OmniSplat_Taming_Feed-Forward_3D_Gaussian_Splatting_for_Omnidirectional_Images_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_OmniSplat_Taming_Feed-Forward_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Suyoung and Chung, Jaeyoung and Kim, Kihoon and Huh, Jaeyoo and Lee, Gunhee and Lee, Minsoo and Lee, Kyoung Mu},
    title     = {OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16356-16365}
}

raw_affiliation:
[{"affiliation": "Dept. of ECE & ASRI, Seoul National University", "city": "Seoul", "country": "Korea"}, {"affiliation": "IPAI, Seoul National University", "city": "Seoul", "country": "Korea"}, {"affiliation": "LG AI Research", "city": "Seoul", "country": "Korea"}]

affiliations:
["Dept. of ECE & ASRI, Seoul National University", "IPAI, Seoul National University", "LG AI Research"]


title: VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
authors: Zhongwei Ren, Yunchao Wei, Xun Guo, Yao Zhao, Bingyi Kang, Jiashi Feng, Xiaojie Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29029-29039
affiliations: ["Beijing Jiaotong University", "ByteDance Seed"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_VideoWorld_Exploring_Knowledge_Learning_from_Unlabeled_Videos_CVPR_2025_paper.html

abstract:
This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual change is crucial for knowledge acquisition. To improve both the efficiency and efficacy of this process, we introduce the Latent Dynamics Model (LDM) as a key component of VideoWorld. Remarkably, VideoWorld reaches a 5-dan professional level in the Video-GoBench with just a 300-million-parameter model, without relying on search algorithms or reward mechanisms typical in reinforcement learning. In robotic tasks, VideoWorld effectively learns diverse control operations and generalizes across environments, approaching the performance of oracle models in CALVIN and RLBench. This study opens new avenues for knowledge acquisition from visual data, with all code, data, and models open-sourced for further research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_VideoWorld_Exploring_Knowledge_Learning_from_Unlabeled_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_VideoWorld_Exploring_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Zhongwei and Wei, Yunchao and Guo, Xun and Zhao, Yao and Kang, Bingyi and Feng, Jiashi and Jin, Xiaojie},
    title     = {VideoWorld: Exploring Knowledge Learning from Unlabeled Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29029-29039}
}

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


title: FSHNet: Fully Sparse Hybrid Network for 3D Object Detection
authors: Shuai Liu, Mingyue Cui, Boyang Li, Quanmin Liang, Tinghe Hong, Kai Huang, Yunxiao Shan, Kai Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8900-8909
cities: ["Guangzhou", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_FSHNet_Fully_Sparse_Hybrid_Network_for_3D_Object_Detection_CVPR_2025_paper.html

abstract:
Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature extraction capability, while the latter hinders network optimization. To address these challenges, we introduce the Fully Sparse Hybrid Network (FSHNet). FSHNet incorporates a proposed SlotFormer block to enhance the long-range feature extraction capability of existing sparse encoders. The SlotFormer divides sparse voxels using a slot partition approach, which, compared to traditional window partition, provides a larger receptive field. Additionally, we propose a dynamic sparse label assignment strategy to deeply optimize the network by providing more high-quality positive samples. To further enhance performance, we introduce a sparse upsampling module to refine downsampled voxels, preserving fine-grained details crucial for detecting small objects. Extensive experiments on the Waymo, nuScenes, and Argoverse2 benchmarks demonstrate the effectiveness of FSHNet. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_FSHNet_Fully_Sparse_Hybrid_Network_for_3D_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_FSHNet_Fully_Sparse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Shuai and Cui, Mingyue and Li, Boyang and Liang, Quanmin and Hong, Tinghe and Huang, Kai and Shan, Yunxiao and Huang, Kai},
    title     = {FSHNet: Fully Sparse Hybrid Network for 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8900-8909}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University", "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University", "School of Artificial Intelligence, Sun Yat-sen University"]


title: 3D-SLNR: A Super Lightweight Neural Representation for Large-scale 3D Mapping
authors: Chenhui Shi, Fulin Tang, Ning An, Yihong Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27233-27242
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_3D-SLNR_A_Super_Lightweight_Neural_Representation_for_Large-scale_3D_Mapping_CVPR_2025_paper.html

abstract:
We propose 3D-SLNR, a new and ultra-lightweight neural representation with outstanding performance for large-scale 3D mapping. The representation defines a global signed distance function (SDF) in near-surface space based on a set of band-limited local SDFs anchored at support points sampled from point clouds. These SDFs are parameterized only by a tiny multi-layer perceptron (MLP) with no latent features, and the state of each SDF is modulated by three learnable geometric properties: position, rotation, and scaling, which make the representation adapt to complex geometries. Then, we develop a novel parallel algorithm tailored for this unordered representation to efficiently detect local SDFs where each sampled point is located, allowing for real-time updates of local SDF states during training. Additionally, a prune-and-expand strategy is introduced to enhance adaptability further. The synergy of our low-parameter model and its adaptive capabilities results in an extremely compact representation with excellent expressiveness. Extensive experiments demonstrate that our method achieves state-of-the-art reconstruction performance with less than 1/5 of the memory footprint compared with previous advanced methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_3D-SLNR_A_Super_Lightweight_Neural_Representation_for_Large-scale_3D_Mapping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_3D-SLNR_A_Super_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Chenhui and Tang, Fulin and An, Ning and Wu, Yihong},
    title     = {3D-SLNR: A Super Lightweight Neural Representation for Large-scale 3D Mapping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27233-27242}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "China Coal Research Institute", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Intelligent Coal Mining and Strata Control", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "China Coal Research Institute", "State Key Laboratory of Intelligent Coal Mining and Strata Control"]


title: UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
authors: Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, Jinqiao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15194-15203
cities: ["Beijing", "Beijing", "Beijing", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_UniVAD_A_Training-free_Unified_Model_for_Few-shot_Visual_Anomaly_Detection_CVPR_2025_paper.html

abstract:
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields. Due to the domain gaps between these fields, existing VAD methods are typically tailored to each domain, with specialized detection techniques and model architectures that are difficult to generalize across different domains. Moreover, even within the same domain, current VAD approaches often require large amounts of normal samples to train class-specific models, resulting in poor generalizability and hindering unified evaluation across domains. To address this issue, we propose a generalized few-shot VAD method, UniVAD, capable of detecting anomalies across various domains, with a training-free unified model. UniVAD only needs few normal samples as references during testing to detect anomalies in previously unseen objects, without training on the specific domain. Specifically, UniVAD employs a Contextual Component Clustering (C3) module based on clustering and vision foundation models to segment components within the image accurately, and leverages Component-Aware Patch Matching (CAPM) and Graph-Enhanced Component Modeling (GECM) modules to detect anomalies at different semantic levels, which are aggregated to produce the final detection result. We conduct experiments on nine datasets spanning industrial, logical, and medical fields, and the results demonstrate that UniVAD achieves state-of-the-art performance in few-shot anomaly detection tasks across multiple domains, outperforming domain-specific anomaly detection models. Code is available at https://github.com/FantasticGNU/UniVAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_UniVAD_A_Training-free_Unified_Model_for_Few-shot_Visual_Anomaly_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Zhaopeng and Zhu, Bingke and Zhu, Guibo and Chen, Yingying and Tang, Ming and Wang, Jinqiao},
    title     = {UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15194-15203}
}

raw_affiliation:
[{"affiliation": "Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Objecteye Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University", "city": "Guangzhou", "country": "China"}]

affiliations:
["Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences", "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "Objecteye Inc.", "Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University"]


title: STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation
authors: Yisi Luo, Xile Zhao, Kai Ye, Deyu Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25930-25939
cities: ["Xi'an", "Chengdu", "Shenzhen", "Macao"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_STINR_Deciphering_Spatial_Transcriptomics_via_Implicit_Neural_Representation_CVPR_2025_paper.html

abstract:
Spatial transcriptomics (ST) are emerging technologies that reveal spatial distributions of gene expressions within tissues, serving as important ways to uncover biological insights. However, the irregular spatial profiles and variability of genes make it challenging to integrate spatial information with gene expression under a computational framework. Current algorithms mostly utilize spatial graph neural networks to encode spatial information, which may incur increased computational costs and may not be flexible enough to depict complex spatial configurations. In this study, we introduce a concise yet effective representation framework, STINR, for deciphering ST data. STINR leverages an implicit neural representation (INR) to continuously represent ST data, which efficiently characterizes spatial and slice-wise correlations of ST data by inheriting the implicit smoothness of INR. STINR allows easier integration of multiple slices and multi-omics without any alignment, and serves as a potent tool for various biological tasks including gene imputation, gene denoising, spatial domain detection, and cell-type deconvolution stemed from ST data. In particular, STINR identifies the thinnest cortex layer in the dorsolateral prefrontal cortex which previous methods were unable to achieve, and more accurately identifies tumor regions in the human squamous cell carcinoma, showcasing its practical value for biological discoveries.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_STINR_Deciphering_Spatial_Transcriptomics_via_Implicit_Neural_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_STINR_Deciphering_Spatial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Yisi and Zhao, Xile and Ye, Kai and Meng, Deyu},
    title     = {STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25930-25939}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Macau University of Science and Technology", "city": "Macao", "country": "China"}]

affiliations:
["Xi\u2019an Jiaotong University", "University of Electronic Science and Technology of China", "Pengcheng Laboratory", "Macau University of Science and Technology"]


title: Remote Photoplethysmography in Real-World and Extreme Lighting Scenarios
authors: Hang Shao, Lei Luo, Jianjun Qian, Mengkai Yan, Shuo Chen, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10858-10867
affiliations: ["PCA Lab\u2020, Nanjing University of Science and Technology", "Nanjing University"]
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_Remote_Photoplethysmography_in_Real-World_and_Extreme_Lighting_Scenarios_CVPR_2025_paper.html

abstract:
Physiological activities can be manifested by the sensitive changes in facial imaging. While they are barely observable to our eyes, computer vision manners can, and the derived remote photoplethysmography (rPPG) has shown considerable promise. However, existing studies mainly rely on spatial skin recognition and temporal rhythmic interactions, so they focus on identifying explicit features under ideal light conditions, but perform poorly in-the-wild with intricate obstacles and extreme illumination exposure. In this paper, we propose an end-to-end video transformer model for rPPG. It strives to eliminate complex and unknown external time-varying interferences, whether they are sufficient to occupy subtle biosignal amplitudes or exist as periodic perturbations that hinder network training. In the specific implementation, we utilize global interference sharing, subject background reference, and self-supervised disentanglement to eliminate interference, and further guide learning based on spatiotemporal filtering, reconstruction guidance, and frequency domain and biological prior constraints to achieve effective rPPG. To the best of our knowledge, this is the first robust rPPG model for real outdoor scenarios based on natural face videos, and is lightweight to deploy. Extensive experiments show the competitiveness and performance of our model in rPPG prediction across datasets and scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_Remote_Photoplethysmography_in_Real-World_and_Extreme_Lighting_Scenarios_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_Remote_Photoplethysmography_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Hang and Luo, Lei and Qian, Jianjun and Yan, Mengkai and Chen, Shuo and Yang, Jian},
    title     = {Remote Photoplethysmography in Real-World and Extreme Lighting Scenarios},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10858-10867}
}

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


authors: Muhammad Abdullah Jamal, Omid Mohareri
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17947-17957
raw_affiliation: [{"affiliation": "Intuitive Surgical Inc.", "city": "Sunnyvale", "country": "USA"}]
affiliations: ["Intuitive Surgical Inc."]
cities: ["Sunnyvale"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jamal_Multi-Modal_Contrastive_Masked_Autoencoders_A_Two-Stage_Progressive_Pre-training_Approach_for_CVPR_2025_paper.html

title:
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets

abstract:
In this paper, we propose a new progressive pre-training method for image understanding tasks which leverages RGB-D datasets. The method utilizes Multi-Modal Contrastive Masked Autoencoder and Denoising techniques. Our proposed approach consists of two stages. In the first stage, we pre-train the model using contrastive learning to learn cross-modal representations. In the second stage, we further pre-train the model using masked autoencoding and denoising/noise prediction used in diffusion models. Masked autoencoding focuses on reconstructing the missing patches in the input modality using local spatial correlations, while denoising learns high frequency components of the input data. Moreover, it incorporates global distillation in the second stage by leveraging the knowledge acquired in stage one. Our approach is scalable, robust and suitable for pre-training RGB-D datasets. Extensive experiments on multiple datasets such as ScanNet, NYUv2 and SUN RGB-D show the efficacy and superior performance of our approach. Specifically, we show an improvement of +1.3% mIoU against Mask3D on ScanNet semantic segmentation. We further demonstrate the effectiveness of our approach in low-data regime by evaluating it for semantic segmentation task against the state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jamal_Multi-Modal_Contrastive_Masked_Autoencoders_A_Two-Stage_Progressive_Pre-training_Approach_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jamal_Multi-Modal_Contrastive_Masked_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jamal_2025_CVPR,
    author    = {Jamal, Muhammad Abdullah and Mohareri, Omid},
    title     = {Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17947-17957}
}


title: JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1633-1644
cities: ["Beijing", "Hangzhou", "Hefei", "Xi'an", "Beijing", "Beijing", "Beijing", "Beijing", "Urumqi"]
countries: ["China", "China", "China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_JTD-UAV_MLLM-Enhanced_Joint_Tracking_and_Description_Framework_for_Anti-UAV_Systems_CVPR_2025_paper.html

authors:
Yifan Wang, Jian Zhao, Zhaoxin Fan, Xin Zhang, Xuecheng Wu, Yudian Zhang, Lei Jin, Xinyue Li, Gang Wang, Mengxi Jia, Ping Hu, Zheng Zhu, Xuelong Li

abstract:
Unmanned Aerial Vehicles (UAVs) are widely adopted across various fields, yet they raise significant privacy and safety concerns, demanding robust monitoring solutions. Existing anti-UAV methods primarily focus on position tracking but fail to capture UAV behavior and intent. To address this, we introduce a novel task--UAV Tracking and Intent Understanding (UTIU)--which aims to track UAVs while inferring and describing their motion states and intent for a more comprehensive monitoring approach. To tackle the task, we propose JTD-UAV, the first joint tracking, and intent description framework based on large language models. Our dual-branch architecture integrates UAV tracking with Visual Question Answering (VQA), allowing simultaneous localization and behavior description. To benchmark this task, we introduce the TDUAV dataset, the largest dataset for joint UAV tracking and intent understanding, featuring 1,328 challenging video sequences, over 163K annotated thermal frames, and 3K VQA pairs. Our benchmark demonstrates the effectiveness of JTD-UAV, and both the dataset and code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_JTD-UAV_MLLM-Enhanced_Joint_Tracking_and_Description_Framework_for_Anti-UAV_Systems_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yifan and Zhao, Jian and Fan, Zhaoxin and Zhang, Xin and Wu, Xuecheng and Zhang, Yudian and Jin, Lei and Li, Xinyue and Wang, Gang and Jia, Mengxi and Hu, Ping and Zhu, Zheng and Li, Xuelong},
    title     = {JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1633-1644}
}

raw_affiliation:
[{"affiliation": "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou International Innovation Institute, Beihang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "TeleAI", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Basic Medical Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "GigaAI", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Xinjiang University", "city": "Urumqi", "country": "China"}]

affiliations:
["Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University", "Hangzhou International Innovation Institute, Beihang University", "University of Science and Technology of China", "Xi\u2019an Jiaotong University", "TeleAI", "Beijing Institute of Basic Medical Sciences", "GigaAI", "Beijing University of Posts and Telecommunications", "Xinjiang University"]


title: HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos
authors: Jinglei Zhang, Jiankang Deng, Chao Ma, Rolandos Alexandros Potamias
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1805-1815
affiliations: ["Shanghai Jiao Tong University", "Imperial College London"]
cities: ["Shanghai", "London"]
countries: ["China", "UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_HaWoR_World-Space_Hand_Motion_Reconstruction_from_Egocentric_Videos_CVPR_2025_paper.html

abstract:
Despite the advent in 3D hand pose estimation, current methods predominantly focus on single-image 3D hand reconstruction in the camera frame, overlooking the world-space motion of the hands. Such limitation prohibits their direct use in egocentric video settings, where hands and camera are continuously in motion. In this work, we propose HaWoR, a high-fidelity method for hand motion reconstruction in world coordinates from egocentric videos. We propose to decouple the task by reconstructing the hand motion in the camera space and estimating the camera trajectory in the world coordinate system. To achieve precise camera trajectory estimation, we propose an adaptive egocentric SLAM framework that addresses the shortcomings of traditional SLAM methods, providing robust performance under challenging camera dynamics. To ensure robust hand motion trajectories, even when the hands move out of view frustum, we devise a novel motion infiller network that effectively completes the missing frames of the sequence. Through extensive quantitative and qualitative evaluations, we demonstrate that HaWoR achieves state-of-the-art performance on both hand motion reconstruction and world-frame camera trajectory estimation under different egocentric benchmark datasets. Code and models are available on \href https://hawor-project.github.io/  https://hawor-project.github.io/ .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_HaWoR_World-Space_Hand_Motion_Reconstruction_from_Egocentric_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_HaWoR_World-Space_Hand_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jinglei and Deng, Jiankang and Ma, Chao and Potamias, Rolandos Alexandros},
    title     = {HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1805-1815}
}

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


title: Font-Agent: Enhancing Font Understanding with Large Language Models
authors: Yingxin Lai, Cuijie Xu, Haitian Shi, Guoqing Yang, Xiaoning Li, Zhiming Luo, Shaozi Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19670-19680
cities: ["Xiamen", "Unknown", "Xiamen"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Font-Agent_Enhancing_Font_Understanding_with_Large_Language_Models_CVPR_2025_paper.html

abstract:
The rapid development of generative models has significantly advanced font generation. However, limited exploration has been devoted to the evaluation and interpretability of graphical fonts. Existing quality assessment models can only provide basic visual analyses, such as recognizing clarity and brightness, without in-depth explanations. To address these limitations, we first constructed a large-scale multimodal dataset named the Diversity Font Dataset (DFD), comprising 135,000 font-text pairs. This dataset encompasses a wide range of generated font types and annotations, including language descriptions and quality assessments, thus providing a robust foundation for training and evaluating font analysis models. Based on this dataset, we developed a font agent built upon a Vision-Language Model (VLM) aiming to enhance font quality assessment and offer interpretable question-answering capabilities. Alongside the original visual encoder in VLM, we integrated an Edge-Aware Traces (EAT) module to capture detailed edge information of font strokes and components. Furthermore, we introduced a Dynamic Direct Preference Optimization (D-DPO) strategy to facilitate efficient model fine-tuning. Experimental results demonstrate that Font-Agent achieves state-of-the-art performance on the established dataset. To further evaluate the generalization ability of our algorithm, we conducted additional experiments on several public datasets. The results highlight the notable advantage of Font-Agent in both assessing the quality of generated fonts and comprehending their content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Font-Agent_Enhancing_Font_Understanding_with_Large_Language_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Yingxin and Xu, Cuijie and Shi, Haitian and Yang, Guoqing and Li, Xiaoning and Luo, Zhiming and Li, Shaozi},
    title     = {Font-Agent: Enhancing Font Understanding with Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19670-19680}
}

raw_affiliation:
[{"affiliation": "Department of Artificial Intelligence, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Graph Origin", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}]

affiliations:
["Department of Artificial Intelligence, Xiamen University", "Graph Origin", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University"]


title: Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13445-13454
affiliations: ["WeChat AI, Tencent Inc", "Institute for Artificial Intelligence, Peking University"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_Secret_Lies_in_Color_Enhancing_AI-Generated_Images_Detection_with_Color_CVPR_2025_paper.html

authors:
Zexi Jia, Chuanwei Huang, Yeshuang Zhu, Hongyan Fei, Xiaoyue Duan, Zhiqiang Yuan, Ying Deng, Jiapei Zhang, Jinchao Zhang, Jie Zhou

abstract:
The advancement of Generative Adversarial Networks (GANs) and diffusion models significantly enhances the realism of synthetic images, driving progress in image processing and creative design. However, this progress also necessitates the development of effective detection methods, as synthetic images become increasingly difficult to distinguish from real ones. This difficulty leads to societal issues, such as the spread of misinformation, identity theft, and online fraud. While previous detection methods perform well on public benchmarks, they struggle with our benchmark, FakeART, particularly when dealing with the latest models and cross-domain tasks (e.g., photo-to-painting). To address this challenge, we develop a new synthetic image detection technique based on color distribution. Unlike real images, synthetic images often exhibit uneven color distribution. By employing color quantization and restoration techniques, we analyze the color differences before and after image restoration. We discover and prove that these differences closely relate to the uniformity of color distribution. Based on this finding, we extract effective color features and combine them with image features to create a detection model with only 1.4 million parameters. This model achieves state-of-the-art results across various evaluation benchmarks, including the challenging FakeART dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_Secret_Lies_in_Color_Enhancing_AI-Generated_Images_Detection_with_Color_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_Secret_Lies_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Zexi and Huang, Chuanwei and Zhu, Yeshuang and Fei, Hongyan and Duan, Xiaoyue and Yuan, Zhiqiang and Deng, Ying and Zhang, Jiapei and Zhang, Jinchao and Zhou, Jie},
    title     = {Secret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13445-13454}
}

raw_affiliation:
[{"affiliation": "WeChat AI, Tencent Inc", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]


title: RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22487-22497
raw_affiliation: [{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]
affiliations: ["NVIDIA"]
cities: ["Santa Clara"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heinrich_RADIOv2.5_Improved_Baselines_for_Agglomerative_Vision_Foundation_Models_CVPR_2025_paper.html

authors:
Greg Heinrich, Mike Ranzinger, Hongxu Yin, Yao Lu, Jan Kautz, Andrew Tao, Bryan Catanzaro, Pavlo Molchanov

abstract:
Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DINO, and SAM. This strategy enables the efficient creation of robust models, combining the strengths of individual teachers while significantly reducing computational and resource demands. In this paper, we thoroughly analyze state-of-the-art agglomerative models, identifying critical challenges including resolution mode shifts, teacher imbalance, idiosyncratic teacher artifacts, and an excessive number of output tokens. To address these issues, we propose several novel solutions: multi-resolution training, mosaic augmentation, and improved balancing of teacher loss functions. Specifically, in the context of Vision Language Models, we introduce a token compression technique to maintain high-resolution information within a fixed token count. We release our top-performing variants at multiple scales (-B, -L, -H, and -g), along with inference code and pretrained weights.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heinrich_RADIOv2.5_Improved_Baselines_for_Agglomerative_Vision_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heinrich_RADIOv2.5_Improved_Baselines_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Heinrich_2025_CVPR,
    author    = {Heinrich, Greg and Ranzinger, Mike and Yin, Hongxu and Lu, Yao and Kautz, Jan and Tao, Andrew and Catanzaro, Bryan and Molchanov, Pavlo},
    title     = {RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22487-22497}
}


authors: Cédric Vincent, Taehyoung Kim, Henri Meeß
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1461-1471
affiliations: ["T\u00b4el\u00b4ecom Paris, Institut Polytechnique de Paris", "Fraunhofer IVI"]
cities: ["Paris", "Dresden"]
countries: ["France", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vincent_High_Temporal_Consistency_through_Semantic_Similarity_Propagation_in_Semi-Supervised_Video_CVPR_2025_paper.html

title:
High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous Flight

abstract:
Semantic segmentation from RGB cameras is essential to the perception of autonomous flying vehicles. The stability of predictions through the captured videos is paramount to their reliability and, by extension, to the trustworthiness of the agents. In this paper, we propose a lightweight video semantic segmentation approach--suited to onboard real-time inference--achieving high temporal consistency on aerial data through Semantic Similarity Propagation across frames. SSP temporally propagates the predictions of an efficient image segmentation model with global registration alignment to compensate for camera movements. It combines the current estimation and the prior prediction with linear interpolation using weights computed from the features similarities of the two frames. Because data availability is a challenge in this domain, we propose a consistency-aware Knowledge Distillation training procedure for sparsely labeled datasets with few annotations. Using a large image segmentation model as a teacher to train the efficient SSP, we leverage the strong correlations between labeled and unlabeled frames in the same training videos to obtain high-quality supervision on all frames. KD-SSP obtains a significant temporal consistency increase over the base image segmentation model of 12.5% and 6.7% TC on UAVid and RuralScapes respectively, with higher accuracy and comparable inference speed. On these aerial datasets, KD-SSP provides a superior segmentation quality and inference speed trade-off than other video methods proposed for general applications and shows considerably higher consistency. The code will be made publicly available upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vincent_High_Temporal_Consistency_through_Semantic_Similarity_Propagation_in_Semi-Supervised_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vincent_High_Temporal_Consistency_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Vincent_2025_CVPR,
    author    = {Vincent, C\'edric and Kim, Taehyoung and Mee{\ss}, Henri},
    title     = {High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous Flight},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1461-1471}
}

raw_affiliation:
[{"affiliation": "T\u00b4el\u00b4ecom Paris, Institut Polytechnique de Paris", "city": "Paris", "country": "France"}, {"affiliation": "Fraunhofer IVI", "city": "Dresden", "country": "Germany"}]


title: Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding
authors: Jinlong Li, Cristiano Saltori, Fabio Poiesi, Nicu Sebe
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19390-19400
affiliations: ["University of Trento", "Fondazione Bruno Kessler"]
cities: ["Trento", "Trento"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Cross-Modal_and_Uncertainty-Aware_Agglomeration_for_Open-Vocabulary_3D_Scene_Understanding_CVPR_2025_paper.html

abstract:
The lack of a large-scale 3D-text corpus has led recent works to distill open-vocabulary knowledge from vision-language models (VLMs). However, these methods typically rely on a single VLM to align the feature spaces of 3D models within a common language space, which limits the potential of 3D models to leverage the diverse spatial and semantic capabilities encapsulated in various foundation models. In this paper, we propose Cross-modal and Uncertainty-aware Agglomeration for Open-vocabulary 3D Scene Understanding dubbed CUA-O3D, the first model to integrate multiple foundation models--such as CLIP, DINOv2, and Stable Diffusion--into 3D scene understanding. We further introduce a deterministic uncertainty estimation to adaptively distill and harmonize the heterogeneous 2D feature embeddings from these models. Our method addresses two key challenges: (1) incorporating semantic priors from VLMs alongside the geometric knowledge of spatially-aware vision foundation models, and (2) using a novel deterministic uncertainty estimation to capture model-specific uncertainties across diverse semantic and geometric sensitivities, helping to reconcile heterogeneous representations during training.  Extensive experiments on ScanNetV2 and Matterport3D demonstrate that our method not only advances open-vocabulary segmentation but also achieves robust cross-domain alignment and competitive spatial perception capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Cross-Modal_and_Uncertainty-Aware_Agglomeration_for_Open-Vocabulary_3D_Scene_Understanding_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jinlong and Saltori, Cristiano and Poiesi, Fabio and Sebe, Nicu},
    title     = {Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19390-19400}
}

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


