# papers

authors: Shentong Mo, Yibing Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28912-28921
affiliations: ["CMU", "MBZUAI", "DAMO Academy, Alibaba Group", "Hupan Laboratory"]
cities: ["Pittsburgh", "Abu Dhabi", "Hangzhou", "Hangzhou"]
countries: ["USA", "UAE", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mo_Foley-Flow_Coordinated_Video-to-Audio_Generation_with_Masked_Audio-Visual_Alignment_and_Dynamic_CVPR_2025_paper.html

title:
Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows

abstract:
Coordinated audio generation based on video inputs typically requires a strict audio-visual (AV) alignment, where both semantics and rhythmics of the generated audio segments shall correspond to those in the video frames. Previous studies leverage a two-stage design where the AV encoders are firstly aligned via contrastive learning, then the encoded video representations guide the audio generation process. We observe that both contrastive learning and global video guidance are effective in aligning overall AV semantics while limiting temporally rhythmic synchronization. In this work, we propose Foley-Flow to first align unimodal AV encoders via masked modeling training, where the masked audio segments are recovered under the guidance of the corresponding video segments. After training, the AV encoders which are separately pretrained using only unimodal data are aligned with semantic and rhythmic consistency. Then, we develop a dynamic conditional flow for the final audio generation. Built upon the efficient velocity flow generation framework, our dynamic conditional flow utilizes temporally varying video features as the dynamic condition to guide corresponding audio segment generations. To this end, we extract coherent semantic and rhythmic representations during masked AV alignment, and use this representation of video segments to guide audio generation temporally. Our audio results are evaluated on the standard benchmarks and largely surpass existing results under several metrics. The superior performance indicates that Foley-Flow is effective in generating coordinated audios that are both semantically and rhythmically coherent to various video sequences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mo_Foley-Flow_Coordinated_Video-to-Audio_Generation_with_Masked_Audio-Visual_Alignment_and_Dynamic_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Mo_2025_CVPR,
    author    = {Mo, Shentong and Song, Yibing},
    title     = {Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28912-28921}
}

raw_affiliation:
[{"affiliation": "CMU", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hupan Laboratory", "city": "Hangzhou", "country": "China"}]


title: LION-FS: Fast & Slow Video-Language Thinker as Online Video Assistant
authors: Wei Li, Bing Hu, Rui Shao, Leyang Shen, Liqiang Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3240-3251
affiliations: ["Harbin Institute of Technology, Shenzhen"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LION-FS_Fast__Slow_Video-Language_Thinker_as_Online_Video_Assistant_CVPR_2025_paper.html

abstract:
First-person video assistants are highly anticipated to enhance our daily life through online video dialogue. However, existing online video assistants often sacrifice assistant efficacy for real-time efficiency by processing low-frame-rate videos with coarse-grained visual features. To overcome the trade-off between efficacy and efficiency, we propose "**F**ast & **S**low Video-Language Thinker" as on**LI**ne vide**O** assista**N**t, **LION-FS**, achieving real-time, proactive, temporally accurate, and contextually precise responses. LION-FS adopts a two-stage optimization strategy: **1) Fast Path: Routing-Based Response Determination** evaluates frame-by-frame whether a immediate response is necessary. To enhance responses determination accuracy and handle higher frame-rate inputs efficiently, we employ Token Aggregation Routing to dynamically fuse spatiotemporal features without increasing token numbers, while utilizing Token Dropping Routing to eliminate redundant features, and **2) Slow Path: Multi-granularity Keyframe Augmentation** optimizes keyframes during response generation. To provide comprehensive and detailed responses beyond atomic actions constrained by training data, fine-grained spatial features and human-environment interaction features are extracted through multi-granular pooling. They are further integrated into a meticulously designed multimodal Thinking Template to guide more precise response generation. Comprehensive evaluations on online video tasks demonstrate that LION-FS achieves state-of-the-art efficacy and efficiency. The codes will be released soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LION-FS_Fast__Slow_Video-Language_Thinker_as_Online_Video_Assistant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LION-FS_Fast__CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wei and Hu, Bing and Shao, Rui and Shen, Leyang and Nie, Liqiang},
    title     = {LION-FS: Fast \& Slow Video-Language Thinker as Online Video Assistant},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3240-3251}
}

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


title: CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
authors: Yuan Zhou, Qingshan Xu, Jiequan Cui, Junbao Zhou, Jing Zhang, Richang Hong, Hanwang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20135-20145
affiliations: ["Nanyang Technological University", "Beihang University", "Hefei University of Technology"]
cities: ["Singapore", "Beijing", "Hefei"]
countries: ["Singapore", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_CARE_Transformer_Mobile-Friendly_Linear_Visual_Transformer_via_Decoupled_Dual_Interaction_CVPR_2025_paper.html

abstract:
Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed in resource-constrained mobile devices, due to suffering from either few efficiency gains or significant accuracy drops. In this paper, we propose a new deCoupled duAl-interactive lineaR attEntion (CARE) mechanism, revealing that features' decoupling and interaction can fully unleash the power of linear attention. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies, thereby preserving sufficient local and global information while effectively enhancing the efficiency of models. Then, a dynamic memory unit is employed to maintain critical information along the network pipeline. Moreover, we design a dual interaction module to effectively facilitate interaction between local inductive bias and long-range information as well as among features at different layers. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy. Extensive experiments on ImageNet-1K, COCO, and ADE20K datasets demonstrate the effectiveness of our approach, e.g., achieving 78.4/82.1% top-1 accuracy on ImagegNet-1K at the cost of only 0.7/1.9 GMACs. Codes will be released on github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_CARE_Transformer_Mobile-Friendly_Linear_Visual_Transformer_via_Decoupled_Dual_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_CARE_Transformer_Mobile-Friendly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yuan and Xu, Qingshan and Cui, Jiequan and Zhou, Junbao and Zhang, Jing and Hong, Richang and Zhang, Hanwang},
    title     = {CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20135-20145}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Hefei University of Technology", "city": "Hefei", "country": "China"}]


title: Paint by Inpaint: Learning to Add Image Objects by Removing Them First
authors: Navve Wasserman, Noam Rotstein, Roy Ganz, Ron Kimmel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18313-18324
affiliations: ["Weizmann Institute of Science", "Technion - Israel Institute of Technology"]
cities: ["Rehovot", "Haifa"]
countries: ["Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wasserman_Paint_by_Inpaint_Learning_to_Add_Image_Objects_by_Removing_CVPR_2025_paper.html

abstract:
Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without requiring user-provided input masks remains a challenge. We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to inpainting models that benefit from segmentation mask guidance. Capitalizing on this realization, by implementing an automated and extensive pipeline, we curate a filtered large-scale image dataset containing pairs of images and their corresponding object-removed versions. Using these pairs, we train a diffusion model to inverse the inpainting process, effectively adding objects into images. Unlike other editing datasets, ours features natural target images instead of synthetic ones while ensuring source-target consistency by construction. Additionally, we utilize a large Vision-Language Model to provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions. Our quantitative and qualitative results show that the trained model surpasses existing models in both object addition and general editing tasks. Visit our project page for the released dataset and trained models: https://rotsteinnoam.github.io/Paint-by-Inpaint/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wasserman_Paint_by_Inpaint_Learning_to_Add_Image_Objects_by_Removing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wasserman_Paint_by_Inpaint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wasserman_2025_CVPR,
    author    = {Wasserman, Navve and Rotstein, Noam and Ganz, Roy and Kimmel, Ron},
    title     = {Paint by Inpaint: Learning to Add Image Objects by Removing Them First},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18313-18324}
}

raw_affiliation:
[{"affiliation": "Weizmann Institute of Science", "city": "Rehovot", "country": "Israel"}, {"affiliation": "Technion - Israel Institute of Technology", "city": "Haifa", "country": "Israel"}]


title: Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8625-8636
cities: ["Orlando", "Perth", "Chapel Hill", "Seattle", "Dallas"]
countries: ["USA", "Australia", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Motion-Grounded_Video_Reasoning_Understanding_and_Perceiving_Motion_at_Pixel_Level_CVPR_2025_paper.html

authors:
Andong Deng, Tongjia Chen, Shoubin Yu, Taojiannan Yang, Lincoln Spencer, Yapeng Tian, Ajmal Saeed Mian, Mohit Bansal, Chen Chen

abstract:
In this paper, we introduce Motion-Grounded Video Reasoning, a new motionunderstanding task that requires generating visual answers (video segmentationmasks) according to the input question, and hence needs implicit spatiotemporalreasoning and grounding. This task extends existing spatiotemporal groundingwork focusing on explicit action/motion grounding, to a more general format byenabling implicit reasoning via questions. To facilitate the development of the newtask, we collect a large-scale dataset called GROUNDMORE, which comprises1,715 video clips, 249K object masks that are deliberately designed with 4 questiontypes (Causal, Sequential, Counterfactual, and Descriptive) for benchmarkingdeep and comprehensive motion reasoning abilities. GROUNDMORE uniquelyrequires models to generate visual answers, providing a more concrete and visuallyinterpretable response than plain texts. It evaluates models on both spatiotemporalgrounding and reasoning, fostering to address complex challenges in motion-relatedvideo reasoning, temporal perception, and pixel-level understanding. Furthermore,we introduce a novel baseline model named Motion-Grounded Video ReasoningAssistant (MORA). MORA incorporates the multimodal reasoning ability from theMultimodal LLM, the pixel-level perception capability from the grounding model(SAM), and the temporal perception ability from a lightweight localization head.MORA achieves respectable performance on GROUNDMORE outperforming thebest existing visual grounding baseline model by an average of 21.5% relatively.We hope this novel and challenging task will pave the way for future advancementsin robust and general motion understanding via video reasoning segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Motion-Grounded_Video_Reasoning_Understanding_and_Perceiving_Motion_at_Pixel_Level_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Motion-Grounded_Video_Reasoning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Andong and Chen, Tongjia and Yu, Shoubin and Yang, Taojiannan and Spencer, Lincoln and Tian, Yapeng and Mian, Ajmal Saeed and Bansal, Mohit and Chen, Chen},
    title     = {Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8625-8636}
}

raw_affiliation:
[{"affiliation": "CRCV , University of Central Florida", "city": "Orlando", "country": "USA"}, {"affiliation": "University of Western Australia", "city": "Perth", "country": "Australia"}, {"affiliation": "UNC, Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Amazon Web Services", "city": "Seattle", "country": "USA"}, {"affiliation": "The University of Texas at Dallas", "city": "Dallas", "country": "USA"}]

affiliations:
["CRCV , University of Central Florida", "University of Western Australia", "UNC, Chapel Hill", "Amazon Web Services", "The University of Texas at Dallas"]


title: PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16976-16986
cities: ["Beijing", "Shenzhen", "Shenzhen", "Shenzhen", "Menlo Park"]
countries: ["China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zha_PMA_Towards_Parameter-Efficient_Point_Cloud_Understanding_via_Point_Mamba_Adapter_CVPR_2025_paper.html

authors:
Yaohua Zha, Yanzi Wang, Hang Guo, Jinpeng Wang, Tao Dai, Bin Chen, Zhihao Ouyang, Xue Yuerong, Ke Chen, Shu-Tao Xia

abstract:
Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightforward, utilizing only the final output of the pre-trained model for various task heads. It neglects the rich complementary information in the intermediate layer, thereby failing to fully unlock the potential of pre-trained models. To overcome this limitation, we propose an orthogonal solution: Point Mamba Adapter (PMA), which constructs an ordered feature sequence from all layers of the pre-trained model and leverages Mamba to fuse all complementary semantics, thereby promoting comprehensive point cloud understanding. Constructing this ordered sequence is non-trivial due to the inherent isotropy of 3D space. Therefore, we further propose a geometry-constrained gate prompt generator (G2PG) shared across different layers, which applies shared geometric constraints to the output gates of the Mamba and dynamically optimizes the spatial order, thus enabling more effective integration of multi-layer information. Extensive experiments conducted on challenging point cloud datasets across various tasks demonstrate that our PMA elevates the capability for point cloud understanding to a new level by fusing diverse complementary intermediate features. Code is available at https://github.com/zyh16143998882/PMA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zha_PMA_Towards_Parameter-Efficient_Point_Cloud_Understanding_via_Point_Mamba_Adapter_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Zha_2025_CVPR,
    author    = {Zha, Yaohua and Wang, Yanzi and Guo, Hang and Wang, Jinpeng and Dai, Tao and Chen, Bin and Ouyang, Zhihao and Yuerong, Xue and Chen, Ke and Xia, Shu-Tao},
    title     = {PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16976-16986}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]

affiliations:
["Tsinghua University", "Pengcheng Laboratory", "Shenzhen University", "Harbin Institute of Technology, Shenzhen", "Meta"]


title: All-directional Disparity Estimation for Real-world QPD Images
authors: Hongtao Yu, Shaohui Song, Lihu Sun, Wenkai Su, Xiaodong Yang, Chengming Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21836-21846
affiliations: ["OMNIVISION", "OMNIVISION"]
cities: ["Wuhan", "Santa Clara"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_All-directional_Disparity_Estimation_for_Real-world_QPD_Images_CVPR_2025_paper.html

abstract:
Quad Photodiode (QPD) sensors represent an evolution by providing four sub-views, whereas dual-pixel (DP) sensors are limited to two sub-views. In addition to enhancing auto-focus performance, QPD sensors also enable disparity estimation in horizontal and vertical directions. However, the characteristics of QPD sensors, including uneven illumination across sub-views and the narrow baseline, render algorithm design difficult. Furthermore, effectively utilizing the two-directional disparity of QPD sensors remains a challenge. The scarcity of QPD disparity datasets also limits the development of learning-based methods. In this work, we address these challenges by first proposing a DPNet for DP disparity estimation. Specifically, we design an illumination-invariant module to reduce the impact of illumination, followed by a coarse-to-fine module to estimate sub-pixel disparity. Building upon the DPNet, we further propose a QuadNet, which integrates the two-directional disparity via an edge-aware fusion module. To facilitate the evaluation of our approaches, we propose the first QPD disparity dataset QPD2K, comprising 2,100 real-world QPD images and corresponding disparity maps. Experiments demonstrate that our approaches achieve state-of-the-art performance in DP and QPD disparity estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_All-directional_Disparity_Estimation_for_Real-world_QPD_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_All-directional_Disparity_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Hongtao and Song, Shaohui and Sun, Lihu and Su, Wenkai and Yang, Xiaodong and Liu, Chengming},
    title     = {All-directional Disparity Estimation for Real-world QPD Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21836-21846}
}

raw_affiliation:
[{"affiliation": "OMNIVISION", "city": "Wuhan", "country": "China"}, {"affiliation": "OMNIVISION", "city": "Santa Clara", "country": "USA"}]


title: LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation
authors: Min Wu Jeong, Chae Eun Rhee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17671-17681
raw_affiliation: [{"affiliation": "Hanyang University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Hanyang University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_LC-Mamba_Local_and_Continuous_Mamba_with_Shifted_Windows_for_Frame_CVPR_2025_paper.html

abstract:
In this paper, we propose LC-Mamba, a Mamba-based model that captures fine-grained spatiotemporal information in video frames, addressing limitations in current interpolation methods and enhancing performance. The main contributions are as follows: First, we apply a shifted local window technique to reduce historical decay and enhance local spatial features, allowing multiscale capture of detailed motion between frames. Second, we introduce a Hilbert curve-based selective state scan to maintain continuity across window boundaries, preserving spatial correlations both within and between windows. Third, we extend the Hilbert curve to enable voxel-level scanning to effectively capture spatiotemporal characteristics between frames. The proposed LC-Mamba achieves competitive results, with a PSNR of 36.53 dB on Vimeo-90k, outperforming prior models by +0.03 dB. The code and models are publicly available at https://github.com/Miinuuu/LC-Mamba.git

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_LC-Mamba_Local_and_Continuous_Mamba_with_Shifted_Windows_for_Frame_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_LC-Mamba_Local_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Min Wu and Rhee, Chae Eun},
    title     = {LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17671-17681}
}


title: Zero-Shot Head Swapping in Real-World Scenarios
authors: Taewoong Kang, Sohyun Jeong, Hyojin Jang, Jaegul Choo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10805-10814
affiliations: ["KAIST", "FLIPTION"]
cities: ["Daejeon", "Unknown"]
countries: ["South Korea", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_Zero-Shot_Head_Swapping_in_Real-World_Scenarios_CVPR_2025_paper.html

abstract:
With growing demand in media and social networks for personalized images, the need for advanced head-swapping techniques--integrating an entire head from the head image with the body from the body image--has increased. However, traditional head-swapping methods heavily rely on face-centered cropped data with primarily frontal-facing views, which limits their effectiveness in real-world applications. Additionally, their masking methods, designed to indicate regions requiring editing, are optimized for these types of dataset but struggle to achieve seamless blending in complex situations, such as when the original data includes features like long hair extending beyond the masked area. To overcome these limitations and enhance adaptability in diverse and complex scenarios, we propose a novel head swapping method, HID, that is robust to images including the full head and the upper body, and handles from frontal to side views, while automatically generating context-aware masks. For automatic mask generation, we introduce the IOMask, which enables seamless blending of the head and body, effectively addressing integration challenges. We further introduce the hair injection module to capture hair details with greater precision. Our experiments demonstrate that the proposed approach achieves state-of-the-art performance in head swapping, providing visually consistent and realistic results across a wide range of challenging conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_Zero-Shot_Head_Swapping_in_Real-World_Scenarios_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_Zero-Shot_Head_Swapping_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kang_2025_CVPR,
    author    = {Kang, Taewoong and Jeong, Sohyun and Jang, Hyojin and Choo, Jaegul},
    title     = {Zero-Shot Head Swapping in Real-World Scenarios},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10805-10814}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "FLIPTION", "city": "Unknown", "country": "Unknown"}]


title: Toward Robust Neural Reconstruction from Sparse Point Sets
authors: Amine Ouasfi, Shubhendu Jena, Eric Marchand, Adnane Boukhayma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6552-6562
raw_affiliation: [{"affiliation": "Inria, Univ. Rennes, CNRS, IRISA", "city": "Rennes", "country": "France"}]
affiliations: ["Inria, Univ. Rennes, CNRS, IRISA"]
cities: ["Rennes"]
countries: ["France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ouasfi_Toward_Robust_Neural_Reconstruction_from_Sparse_Point_Sets_CVPR_2025_paper.html

abstract:
We consider the challenging problem of  learning Signed Distance Functions (SDF) from sparse and noisy 3D point clouds. In contrast to recent methods that depend on smoothness priors, our method, rooted in a distributionally robust optimization (DRO) framework, incorporates a regularization term that leverages samples from the uncertainty regions of the model to improve the learned SDFs. Thanks to tractable dual formulations, we show that this framework enables a stable and efficient optimization of SDFs in the absence of ground truth supervision. Using a variety of synthetic and real data evaluations from different modalities, we show  that of our DRO based learning framework can improve  SDF learning with respect to baselines and the state-of-the-art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ouasfi_Toward_Robust_Neural_Reconstruction_from_Sparse_Point_Sets_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ouasfi_Toward_Robust_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ouasfi_2025_CVPR,
    author    = {Ouasfi, Amine and Jena, Shubhendu and Marchand, Eric and Boukhayma, Adnane},
    title     = {Toward Robust Neural Reconstruction from Sparse Point Sets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6552-6562}
}


title: GPAvatar: High-fidelity Head Avatars by Learning Efficient Gaussian Projections
authors: Wei-Qi Feng, Dong Han, Ze-Kang Zhou, Shunkai Li, Xiaoqiang Liu, Pengfei Wan, Di Zhang, Miao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 250-259
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_GPAvatar_High-fidelity_Head_Avatars_by_Learning_Efficient_Gaussian_Projections_CVPR_2025_paper.html

abstract:
Existing radiance field-based head avatar methods have mostly relied on pre-computed explicit priors (e.g., mesh, point) or neural implicit representations, making it challenging to achieve high fidelity with both computational efficiency and low memory consumption. To overcome this, we present GPAvatar, a novel and efficient Gaussian splatting-based method for reconstructing high-fidelity dynamic 3D head avatars from monocular videos. We extend Gaussians in 3D space to a high-dimensional embedding space encompassing Gaussian's spatial position and avatar expression, enabling the representation of the head avatar with arbitrary pose and expression. To enable splatting-based rasterization, a linear transformation is learned to project each high-dimensional Gaussian back to the 3D space, which is sufficient to capture expression variations instead of using complex neural networks. Furthermore, we propose an adaptive densification strategy that dynamically allocates Gaussians to regions with high expression variance, improving the  facial detail representation. Experimental results on three datasets show that our method outperforms existing state-of-the-art methods in rendering quality and speed while reducing memory usage in training and rendering.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_GPAvatar_High-fidelity_Head_Avatars_by_Learning_Efficient_Gaussian_Projections_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_GPAvatar_High-fidelity_Head_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Wei-Qi and Han, Dong and Zhou, Ze-Kang and Li, Shunkai and Liu, Xiaoqiang and Wan, Pengfei and Zhang, Di and Wang, Miao},
    title     = {GPAvatar: High-fidelity Head Avatars by Learning Efficient Gaussian Projections},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {250-259}
}

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

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University", "Zhongguancun Laboratory", "Kuaishou Technology"]


title: PIAD: Pose and Illumination agnostic Anomaly Detection
authors: Kaichen Yang, Junjie Cao, Zeyu Bai, Zhixun Su, Andrea Tagliasacchi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4734-4743
cities: ["Dalian", "Dalian", "Burnaby"]
countries: ["China", "China", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.html

abstract:
We introduce the Pose and Illumination agnostic Anomaly Detection (PIAD) problem, a generalization of pose-agnostic anomaly detection (PAD). Being illumination agnostic is critical, as it relaxes the assumption that training data for an object has to be acquired in the same light configuration of the query images that we want to test. Moreover, even if the object is placed within the same capture environment, being illumination agnostic implies that we can relax the assumption that the relative pose between environment light and query object has to match the one in the training data. We introduce a new dataset to study this problem, containing both synthetic and real-world examples, propose a new baseline for PIAD, and demonstrate how our baseline provides state-of-the-art results in both PAD and PIAD, not only in the new proposed dataset, but also in existing datasets that were designed for the simpler PAD problem. Project page: https://kaichen-yang.github.io/piad/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_PIAD_Pose_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Kaichen and Cao, Junjie and Bai, Zeyu and Su, Zhixun and Tagliasacchi, Andrea},
    title     = {PIAD: Pose and Illumination agnostic Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4734-4743}
}

raw_affiliation:
[{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province", "city": "Dalian", "country": "China"}, {"affiliation": "Simon Fraser University", "city": "Burnaby", "country": "Canada"}]

affiliations:
["Dalian University of Technology", "Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province", "Simon Fraser University"]


title: CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18794-18803
cities: ["Frankfurt", "Cambridge", "Yorktown Heights", "Cambridge", "Tuebingen"]
countries: ["Germany", "USA", "USA", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Araujo_CAV-MAE_Sync_Improving_Contrastive_Audio-Visual_Mask_Autoencoders_via_Fine-Grained_Alignment_CVPR_2025_paper.html

authors:
Edson Araujo, Andrew Rouditchenko, Yuan Gong, Saurabhchand Bhati, Samuel Thomas, Brian Kingsbury, Leonid Karlinsky, Rogerio Feris, James R. Glass, Hilde Kuehne

abstract:
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However,  most approaches rely on global audio representations that fail to capture fine-grained temporal correspondences with visual frames.Additionally, existing methods often struggle with conflicting optimization objectives when trying to jointly learn reconstruction and cross-modal alignment. In this work, we propose CAV-MAE Sync as a simple yet effective extension of the original CAV-MAE framework for self-supervised audio-visual learning. We address three key challenges: First, we tackle the granularity mismatch between modalities by treating audio as a temporal sequence aligned with video frames, rather than using global representations. Second, we resolve conflicting optimization goals by separating contrastive and reconstruction objectives through dedicated global tokens. Third, we improve spatial localization by introducing learnable register tokens that reduce semantic load on patch tokens. We evaluate the proposed approach on AudioSet, VGG Sound, and the ADE20K Sound dataset on zero-shot retrieval, classification and localization tasks demonstrating state-of-the-art performance and outperforming more complex architectures. Code available at https://github.com/edsonroteia/cav-mae-sync.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Araujo_CAV-MAE_Sync_Improving_Contrastive_Audio-Visual_Mask_Autoencoders_via_Fine-Grained_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Araujo_CAV-MAE_Sync_Improving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Araujo_2025_CVPR,
    author    = {Araujo, Edson and Rouditchenko, Andrew and Gong, Yuan and Bhati, Saurabhchand and Thomas, Samuel and Kingsbury, Brian and Karlinsky, Leonid and Feris, Rogerio and Glass, James R. and Kuehne, Hilde},
    title     = {CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18794-18803}
}

raw_affiliation:
[{"affiliation": "Goethe University of Frankfurt", "city": "Frankfurt", "country": "Germany"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "IBM Research", "city": "Yorktown Heights", "country": "USA"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}, {"affiliation": "Tuebingen AI Center/University of Tuebingen", "city": "Tuebingen", "country": "Germany"}]

affiliations:
["Goethe University of Frankfurt", "MIT", "IBM Research", "MIT-IBM Watson AI Lab", "Tuebingen AI Center/University of Tuebingen"]


title: Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
authors: Yoojin Jung, Byung Cheol Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9696-9706
affiliations: ["Department of Electrical and Computer Engineering, Inha University"]
cities: ["Incheon"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_Two_is_Better_than_One__Efficient_Ensemble_Defense_for_CVPR_2025_paper.html

abstract:
Deep learning-based computer vision systems adopt complex and large architectures to improve performance, yet they face challenges in deployment on resource-constrained mobile and edge devices. To address this issue, model compression techniques such as pruning, quantization, and matrix factorization have been proposed; however, these compressed models are often highly vulnerable to adversarial attacks. We introduce the Efficient Ensemble Defense (EED) technique, which diversifies the compression of a single base model based on different pruning importance scores and enhances ensemble diversity to achieve high adversarial robustness and resource efficiency. EED dynamically determines the number of necessary sub-models during the inference stage, minimizing unnecessary computations while maintaining high robustness. On the CIFAR-10 and SVHN datasets, EED demonstrated state-of-the-art robustness performance compared to existing adversarial pruning techniques, along with an inference speed improvement of up to 1.86 times. This proves that EED is a powerful defense solution in resource-constrained environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_Two_is_Better_than_One__Efficient_Ensemble_Defense_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_Two_is_Better_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Yoojin and Song, Byung Cheol},
    title     = {Two is Better than One:  Efficient Ensemble Defense for Robust and Compact Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9696-9706}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Inha University", "city": "Incheon", "country": "Republic of Korea"}]


html_link: https://openaccess.thecvf.com/content/CVPR2025/html/Madar_Tiled_Diffusion_CVPR_2025_paper.html
title: Tiled Diffusion
authors: Or Madar, Ohad Fried
pdf_link: https://openaccess.thecvf.com/content/CVPR2025/papers/Madar_Tiled_Diffusion_CVPR_2025_paper.pdf
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7795-7804
affiliations: ["Reichman University", "Reichman University"]
cities: ["Herzliya", "Herzliya"]
countries: ["Israel", "Israel"]
abstract:
Image tiling--the seamless connection of disparate images to create a coherent visual field--is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains.This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images.Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360deg synthesis.

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Madar_Tiled_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Madar_2025_CVPR,
    author    = {Madar, Or and Fried, Ohad},
    title     = {Tiled Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7795-7804}
}

raw_affiliation:
[{"affiliation": "Reichman University", "city": "Herzliya", "country": "Israel"}, {"affiliation": "Reichman University", "city": "Herzliya", "country": "Israel"}]


title: Using Diffusion Priors for Video Amodal Segmentation
authors: Kaihua Chen, Deva Ramanan, Tarasha Khurana
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22890-22900
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Using_Diffusion_Priors_for_Video_Amodal_Segmentation_CVPR_2025_paper.html

abstract:
Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; single-image segmentation methods cannot handle high-levels of occlusions which are better inferred using temporal information, and multi-frame methods have focused solely on segmenting rigid objects. To this end, we propose to tackle video amodal segmentation by formulating it as a conditional generation task, thereby capitalizing on the foundational knowledge in video generative models. Our method is simple; we repurpose these models to condition on a sequence of modal mask frames of an object along with contextual depth maps, to learn which object boundary may be occluded and therefore, extended to hallucinate the complete extent of an object. This is followed by a content completion stage which is able to inpaint the occluded regions of an object. We benchmark our approach alongside a wide array of state-of-the-art methods on four datasets and show a dramatic improvement of upto 13% for amodal segmentation in an object's occluded region.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Using_Diffusion_Priors_for_Video_Amodal_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Using_Diffusion_Priors_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Kaihua and Ramanan, Deva and Khurana, Tarasha},
    title     = {Using Diffusion Priors for Video Amodal Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22890-22900}
}


title: COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation
authors: Arnav M. Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma, Jeff Bilmes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20534-20546
raw_affiliation: [{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}]
affiliations: ["University of Washington"]
cities: ["Seattle"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Das_COBRA_COmBinatorial_Retrieval_Augmentation_for_Few-Shot_Adaptation_CVPR_2025_paper.html

abstract:
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task. However, these approaches are prone to selecting highly redundant samples, since they fail to incorporate any notion of diversity. In our work, we first demonstrate that data selection strategies used in prior retrieval-augmented few-shot adaptation settings can be generalized using a class of functions known as Combinatorial Mutual Information (CMI) measures. We then propose COBRA (COmBinatorial Retrieval Augmentation), which employs an alternative CMI measure that considers both diversity and similarity to a target dataset. COBRA consistently outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques when used to retrieve samples from LAION-2B. COBRA introduces negligible computational overhead to the cost of retrieval while providing significant gains in downstream model performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Das_COBRA_COmBinatorial_Retrieval_Augmentation_for_Few-Shot_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Das_COBRA_COmBinatorial_Retrieval_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Das_2025_CVPR,
    author    = {Das, Arnav M. and Bhatt, Gantavya and Kumari, Lilly and Verma, Sahil and Bilmes, Jeff},
    title     = {COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20534-20546}
}


title: Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera
authors: Zhengdi Yu, Stefanos Zafeiriou, Tolga Birdal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27716-27726
raw_affiliation: [{"affiliation": "Imperial College London", "city": "London", "country": "UK"}]
affiliations: ["Imperial College London"]
cities: ["London"]
countries: ["UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.html

abstract:
We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). However, existing methods for monocular hand reconstruction typically rely on a weak perspective camera model, which simulates hand motion within a limited camera frustum. As a result, these approaches struggle to recover the full 3D global trajectory and often produce noisy or incorrect depth estimations, particularly when the video is captured by dynamic or moving cameras, which is common in egocentric scenarios. Our \name consists of a multi-stage, multi-objective optimization pipeline, that factors in (i) simultaneous localization and mapping (SLAM) to robustly estimate relative camera motion, (ii) an interacting-hand prior for generative infilling and to refine the interaction dynamics, ensuring plausible recovery under (self-)occlusions, and (iii) hierarchical initialization through a combination of state-of-the-art hand tracking methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Dyn-HaMR_Recovering_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Zhengdi and Zafeiriou, Stefanos and Birdal, Tolga},
    title     = {Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27716-27726}
}


title: The Scene Language: Representing Scenes with Programs, Words, and Embeddings
authors: Yunzhi Zhang, Zizhang Li, Matt Zhou, Shangzhe Wu, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24625-24634
affiliations: ["Stanford University", "UC Berkeley"]
cities: ["Stanford", "Berkeley"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_The_Scene_Language_Representing_Scenes_with_Programs_Words_and_Embeddings_CVPR_2025_paper.html

abstract:
We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene with three key components: a program that specifies the hierarchical and relational structure of entities in the scene, words in natural language that summarize the semantic class of each entity, and embeddings that capture the visual identity of each entity. This representation can be inferred from pre-trained language models via a training-free inference technique, given text or image inputs. The resulting scene can be rendered into images using traditional, neural, or hybrid graphics renderers. Together, this forms an automated system for high-quality 3D and 4D scene generation. Compared with existing representations like scene graphs, our proposed Scene Language generates complex scenes with higher fidelity, while explicitly modeling the scene structures to enable precise control and editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_The_Scene_Language_Representing_Scenes_with_Programs_Words_and_Embeddings_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_The_Scene_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yunzhi and Li, Zizhang and Zhou, Matt and Wu, Shangzhe and Wu, Jiajun},
    title     = {The Scene Language: Representing Scenes with Programs, Words, and Embeddings},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24625-24634}
}

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


title: ProbeSDF: Light Field Probes For Neural Surface Reconstruction
authors: Briac Toussaint, Diego Thomas, Jean-Sébastien Franco
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11026-11035
affiliations: ["Univ . Grenoble Alpes, CNRS, Inria, Grenoble INP , LJK, France", "Kyushu University , Japan"]
cities: ["Grenoble", "Fukuoka"]
countries: ["France", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Toussaint_ProbeSDF_Light_Field_Probes_For_Neural_Surface_Reconstruction_CVPR_2025_paper.html

abstract:
SDF-based differential rendering frameworks have achieved state-of-the-art multiview 3D shape reconstruction. In this work, we re-examine this family of approaches by minimally reformulating its core appearance model in a way that simultaneously yields faster computation and increased performance. To this goal, we exhibit a physically-inspired minimal radiance parametrization decoupling angular and spatial contributions, by encoding them with a small number of features stored in two respective volumetric grids of different resolutions. Requiring as little as four parameters per voxel, and a tiny MLP call inside a single fully fused kernel, our approach allows to enhance performance with both surface and image (PSNR) metrics, while providing a significant training speedup and real-time rendering. We show this performance to be consistently achieved on real data over two widely different and popular application fields, generic object and human subject shape reconstruction, using four representative and challenging datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Toussaint_ProbeSDF_Light_Field_Probes_For_Neural_Surface_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Toussaint_ProbeSDF_Light_Field_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Toussaint_2025_CVPR,
    author    = {Toussaint, Briac and Thomas, Diego and Franco, Jean-S\'ebastien},
    title     = {ProbeSDF: Light Field Probes For Neural Surface Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11026-11035}
}

raw_affiliation:
[{"affiliation": "Univ . Grenoble Alpes, CNRS, Inria, Grenoble INP , LJK, France", "city": "Grenoble", "country": "France"}, {"affiliation": "Kyushu University , Japan", "city": "Fukuoka", "country": "Japan"}]


title: Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays
authors: Laurie Bose, Jianing Chen, Piotr Dudek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5392-5400
affiliations: ["The University of Manchester, Department of EEE", "Visionchip Limited"]
cities: ["Manchester", "Manchester"]
countries: ["United Kingdom", "United Kingdom"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.html

abstract:
This paper presents a novel approach for joint point-feature detection and tracking, designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels, PPA sensors consist of thousands of "pixel-processors", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel, meaning no raw image data need ever leave the sensor for external processing. We introduce a Descriptor-In-Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA's architecture enables the response of every processor's descriptor, upon the current image, to be computed in parallel. This produces a "descriptor response map", which, by generating the correct layout of descriptors across the pixel-processors, can be used for both point-feature detection and tracking. This reduces sensor output to just sparse feature locations and descriptors, read-out via an address-event interface, giving a greater than 1000X reduction in data transfer compared to raw image output. The sparse readout and complete utilization of all pixel-processors makes our approach very efficient. Our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably under violent motion. This is the first work performing point-feature detection and tracking entirely in-pixel.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bose_Descriptor-In-Pixel__Point-Feature_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bose_2025_CVPR,
    author    = {Bose, Laurie and Chen, Jianing and Dudek, Piotr},
    title     = {Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5392-5400}
}

raw_affiliation:
[{"affiliation": "The University of Manchester, Department of EEE", "city": "Manchester", "country": "United Kingdom"}, {"affiliation": "Visionchip Limited", "city": "Manchester", "country": "United Kingdom"}]


title: Hybrid Concept Bottleneck Models
authors: Yang Liu, Tianwei Zhang, Shi Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20179-20189
cities: ["Chengdu", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.html

abstract:
Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts. However, the application of CBMs is often constrained by insufficient concept annotations. Recently, multi-modal pre-trained models have shown promise in reducing annotation costs by aligning visual representations with textual concept embeddings. Nevertheless, the quality and completeness of the predefined concepts significantly affect the performance of CBMs.In this work, we propose Hybrid Concept Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts. Our method consists of two main components: a Static Concept Bank and a Dynamic Concept Bank. The Static Concept Bank directly leverages large language models (LLMs) for concept construction, while the Dynamic Concept Bank employs learnable vectors to capture complementary and valuable concepts continuously during training. After training, a pre-trained translator converts these vectors into human-understandable concepts, further enhancing model interpretability. Notably, HybridCBM is highly flexible and can be easily applied to any CBM to improve performance. Experimental results across multiple datasets demonstrate that HybridCBM outperforms current state-of-the-art methods and achieves comparable results to black-box models. Additionally, we propose novel metrics to evaluate the quality of the learned concepts, showing that they perform comparably to predefined concepts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hybrid_Concept_Bottleneck_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yang and Zhang, Tianwei and Gu, Shi},
    title     = {Hybrid Concept Bottleneck Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20179-20189}
}

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

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


title: UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5927-5937
affiliations: ["Brown University", "Meta Reality Labs"]
cities: ["Providence", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html

authors:
Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Kefan Chen, Srinath Sridhar, Aayush Prakash

abstract:
3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation. We further find that these heterogeneous features can be compressed into a lower-dimensional (e.g., 3-channel) shared feature space using a carefully designed multi-branch network.The compressed UVGS can be treated as typical RGB images. Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians, making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. In our experiments, we demonstrate various unconditional, conditional generation, and inpainting applications of 3DGS based on diffusion models, which were previously non-trivial.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rai_UVGS_Reimagining_Unstructured_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rai_2025_CVPR,
    author    = {Rai, Aashish and Wang, Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush},
    title     = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5927-5937}
}

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


title: Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning
authors: Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20547-20557
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Dual_Consolidation_for_Pre-Trained_Model-Based_Domain-Incremental_Learning_CVPR_2025_paper.html

abstract:
Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. While recent advances in pre-trained models provide a solid foundation for DIL, learning new concepts often results in the catastrophic forgetting of pre-trained knowledge. Specifically, sequential model updates can overwrite both the representation and the classifier with knowledge from the latest domain. Thus, it is crucial to develop a representation and corresponding classifier that accommodate all seen domains throughout the learning process. To this end, we propose DUal ConsolidaTion (Duct) to unify and consolidate historical knowledge at both the representation and classifier levels. By merging the backbone of different stages, we create a representation space suitable for multiple domains incrementally. The merged representation serves as a balanced intermediary that captures task-specific features from all seen domains. Additionally, to address the mismatch between consolidated embeddings and the classifier, we introduce an extra classifier consolidation process. Leveraging class-wise semantic information, we estimate the classifier weights of old domains within the latest embedding space. By merging historical and estimated classifiers, we align them with the consolidated embedding space, facilitating incremental classification. Extensive experimental results on four benchmark datasets demonstrate Duct's state-of-the-art performance. Code is available at: https://github.com/Estrella-fugaz/CVPR25-Duct.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Dual_Consolidation_for_Pre-Trained_Model-Based_Domain-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Dual_Consolidation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Da-Wei and Cai, Zi-Wen and Ye, Han-Jia and Zhang, Lijun and Zhan, De-Chuan},
    title     = {Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20547-20557}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "National Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Nanjing University", "National Key Laboratory for Novel Software Technology, Nanjing University"]


title: Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References
authors: Yitang Li, Mingxian Lin, Zhuo Lin, Yipeng Deng, Yue Cao, Li Yi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27673-27682
cities: ["Beijing", "Shanghai", "Shanghai", "Hong Kong", "Unknown"]
countries: ["China", "China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Learning_Physics-Based_Full-Body_Human_Reaching_and_Grasping_from_Brief_Walking_CVPR_2025_paper.html

abstract:
Existing motion generation methods based on mocap data are often limited by data quality and coverage. In this work, we propose a framework that generates diverse, physically feasible full-body human reaching and grasping motions using only brief walking mocap data. Base on the observation that walking data captures valuable movement patterns transferable across tasks and, on the other hand, the advanced kinematic methods can generate diverse grasping poses, which can then be interpolated into motions to serve as task-specific guidance. Our approach incorporates an active data generation strategy to maximize the utility of the generated motions, along with a local feature alignment mechanism that transfers natural movement patterns from walking data to enhance both the success rate and naturalness of the synthesized motions. By combining the fidelity and stability of natural walking with the flexibility and generalizability of task-specific generated data, our method demonstrates strong performance and robust adaptability in diverse scenes and with unseen objects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Learning_Physics-Based_Full-Body_Human_Reaching_and_Grasping_from_Brief_Walking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Learning_Physics-Based_Full-Body_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yitang and Lin, Mingxian and Lin, Zhuo and Deng, Yipeng and Cao, Yue and Yi, Li},
    title     = {Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27673-27682}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Galbot", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Tsinghua University", "Shanghai Qi Zhi Institute", "Shanghai Artificial Intelligence Laboratory", "The University of Hong Kong", "Galbot"]


title: EmoEdit: Evoking Emotions through Image Manipulation
authors: Jingyuan Yang, Jiawei Feng, Weibin Luo, Dani Lischinski, Daniel Cohen-Or, Hui Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24690-24699
affiliations: ["Shenzhen University", "The Hebrew University of Jerusalem", "T el A viv University"]
cities: ["Shenzhen", "Jerusalem", "Tel Aviv"]
countries: ["China", "Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_EmoEdit_Evoking_Emotions_through_Image_Manipulation_CVPR_2025_paper.html

abstract:
Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotions. This task is inherently complex due to its twofold objective: evoking the intended emotion while preserving image composition. Existing AIM methods primarily adjust color and style, often failing to elicit precise, profound emotional shifts. Drawing on psychological insights, we introduce EmoEdit, which extends AIM by incorporating content modifications to enhance emotional impact. Specifically, we construct EmoEditSet, a large-scale AIM dataset of 40,120 paired data through emotion attribution and data construction. To make generative models emotion-aware, we design an Emotion Adapter and train it using EmoEditSet. We further propose an instruction loss to capture semantic variations in each data pair. Our method is evaluated both qualitatively and quantitatively, demonstrating superior performance over state-of-the-art techniques. Additionally, we showcase the portability of our Emotion Adapter to other diffusion-based models, enhancing their emotion knowledge with diverse semantics. Code is available at: https://github.com/JingyuanYY/EmoEdit.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_EmoEdit_Evoking_Emotions_through_Image_Manipulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_EmoEdit_Evoking_Emotions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jingyuan and Feng, Jiawei and Luo, Weibin and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui},
    title     = {EmoEdit: Evoking Emotions through Image Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24690-24699}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Hebrew University of Jerusalem", "city": "Jerusalem", "country": "Israel"}, {"affiliation": "T el A viv University", "city": "Tel Aviv", "country": "Israel"}]


title: RORem: Training a Robust Object Remover with Human-in-the-Loop
authors: Ruibin Li, Tao Yang, Song Guo, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14024-14035
cities: ["Hong Kong", "Shenzhen", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_RORem_Training_a_Robust_Object_Remover_with_Human-in-the-Loop_CVPR_2025_paper.html

abstract:
Despite the significant advancements, existing object removal methods struggle with incomplete removal, incorrect content synthesis and blurry synthesized regions, resulting in low success rates. Such issues are mainly caused by the lack of high-quality paired training data, as well as the self-supervised training paradigm adopted in these methods, which forces the model to in-paint the masked regions, leading to ambiguity between synthesizing the masked objects and restoring the background. To address these issues, we propose a semi-supervised learning strategy with human-in-the-loop to create high-quality paired training data, aiming to train a Robust Object Remover (RORem). We first collect 60K training pairs from open-source datasets to train an initial object removal model for generating removal samples, and then utilize human feedback to select a set of high-quality object removal pairs, with which we train a discriminator to automate the following training data generation process. By iterating this process for several rounds, we finally obtain a substantial object removal dataset with over 200K pairs. Fine-tuning the pre-trained stable diffusion model with this dataset, we obtain our RORem, which demonstrates state-of-the-art object removal performance in terms of both reliability and image quality. Particularly, RORem improves the object removal success rate over previous methods by more than 18%. The dataset, source code and trained model will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_RORem_Training_a_Robust_Object_Remover_with_Human-in-the-Loop_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_RORem_Training_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ruibin and Yang, Tao and Guo, Song and Zhang, Lei},
    title     = {RORem: Training a Robust Object Remover with Human-in-the-Loop},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14024-14035}
}

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

affiliations:
["The Hong Kong Polytechnic University", "OPPO Research Institute", "ByteDance", "The Hong Kong University of Science and Technology"]


title: All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19565-19575
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vayani_All_Languages_Matter_Evaluating_LMMs_on_Culturally_Diverse_100_Languages_CVPR_2025_paper.html

authors:
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana, Noor Ahsan, Nevasini Sasikumar, Omkar Thawakar, Henok Biadglign Ademtew, Yahya Hmaiti, Amandeep Kumar, Kartik Kukreja, Mykola Maslych, Wafa Al Ghallabi, Mihail Minkov Mihaylov, Chao Qin, Abdelrahman M. Shaker, Mike Zhang, Mahardika Krisna Ihsani, Amiel Gian Esplana, Monil Gokani, Shachar Mirkin, Harsh Singh, Ashay Srivastava, Endre Hamerlik, Fathinah Asma Izzati, Fadillah Adamsyah Maani, Sebastian Cavada, Jenny Chim, Rohit Gupta, Sanjay Manjunath, Kamila Zhumakhanova, Feno Heriniaina Rabevohitra, Azril Hafizi Amirudin, Muhammad Ridzuan, Daniya Najiha Abdul Kareem, Ketan Pravin More, Kunyang Li, Pramesh Shakya, Muhammad Saad, Amirpouya Ghasemaghaei, Amirbek Djanibekov, Dilshod Azizov, Branislava Jankovic, Naman Bhatia, Alvaro Cabrera, Johan Obando-Ceron, Olympiah Otieno, Febian Farestam, Muztoba Rabbani, Sanoojan Ballah, Santosh Sanjeev, Abduragim Shtanchaev, Maheen Fatima, Thao Nguyen, Amrin Kareem, Toluwani Aremu, Nathan Augusto Zacarias Xavier, Amit Bhatkal, Hawau Olamide Toyin, Aman Chadha, Hisham Cholakkal, Rao Muhammad Anwer, Michael Felsberg, Jorma Laaksonen, Thamar Solorio, Monojit Choudhury, Ivan Laptev, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan

abstract:
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages.  As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.  ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in multimodal research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including True/False, multiple choice, and open-ended questions, which are further divided into short and long-answer categories.  ALM-bench design ensures a comprehensive assessment of a model's ability to handle varied levels of difficulty in visual and linguistic reasoning.  To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations.  Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark will be publicly released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vayani_All_Languages_Matter_Evaluating_LMMs_on_Culturally_Diverse_100_Languages_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vayani_All_Languages_Matter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vayani_2025_CVPR,
    author    = {Vayani, Ashmal and Dissanayake, Dinura and Watawana, Hasindri and Ahsan, Noor and Sasikumar, Nevasini and Thawakar, Omkar and Ademtew, Henok Biadglign and Hmaiti, Yahya and Kumar, Amandeep and Kukreja, Kartik and Maslych, Mykola and Al Ghallabi, Wafa and Mihaylov, Mihail Minkov and Qin, Chao and Shaker, Abdelrahman M. and Zhang, Mike and Ihsani, Mahardika Krisna and Esplana, Amiel Gian and Gokani, Monil and Mirkin, Shachar and Singh, Harsh and Srivastava, Ashay and Hamerlik, Endre and Izzati, Fathinah Asma and Maani, Fadillah Adamsyah and Cavada, Sebastian and Chim, Jenny and Gupta, Rohit and Manjunath, Sanjay and Zhumakhanova, Kamila and Rabevohitra, Feno Heriniaina and Amirudin, Azril Hafizi and Ridzuan, Muhammad and Kareem, Daniya Najiha Abdul and More, Ketan Pravin and Li, Kunyang and Shakya, Pramesh and Saad, Muhammad and Ghasemaghaei, Amirpouya and Djanibekov, Amirbek and Azizov, Dilshod and Jankovic, Branislava and Bhatia, Naman and Cabrera, Alvaro and Obando-Ceron, Johan and Otieno, Olympiah and Farestam, Febian and Rabbani, Muztoba and Ballah, Sanoojan and Sanjeev, Santosh and Shtanchaev, Abduragim and Fatima, Maheen and Nguyen, Thao and Kareem, Amrin and Aremu, Toluwani and Xavier, Nathan Augusto Zacarias and Bhatkal, Amit and Toyin, Hawau Olamide and Chadha, Aman and Cholakkal, Hisham and Anwer, Rao Muhammad and Felsberg, Michael and Laaksonen, Jorma and Solorio, Thamar and Choudhury, Monojit and Laptev, Ivan and Shah, Mubarak and Khan, Salman and Khan, Fahad Shahbaz},
    title     = {All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19565-19575}
}

raw_affiliation:
[{"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}, {"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}, {"affiliation": "Aalto University", "city": "Espoo", "country": "Finland"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Link\u00f6ping University", "city": "Link\u00f6ping", "country": "Sweden"}, {"affiliation": "Ehtiopian AI Institute", "city": "Addis Ababa", "country": "Ethiopia"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Aalborg University", "city": "Aalborg", "country": "Denmark"}, {"affiliation": "University of the West of England", "city": "Bristol", "country": "UK"}, {"affiliation": "IIT Hyderadabad", "city": "Hyderabad", "country": "India"}, {"affiliation": "Alpinference", "city": "Bern", "country": "Switzerland"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "HUN-REN Institute for CS and Control", "city": "Budapest", "country": "Hungary"}, {"affiliation": "Queen Mary University of London", "city": "London", "country": "UK"}, {"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "University of the People", "city": "Pasadena", "country": "USA"}, {"affiliation": "University of Montreal", "city": "Montreal", "country": "Canada"}, {"affiliation": "JKUAT", "city": "Nairobi", "country": "Kenya"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}, {"affiliation": "Air University", "city": "Islamabad", "country": "Pakistan"}, {"affiliation": "Universidade Federal de Minas Gerais", "city": "Belo Horizonte", "country": "Brazil"}]

affiliations:
["University of Central Florida", "Mohamed bin Zayed University of AI", "Amazon", "Aalto University", "Australian National University", "Link\u00f6ping University", "Ehtiopian AI Institute", "Johns Hopkins University", "Aalborg University", "University of the West of England", "IIT Hyderadabad", "Alpinference", "University of Maryland", "HUN-REN Institute for CS and Control", "Queen Mary University of London", "Chongqing University", "University of the People", "University of Montreal", "JKUAT", "ETH Zurich", "University of California, Merced", "Air University", "Universidade Federal de Minas Gerais"]

cities:
["Orlando", "Abu Dhabi", "Seattle", "Espoo", "Canberra", "Link\u00f6ping", "Addis Ababa", "Baltimore", "Aalborg", "Bristol", "Hyderabad", "Bern", "College Park", "Budapest", "London", "Chongqing", "Pasadena", "Montreal", "Nairobi", "Zurich", "Merced", "Islamabad", "Belo Horizonte"]

countries:
["USA", "UAE", "USA", "Finland", "Australia", "Sweden", "Ethiopia", "USA", "Denmark", "UK", "India", "Switzerland", "USA", "Hungary", "UK", "China", "USA", "Canada", "Kenya", "Switzerland", "USA", "Pakistan", "Brazil"]


title: SparseAlign: a Fully Sparse Framework for Cooperative Object Detection
authors: Yunshuang Yuan, Yan Xia, Daniel Cremers, Monika Sester
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22296-22305
cities: ["Hannover", "Munich", "Munich"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_SparseAlign_a_Fully_Sparse_Framework_for_Cooperative_Object_Detection_CVPR_2025_paper.html

abstract:
Cooperative perception can increase the view field and decrease the occlusion of an ego vehicle, hence improving the perception performance and safety of autonomous driving. Despite the success of previous works on cooperative object detection, they mostly operate on dense Bird's Eye View (BEV) feature maps, which are computationally demanding and can hardly be extended to long-range detection problems. More efficient fully sparse frameworks are rarely explored. In this work, we design a fully sparse framework, SparseAlign, with three key features: an enhanced sparse 3D backbone, a query-based temporal context learning module, and a robust detection head specially tailored for sparse features. Extensive experimental results on both OPV2V and DairV2X datasets show that our framework, despite its sparsity, outperforms the state of the art with less communication bandwidth requirements. In addition, experiments on the OPV2Vt and DairV2Xt datasets for time-aligned cooperative object detection also show a significant performance gain compared to the baseline works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_SparseAlign_a_Fully_Sparse_Framework_for_Cooperative_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_SparseAlign_a_Fully_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Yunshuang and Xia, Yan and Cremers, Daniel and Sester, Monika},
    title     = {SparseAlign: a Fully Sparse Framework for Cooperative Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22296-22305}
}

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

affiliations:
["Leibniz University Hannover", "Technical University of Munich", "Munich Center for Machine Learning (MCML)"]


title: Video-Bench: Human-Aligned Video Generation Benchmark
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18858-18868
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Video-Bench_Human-Aligned_Video_Generation_Benchmark_CVPR_2025_paper.html

authors:
Hui Han, Siyuan Li, Jiaqi Chen, Yiwen Yuan, Yuling Wu, Yufan Deng, Chak Tou Leong, Hanwen Du, Junchen Fu, Youhua Li, Jie Zhang, Chi Zhang, Li-jia Li, Yongxin Ni

abstract:
Video generation assessment is essential for ensuring that generative models produce visually realistic, high-quality videos while aligning with human expectations. Current video generation benchmarks fall into two main categories: traditional benchmarks, which use metrics and embeddings to evaluate generated video quality across multiple dimensions but often lack alignment with human judgments; and large language model (LLM)-based benchmarks, though capable of human-like reasoning, are constrained by a limited understanding of video quality metrics and cross-modal consistency.To address these challenges and establish a benchmark that better aligns with human preferences, this paper introduces Video-Bench, a comprehensive benchmark featuring a rich prompt suite and extensive evaluation dimensions. This benchmark represents the first attempt to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models. By incorporating few-shot scoring and chain-of-query techniques, Video-Bench provides a structured, scalable approach to generated video evaluation. Experimental results demonstrate that MLLMs achieve superior alignment with human preferences across all dimensions. Moreover, in instances where our framework's assessments diverge from human evaluations, it consistently offers more objective and accurate insights, suggesting an even greater potential advantage over traditional human judgment.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Video-Bench_Human-Aligned_Video_Generation_Benchmark_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Video-Bench_Human-Aligned_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Hui and Li, Siyuan and Chen, Jiaqi and Yuan, Yiwen and Wu, Yuling and Deng, Yufan and Leong, Chak Tou and Du, Hanwen and Fu, Junchen and Li, Youhua and Zhang, Jie and Zhang, Chi and Li, Li-jia and Ni, Yongxin},
    title     = {Video-Bench: Human-Aligned Video Generation Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18858-18868}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Fellou AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Soochow University", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Glasgow", "city": "Glasgow", "country": "UK"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "LiveX AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Shanghai Jiao Tong University", "Stanford University", "Fellou AI", "Fudan University", "Carnegie Mellon University", "Peking University", "Hong Kong Polytechnic University", "Soochow University", "University of Glasgow", "City University of Hong Kong", "Westlake University", "LiveX AI", "National University of Singapore"]

cities:
["Shanghai", "Stanford", "Unknown", "Shanghai", "Pittsburgh", "Beijing", "Hong Kong", "Suzhou", "Glasgow", "Hong Kong", "Hangzhou", "Unknown", "Singapore"]

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


title: Data Distributional Properties As Inductive Bias for Systematic Generalization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25590-25601
affiliations: ["Ponti\ufb01cia Universidad Cat\u00b4olica de Chile", "University of Warwick", "CENIA"]
cities: ["Santiago", "Warwick", "Santiago"]
countries: ["Chile", "UK", "Chile"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/del_Rio_Data_Distributional_Properties_As_Inductive_Bias_for_Systematic_Generalization_CVPR_2025_paper.html

authors:
Felipe del Rio, Alain Raymond-Saez, Daniel Florea, Rodrigo Toro Icarte, Julio Hurtado, Cristian B. Calderon, Alvaro Soto

abstract:
Deep neural networks (DNNs) struggle at systematic generalization (SG). Several studies have evaluated the possibility of promoting SG through the proposal of novel architectures, loss functions, or training methodologies. Few studies, however, have focused on the role of training data properties in promoting SG. In this work, we investigate the impact of certain data distributional properties, as inductive biases for the SG ability of a multi-modal language model. To this end, we study three different properties. First, data diversity, instantiated as an increase in the possible values a latent property in the training distribution may take. Second, burstiness, where we probabilistically restrict the number of possible values of latent factors on particular inputs during training. Third, latent intervention, where a particular latent factor is altered randomly during training. We find that all three factors significantly enhance SG, with diversity contributing an 89% absolute increase in accuracy in the most affected property. Through a series of experiments, we test various hypotheses to understand why these properties promote SG. Finally, we find that Normalized Mutual Information (NMI) between latent attributes in the training distribution is strongly predictive of out-of-distribution generalization. We find that a mechanism by which lower NMI induces SG is in the geometry of representations. In particular, we find that NMI induces more parallelism in neural representations (i.e., input features coded in parallel neural vectors) of the model, a property related to the capacity of reasoning by analogy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/del_Rio_Data_Distributional_Properties_As_Inductive_Bias_for_Systematic_Generalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/del_Rio_Data_Distributional_Properties_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{del_Rio_2025_CVPR,
    author    = {del Rio, Felipe and Raymond-Saez, Alain and Florea, Daniel and Icarte, Rodrigo Toro and Hurtado, Julio and Calderon, Cristian B. and Soto, Alvaro},
    title     = {Data Distributional Properties As Inductive Bias for Systematic Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25590-25601}
}

raw_affiliation:
[{"affiliation": "Ponti\ufb01cia Universidad Cat\u00b4olica de Chile", "city": "Santiago", "country": "Chile"}, {"affiliation": "University of Warwick", "city": "Warwick", "country": "UK"}, {"affiliation": "CENIA", "city": "Santiago", "country": "Chile"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19713-19723
cities: ["Hangzhou", "Beijing", "Hangzhou", "Hong Kong", "Shenzhen", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MergeVQ_A_Unified_Framework_for_Visual_Generation_and_Representation_with_CVPR_2025_paper.html

title:
MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization

authors:
Siyuan Li, Luyuan Zhang, Zedong Wang, Juanxi Tian, Cheng Tan, Zicheng Liu, Chang Yu, Qingsong Xie, Haonan Lu, Haoqian Wang, Zhen Lei

abstract:
Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great success in both self-supervised pre-training and image generation. However, most existing methods struggle to address the trade-off in the shared latent space for generation quality vs. representation learning and efficiency. To push the limits of this paradigm, we propose MergeVQ, which incorporates token merging techniques into VQ-based generative models to bridge the gap between image generation and visual representation learning in a unified architecture. During pre-training, MergeVQ decouples top-k semantics from latent space with the token merge module after self-attention blocks in the encoder for subsequent Look-up Free Quantization (LFQ) and global alignment and recovers their fine-grained details through cross-attention in the decoder for reconstruction. As for second-stage generation, we introduce MergeAR, which performs KV Cache compression for efficient raster-order prediction. Extensive experiments on ImageNet verify that MergeVQ as an AR generative model achieves competitive performance in both visual representation learning and image generation tasks while maintaining favorable token efficiency and inference speed. The code and model will be available at https://apexgen-x.github.io/MergeVQ.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MergeVQ_A_Unified_Framework_for_Visual_Generation_and_Representation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MergeVQ_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Siyuan and Zhang, Luyuan and Wang, Zedong and Tian, Juanxi and Tan, Cheng and Liu, Zicheng and Yu, Chang and Xie, Qingsong and Lu, Haonan and Wang, Haoqian and Lei, Zhen},
    title     = {MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19713-19723}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "OPPO AI Center", "city": "Shenzhen", "country": "China"}, {"affiliation": "CAIR, HKISI-CAS", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Zhejiang University", "Tsinghua University", "Westlake University", "The Hong Kong University of Science and Technology", "OPPO AI Center", "CAIR, HKISI-CAS", "University of Chinese Academy of Sciences"]


title: InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7048-7060
raw_affiliation: [{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_InterAct_Advancing_Large-Scale_Versatile_3D_Human-Object_Interaction_Generation_CVPR_2025_paper.html

authors:
Sirui Xu, Dongting Li, Yucheng Zhang, Xiyan Xu, Qi Long, Ziyin Wang, Yunzhi Lu, Shuchang Dong, Hezi Jiang, Akshat Gupta, Yu-Xiong Wang, Liang-Yan Gui

abstract:
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remain challenging due to dataset limitations. Existing datasets often lack extensive, high-quality motion and annotation and exhibit artifacts such as contact penetration, floating, and incorrect hand motions. To address these issues, we introduce InterAct, a large-scale 3D HOI benchmark featuring dataset and methodological advancements. First, we consolidate and standardize 21.81 hours of HOI data from diverse sources, enriching it with detailed textual annotations. Second, we propose a unified optimization framework to enhance data quality by reducing artifacts and correcting hand motions. Leveraging the principle of contact invariance, we maintain human-object relationships while introducing motion variations, expanding the dataset to 30.70 hours. Third, we define six benchmarking tasks and develop a unified HOI generative modeling perspective, achieving state-of-the-art performance. Extensive experiments validate the utility of our dataset as a foundational resource for advancing 3D human-object interaction generation. The dataset will be publicly accessible to support further research in the field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_InterAct_Advancing_Large-Scale_Versatile_3D_Human-Object_Interaction_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_InterAct_Advancing_Large-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Sirui and Li, Dongting and Zhang, Yucheng and Xu, Xiyan and Long, Qi and Wang, Ziyin and Lu, Yunzhi and Dong, Shuchang and Jiang, Hezi and Gupta, Akshat and Wang, Yu-Xiong and Gui, Liang-Yan},
    title     = {InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7048-7060}
}


title: TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20979-20989
cities: ["Stony Brook", "Boston", "Stanford"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_TopoCellGen_Generating_Histopathology_Cell_Topology_with_a_Diffusion_Model_CVPR_2025_paper.html

authors:
Meilong Xu, Saumya Gupta, Xiaoling Hu, Chen Li, Shahira Abousamra, Dimitris Samaras, Prateek Prasanna, Chao Chen

abstract:
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting training data, aligns more closely with pathologists' domain knowledge, and offers new opportunities for controlling and generalizing the tumor microenvironment. In this paper, we propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies. Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks such as cell detection and classification. To assess the topological fidelity of generated layouts, we introduce a new metric, Topological Frechet Distance (TopoFD), which overcomes the limitations of traditional metrics like FID in evaluating topological structure. Experimental results demonstrate the effectiveness of our approach in generating multi-class cell layouts that capture intricate topological relationships. Code is available at https://github.com/Melon-Xu/TopoCellGen.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_TopoCellGen_Generating_Histopathology_Cell_Topology_with_a_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_TopoCellGen_Generating_Histopathology_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Meilong and Gupta, Saumya and Hu, Xiaoling and Li, Chen and Abousamra, Shahira and Samaras, Dimitris and Prasanna, Prateek and Chen, Chao},
    title     = {TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20979-20989}
}

raw_affiliation:
[{"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School", "city": "Boston", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]

affiliations:
["Stony Brook University", "Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School", "Stanford University"]


title: Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models
authors: Jiaming Zhang, Junhong Ye, Xingjun Ma, Yige Li, Yunfan Yang, Yunhao Chen, Jitao Sang, Dit-Yan Yeung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19900-19909
cities: ["Hong Kong", "Beijing", "Shanghai", "Singapore", "Shenzhen"]
countries: ["China", "China", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Anyattack_Towards_Large-scale_Self-supervised_Adversarial_Attacks_on_Vision-language_Models_CVPR_2025_paper.html

abstract:
Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios. However, recent studies have revealed that VLMs are vulnerable to image-based adversarial attacks. Traditional targeted adversarial attacks require specific targets and labels, limiting their real-world impact. We present AnyAttack, a self-supervised framework that transcends the limitations of conventional attacks through a novel foundation model approach. By pre-training on the massive LAION-400M dataset without label supervision, AnyAttack achieves unprecedented flexibility - enabling any image to be transformed into an attack vector targeting any desired output across different VLMs. This approach fundamentally changes the threat landscape, making adversarial capabilities accessible at an unprecedented scale. Our extensive validation across five open-source VLMs (CLIP, BLIP, BLIP2, InstructBLIP, and MiniGPT-4) demonstrates AnyAttack's effectiveness across diverse multimodal tasks. Most concerning, AnyAttack seamlessly transfers to commercial systems including Google Gemini, Claude Sonnet, Microsoft Copilot and OpenAI GPT, revealing a systemic vulnerability requiring immediate attention.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Anyattack_Towards_Large-scale_Self-supervised_Adversarial_Attacks_on_Vision-language_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jiaming and Ye, Junhong and Ma, Xingjun and Li, Yige and Yang, Yunfan and Chen, Yunhao and Sang, Jitao and Yeung, Dit-Yan},
    title     = {Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19900-19909}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Hong Kong University of Science and Technology", "Beijing Jiaotong University", "Fudan University", "Singapore Management University", "Peng Cheng Laboratory"]


title: Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
authors: Hoang Chuong Nguyen, Wei Mao, Jose M. Alvarez, Miaomiao Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11472-11481
affiliations: ["Australian National University", "NVIDIA"]
cities: ["Canberra", "Santa Clara"]
countries: ["Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_Joint_Optimization_of_Neural_Radiance_Fields_and_Continuous_Camera_Motion_CVPR_2025_paper.html

abstract:
Neural Radiance Fields (NeRF) has demonstrated its superior capability to represent 3D geometry but require accurately precomputed camera poses during training. To mitigate this requirement, existing methods jointly optimize camera poses and NeRF often relying on good pose initialisation or depth priors. However, these approaches struggle in challenging scenarios, such as large rotations, as they map each camera to a world coordinate system. We propose a novel method that eliminates prior dependencies by modeling continuous camera motions as time-dependent angular velocity and velocity. Relative motions between cameras are learned first via velocity integration, while camera poses can be obtained by aggregating such relative motions up to a world coordinate system defined at a single time step within the video. Specifically, accurate continuous camera movements are learned through a time-dependent NeRF, which captures local scene geometry and motion by training from neighboring frames for each time step. The learned motions enable fine-tuning the NeRF to represent the full scene geometry. Experiments on Co3D and Scannet show our approach achieves superior camera pose and depth estimation and comparable novel-view synthesis performance compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_Joint_Optimization_of_Neural_Radiance_Fields_and_Continuous_Camera_Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_Joint_Optimization_of_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Hoang Chuong and Mao, Wei and Alvarez, Jose M. and Liu, Miaomiao},
    title     = {Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11472-11481}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing
authors: Chun Gu, Xiaofei Wei, Zixuan Zeng, Yuxuan Yao, Li Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10943-10952
affiliations: ["School of Data Science, Fudan University", "Shanghai Innovation Institute"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_IRGS_Inter-Reflective_Gaussian_Splatting_with_2D_Gaussian_Ray_Tracing_CVPR_2025_paper.html

abstract:
In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate incident light, resulting in inaccurate material and lighting estimations. To this end, we introduce the inter-reflective Gaussian splatting (IRGS) framework for inverse rendering. To capture inter-reflection, we apply the full rendering equation without simplification and compute incident radiance on the fly using the proposed differentiable 2D Gaussian ray tracing. Additionally, we present an efficient optimization scheme to handle the computational demands of Monte Carlo sampling for rendering equation evaluation. Furthermore, we introduce a novel strategy for querying the indirect radiance of incident light when relighting the optimized scenes. Extensive experiments on multiple standard benchmarks validate the effectiveness of IRGS, demonstrating its capability to accurately model complex inter-reflection effects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_IRGS_Inter-Reflective_Gaussian_Splatting_with_2D_Gaussian_Ray_Tracing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_IRGS_Inter-Reflective_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Chun and Wei, Xiaofei and Zeng, Zixuan and Yao, Yuxuan and Zhang, Li},
    title     = {IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10943-10952}
}

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


title: InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions
authors: Sirui Xu, Hung Yu Ling, Yu-Xiong Wang, Liang-Yan Gui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12266-12277
affiliations: ["University of Illinois Urbana-Champaign", "Electronic Arts"]
cities: ["Urbana", "Redwood City"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_InterMimic_Towards_Universal_Whole-Body_Control_for_Physics-Based_Human-Object_Interactions_CVPR_2025_paper.html

abstract:
Achieving realistic simulations of humans interacting with a wide range of objects has long been a fundamental goal. Extending physics-based motion imitation to complex human-object interactions (HOIs) is challenging due to intricate human-object coupling, variability in object geometries, and artifacts in motion capture data, such as inaccurate contacts and limited hand detail. We introduce InterMimic, a framework that enables a single policy to robustly learn from hours of imperfect MoCap data covering diverse full-body interactions with dynamic and varied objects. Our key insight is to employ a curriculum strategy -- perfect first, then scale up. We first train subject-specific teacher policies to mimic, retarget, and refine motion capture data. Next, we distill these teachers into a student policy, with the teachers acting as online experts providing direct supervision, as well as high-quality references. Notably, we incorporate RL fine-tuning on the student policy to surpass mere demonstration replication and achieve higher-quality solutions. Our experiments demonstrate that InterMimic produces realistic and diverse interactions across multiple HOI datasets. The learned policy generalizes in a zero-shot manner and seamlessly integrates with kinematic generators, elevating the framework from mere imitation to generative modeling of complex human-object interactions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_InterMimic_Towards_Universal_Whole-Body_Control_for_Physics-Based_Human-Object_Interactions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_InterMimic_Towards_Universal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Sirui and Ling, Hung Yu and Wang, Yu-Xiong and Gui, Liang-Yan},
    title     = {InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12266-12277}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "Electronic Arts", "city": "Redwood City", "country": "USA"}]


title: Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
authors: Zichen Tian, Yaoyao Liu, Qianru Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23037-23047
affiliations: ["Singapore Management University", "University of Illinois Urbana-Champaign"]
cities: ["Singapore", "Urbana-Champaign"]
countries: ["Singapore", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Meta-Learning_Hyperparameters_for_Parameter_Efficient_Fine-Tuning_CVPR_2025_paper.html

abstract:
Training large foundation models from scratch for domain-specific applications is almost impossible due to data limits and long-tailed distributions -- taking remote sensing (RS) as an example. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module influence during fine-tuning. MetaPEFT dynamically adjusts three key factors of PEFT on RS images: module insertion, layer selection, and module-wise learning rates, which collectively control the influence of PEFT modules across the network. We conduct extensive experiments on three transfer-learning scenarios and five datasets in both RS and natural image domains. The results show that MetaPEFT achieves state-of-the-art performance in cross-spectral adaptation, requiring only a small amount of trainable parameters and improving tail-class accuracy significantly.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Meta-Learning_Hyperparameters_for_Parameter_Efficient_Fine-Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Meta-Learning_Hyperparameters_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Zichen and Liu, Yaoyao and Sun, Qianru},
    title     = {Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23037-23047}
}

raw_affiliation:
[{"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}]


title: TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
authors: Dana Cohen-Bar, Daniel Cohen-Or, Gal Chechik, Yoni Kasten
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21403-21413
affiliations: ["Tel Aviv University", "NVIDIA"]
cities: ["Tel Aviv", "Santa Clara"]
countries: ["Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cohen-Bar_TriTex_Learning_Texture_from_a_Single_Mesh_via_Triplane_Semantic_CVPR_2025_paper.html

abstract:
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-to-image diffusion models for texturing, they often struggle to preserve the appearance of the source texture during texture transfer. We present TriTex, a novel approach that learns a volumetric texture field from a single textured mesh by mapping semantic features to surface colors. Using an efficient triplane-based architecture, our method enables semantic-aware texture transfer to a novel target mesh. Despite training on just one example, it generalizes effectively to diverse shapes within the same category. Extensive evaluation on our newly created benchmark dataset shows that TriTex achieves superior texture transfer quality and fast inference times compared to existing methods. Our approach advances single-example texture transfer, providing a practical solution for maintaining visual coherence across related 3D models in applications like game development and simulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cohen-Bar_TriTex_Learning_Texture_from_a_Single_Mesh_via_Triplane_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cohen-Bar_TriTex_Learning_Texture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cohen-Bar_2025_CVPR,
    author    = {Cohen-Bar, Dana and Cohen-Or, Daniel and Chechik, Gal and Kasten, Yoni},
    title     = {TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21403-21413}
}

raw_affiliation:
[{"affiliation": "Tel Aviv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning
authors: Kunyu Wang, Xueyang Fu, Xin Lu, Chengjie Ge, Chengzhi Cao, Wei Zhai, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10577-10586
cities: ["Hefei"]
countries: ["China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Efficient_Test-time_Adaptive_Object_Detection_via_Sensitivity-Guided_Pruning_CVPR_2025_paper.html

abstract:
Continual test-time adaptive object detection (CTTA-OD) aims to online adapt a source pre-trained detector to ever-changing environments during inference under continuous domain shifts. Most existing CTTA-OD methods prioritize effectiveness while overlooking computational efficiency, which is crucial for resource-constrained scenarios. In this paper, we propose an efficient CTTA-OD method via pruning. Our motivation stems from the observation that not all learned source features are beneficial; certain domain-sensitive feature channels can adversely affect target domain performance. Inspired by this, we introduce a sensitivity-guided channel pruning strategy that quantifies each channel based on its sensitivity to domain discrepancies at both image and instance levels. We apply weighted sparsity regularization to selectively suppress and prune these sensitive channels, focusing adaptation efforts on invariant ones. Additionally, we introduce a stochastic channel reactivation mechanism to restore pruned channels, enabling recovery of potentially useful features and mitigating the risks of early pruning. Extensive experiments on three benchmarks show that our method achieves superior adaptation performance while reducing computational overhead by 12% in FLOPs compared to the recent SOTA method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Efficient_Test-time_Adaptive_Object_Detection_via_Sensitivity-Guided_Pruning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Kunyu and Fu, Xueyang and Lu, Xin and Ge, Chengjie and Cao, Chengzhi and Zhai, Wei and Zha, Zheng-Jun},
    title     = {Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10577-10586}
}

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", "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"]


title: A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
authors: Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, Xiaojuan Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12143-12154
affiliations: ["The University of Hong Kong", "University of Edinburgh", "Shanghai AI Laboratory"]
cities: ["Hong Kong", "Edinburgh", "Shanghai"]
countries: ["China", "United Kingdom", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_A_Data-Centric_Revisit_of_Pre-Trained_Vision_Models_for_Robot_Learning_CVPR_2025_paper.html

abstract:
Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. Through systematic evaluation, we find that while DINO and iBOT outperform MAE across visuomotor control and perception tasks, they struggle when trained on non-(single-)object-centric (NOC) data--a limitation strongly correlated with their diminished ability to learn object-centric representations. This investigation indicates that the ability to form object-centric representations from the non-object-centric robotics dataset is the key to success for PVMs. Motivated by this discovery, we designed SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck to reduce the number of prototypes to encourage the emergence of objectness as well as cross-view consistency regularization for encouraging multiview invariance. Our experiments encompass pre-training on object-centric, scene-centric, web-crawled, and ego-centric data. Across all settings, our approach learns transferrable representations and achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations. When scaled up with million-scale datasets, our method also demonstrates superior data efficiency and scalability. Our code and models are publicly available at https://github.com/CVMI-Lab/SlotMIM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_A_Data-Centric_Revisit_of_Pre-Trained_Vision_Models_for_Robot_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wen_A_Data-Centric_Revisit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wen_2025_CVPR,
    author    = {Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
    title     = {A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12143-12154}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "United Kingdom"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: Visual Agentic AI for Spatial Reasoning with a Dynamic API
authors: Damiano Marsili, Rohun Agrawal, Yisong Yue, Georgia Gkioxari
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19446-19455
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/Marsili_Visual_Agentic_AI_for_Spatial_Reasoning_with_a_Dynamic_API_CVPR_2025_paper.html

abstract:
Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progress in AI has led to vision and language models capable of answering questions from images. However, their performance declines when tasked with 3D spatial reasoning. To tackle the complexity of such reasoning problems, we introduce an agentic program synthesis approach where LLM agents collaboratively generate a Pythonic API with new functions to solve common subproblems. Our method overcomes limitations of prior approaches that rely on a static, human-defined API, allowing it to handle a wider range of queries. To assess AI capabilities for 3D understanding, we introduce a new benchmark of queries involving multiple steps of grounding and inference. We show that our method outperforms prior zero-shot models for visual reasoning in 3D and empirically validate the effectiveness of our agentic framework for 3D spatial reasoning tasks. Project website: https://glab-caltech.github.io/vadar/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Marsili_Visual_Agentic_AI_for_Spatial_Reasoning_with_a_Dynamic_API_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Marsili_Visual_Agentic_AI_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Marsili_2025_CVPR,
    author    = {Marsili, Damiano and Agrawal, Rohun and Yue, Yisong and Gkioxari, Georgia},
    title     = {Visual Agentic AI for Spatial Reasoning with a Dynamic API},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19446-19455}
}


title: TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
authors: Yilong Wang, Zilin Gao, Qilong Wang, Zhaofeng Chen, Peihua Li, Qinghua Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3449-3459
affiliations: ["Tianjin University", "Dalian University of Technology", "Yancheng Institute of Technology"]
cities: ["Tianjin", "Dalian", "Yancheng"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TAMT_Temporal-Aware_Model_Tuning_for_Cross-Domain_Few-Shot_Action_Recognition_CVPR_2025_paper.html

abstract:
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer learning. Existing CDFSAR methods mainly focus on joint training of source and target data to mitigate the side effect of domain gap. However, such kind of methods suffer from two limitations: First, pair-wise joint training requires retraining deep models in case of one source data and multiple target ones, which incurs heavy computation cost, especially for large source and small target data. Second, pre-trained models after joint training are adopted to target domain in a straightforward manner, hardly taking full potential of pre-trained models and then limiting recognition performance. To overcome above limitations, this paper proposes a simple yet effective baseline, namely Temporal-Aware Model Tuning (TAMT) for CDFSAR. Specifically, our TAMT involves a decoupled paradigm by performing pre-training on source data and fine-tuning target data, which avoids retraining for multiple target data with single source. To effectively and efficiently explore the potential of pre-trained models in transferring to target domain, our TAMT proposes a Hierarchical Temporal Tuning Network (HTTN), whose core involves local temporal-aware adapters (TAA) and a global temporal-aware moment tuning (GTMT). Particularly, TAA learns few parameters to recalibrate the intermediate features of frozen pre-trained models, enabling efficient adaptation to target domains. Furthermore, GTMT helps to generate powerful video representations, improving match performance on the target domain. Experiments on several widely used video benchmarks show our TAMT outperforms the recently proposed counterparts by 13% 31%, achieving new state-of-the-art CDFSAR results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TAMT_Temporal-Aware_Model_Tuning_for_Cross-Domain_Few-Shot_Action_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TAMT_Temporal-Aware_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yilong and Gao, Zilin and Wang, Qilong and Chen, Zhaofeng and Li, Peihua and Hu, Qinghua},
    title     = {TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3449-3459}
}

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


title: Feature Spectrum Learning for Remote Sensing Change Detection
authors: Qi Zang, Dong Zhao, Shuang Wang, Dou Quan, Zhun Zhong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12647-12657
cities: ["Xi'an", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zang_Feature_Spectrum_Learning_for_Remote_Sensing_Change_Detection_CVPR_2025_paper.html

abstract:
Change detection (CD) holds significant implications for Earth observation, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challenges. Existing methods mainly regard pseudo-changes as a kind of style shift and alleviate it by transforming bitemporal images into the same style using generative adversarial networks (GANs). Nevertheless, their efforts are limited by the complexity of optimizing GANs and the absence of guidance from physical properties. This paper finds that the spectrum transformation (ST) has the potential to mitigate pseudo-changes by aligning in the frequency domain carrying the style. However, the benefit of ST is largely constrained by two drawbacks: 1) limited transformation space and 2) inefficient parameter search. To address these limitations, we propose a Feature Spectrum learning (FeaSpect) that adaptively eliminate pseudo-changes in the latent space. For the drawback 1), FeaSpect directs the transformation towards style-aligned discriminative features via feature spectrum transformation (FST). For the drawback 2), FeaSpect allows FST to be trainable, efficiently discovering optimal parameters via extraction box with adaptive attention and extraction box with learnable strides. Extensive experiments on challenging datasets demonstrate that our method remarkably outperforms existing methods and achieves a commendable trade-off between accuracy and efficiency. Importantly, our method can be easily injected into other frameworks, achieving consistent improvements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zang_Feature_Spectrum_Learning_for_Remote_Sensing_Change_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zang_Feature_Spectrum_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zang_2025_CVPR,
    author    = {Zang, Qi and Zhao, Dong and Wang, Shuang and Quan, Dou and Zhong, Zhun},
    title     = {Feature Spectrum Learning for Remote Sensing Change Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12647-12657}
}

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

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


title: BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10372-10383
affiliations: ["BioX-CPath", "Computational Pathology"]
cities: ["London", "London"]
countries: ["UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_Diagnostics_for_Multistain_IHC_Computational_Pathology_CVPR_2025_paper.html

authors:
Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan, Elena Pontarini, Michele Bombardieri, Costantino Pitzalis, Myles J. Lewis, Michael R. Barnes, Luca Rossi, Gregory Slabaugh

abstract:
The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification that leverages both spatial and semantic features across multiple stains. At its core, BioXCPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis and Sjogren's Disease multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores, that permit measuring model alignment with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Source code and documentation can be found at:  https://github.com/AmayaGS/BioX-CPath.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_Diagnostics_for_Multistain_IHC_Computational_Pathology_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gallagher-Syed_2025_CVPR,
    author    = {Gallagher-Syed, Amaya and Senior, Henry and Alwazzan, Omnia and Pontarini, Elena and Bombardieri, Michele and Pitzalis, Costantino and Lewis, Myles J. and Barnes, Michael R. and Rossi, Luca and Slabaugh, Gregory},
    title     = {BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10372-10383}
}

raw_affiliation:
[{"affiliation": "BioX-CPath", "city": "London", "country": "UK"}, {"affiliation": "Computational Pathology", "city": "London", "country": "UK"}]


title: DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12015-12026
cities: ["Beijing", "Beijing", "Luoyang", "Beijing", "Beijing", "Beijing", "Munich"]
countries: ["China", "China", "China", "China", "China", "China", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DriveDreamer4D_World_Models_Are_Effective_Data_Machines_for_4D_Driving_CVPR_2025_paper.html

authors:
Guosheng Zhao, Chaojun Ni, Xiaofeng Wang, Zheng Zhu, Xueyang Zhang, Yida Wang, Guan Huang, Xinze Chen, Boyuan Wang, Youyi Zhang, Wenjun Mei, Xingang Wang

abstract:
Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on conditions closely aligned with training data distributions, which are largely confined to forward-driving scenarios. Consequently, these methods face limitations when rendering complex maneuvers (e.g., lane change, acceleration, deceleration). Recent advancements in autonomous-driving world models have demonstrated the potential to generate diverse driving videos. However, these approaches remain constrained to 2D video generation, inherently lacking the spatiotemporal coherence required to capture intricacies of dynamic driving environments.In this paper, we introduce DriveDreamer4D, which enhances 4D driving scene representation leveraging world model priors. Specifically, we utilize the world model as a data machine to synthesize novel trajectory videos, where structured conditions are explicitly leveraged to control the spatial-temporal consistency of traffic elements. Besides, the cousin data training strategy is proposed to facilitate merging real and synthetic data for optimizing 4DGS. To our knowledge, DriveDreamer4D is the first to utilize video generation models for improving 4D reconstruction in driving scenarios.Experimental results reveal that DriveDreamer4D significantly enhances generation quality under novel trajectory views, achieving a relative improvement in FID  by 32.1%, 46.4%, and 16.3% compared to PVG, S3Gaussian, and Deformable-GS. Moreover, DriveDreamer4D markedly enhances the spatiotemporal coherence of driving agents, which is verified by a comprehensive user study and the relative increases of 22.6%, 43.5%, and 15.6% in the NTA-IoU metric.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DriveDreamer4D_World_Models_Are_Effective_Data_Machines_for_4D_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DriveDreamer4D_World_Models_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Guosheng and Ni, Chaojun and Wang, Xiaofeng and Zhu, Zheng and Zhang, Xueyang and Wang, Yida and Huang, Guan and Chen, Xinze and Wang, Boyuan and Zhang, Youyi and Mei, Wenjun and Wang, Xingang},
    title     = {DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12015-12026}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences, China", "city": "Beijing", "country": "China"}, {"affiliation": "Luoyang Institute for Robot and Intelligent Equipment, China", "city": "Luoyang", "country": "China"}, {"affiliation": "Peking University, China", "city": "Beijing", "country": "China"}, {"affiliation": "GigaAI, China", "city": "Beijing", "country": "China"}, {"affiliation": "Li Auto Inc., China", "city": "Beijing", "country": "China"}, {"affiliation": "Technical University o f Munich, Germeny", "city": "Munich", "country": "Germany"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences, China", "School of Artificial Intelligence, University of Chinese Academy of Sciences, China", "Luoyang Institute for Robot and Intelligent Equipment, China", "Peking University, China", "GigaAI, China", "Li Auto Inc., China", "Technical University o f Munich, Germeny"]


title: LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models
authors: Xuan Cai, Renjie Pan, Hua Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14869-14880
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_LoKi_Low-dimensional_KAN_for_Efficient_Fine-tuning_Image_Models_CVPR_2025_paper.html

abstract:
'Pre-training + fine-tuning' has been widely used in various downstream tasks. Parameter-efficient fine-tuning (PEFT) has demonstrated higher efficiency and promising performance comapred to traditional full-tuning. The widely used adapter-based and prompt-based methods in PEFT can be uniformly represented as adding an MLP structure to the pre-trained model. These methods are prone to over-fitting in downstream tasks, due to the difference in data scale and distribution. To address this issue, we propose a new adapter-based PEFT module, i.e., LoKi, which consists of an encoder, a learnable activation layer, and a decoder. To maintain the simplicity of LoKi, we use single-layer linear networks for the encoder and decoder, and for the learnable activation layer, we use a Kolmogorov-Arnold Network (KAN) with the minimal number of layers (only 2 KAN linear layers). With a bottleneck rate much lower than that of Adapter, LoKi is equipped with fewer parameters (only half of Adapter) and eliminates slow training speed and high memory usage of KAN. We conduct extensive experiments on LoKi under image classification and video action recognition across 9 datasets. LoKi demonstrates highly competitive generalization performance compared to other PEFT methods with fewer tunable parameters, ensuring both effectiveness and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_LoKi_Low-dimensional_KAN_for_Efficient_Fine-tuning_Image_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_LoKi_Low-dimensional_KAN_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Xuan and Pan, Renjie and Yang, Hua},
    title     = {LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14869-14880}
}

raw_affiliation:
[{"affiliation": "Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Lab of Digital Media Processing and Transmission, China", "city": "Shanghai", "country": "China"}, {"affiliation": "China MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China", "city": "Shanghai", "country": "China"}]

affiliations:
["Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China", "Shanghai Key Lab of Digital Media Processing and Transmission, China", "China MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China"]


title: Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration
authors: Kim Jun-Seong, GeonU Kim, Kim Yu-Ji, Yu-Chiang Frank Wang, Jaesung Choe, Tae-Hyun Oh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14137-14146
cities: ["Pohang", "Pohang", "Santa Clara", "Daejeon"]
countries: ["South Korea", "South Korea", "USA", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jun-Seong_Dr._Splat_Directly_Referring_3D_Gaussian_Splatting_via_Direct_Language_CVPR_2025_paper.html

abstract:
We introduce Dr. Splat, a novel approach for open-vocabulary 3D scene understanding leveraging 3D Gaussian Splatting. Unlike existing language-embedded 3DGS methods, which rely on a rendering process, our method directly associates language-aligned CLIP embeddings with 3D Gaussians for holistic 3D scene understanding. The key of our method is a language feature registration technique where CLIP embeddings are assigned to the dominant Gaussians intersected by each pixel-ray.  Moreover, we integrate Product Quantization (PQ) trained on general large scale image data to compactly represent embeddings without per-scene optimization. Experiments demonstrate that our approach significantly outperforms existing approaches in 3D perception benchmarks, such as open-vocabulary 3D semantic segmentation, 3D object localization, and 3D object selection tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jun-Seong_Dr._Splat_Directly_Referring_3D_Gaussian_Splatting_via_Direct_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jun-Seong_Dr._Splat_Directly_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jun-Seong_2025_CVPR,
    author    = {Jun-Seong, Kim and Kim, GeonU and Yu-Ji, Kim and Wang, Yu-Chiang Frank and Choe, Jaesung and Oh, Tae-Hyun},
    title     = {Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14137-14146}
}

raw_affiliation:
[{"affiliation": "Department of Electrical Engineering, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Graduate School of AI, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "School of Computing, KAIST", "city": "Daejeon", "country": "South Korea"}]

affiliations:
["Department of Electrical Engineering, POSTECH", "Graduate School of AI, POSTECH", "NVIDIA", "School of Computing, KAIST"]


title: Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection
authors: Feng Yan, Xiaoheng Jiang, Yang Lu, Jiale Cao, Dong Chen, Mingliang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23860-23869
cities: ["Zhengzhou", "Zhengzhou", "Zhengzhou", "Tianjin"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Wavelet_and_Prototype_Augmented_Query-based_Transformer_for_Pixel-level_Surface_Defect_CVPR_2025_paper.html

abstract:
As an important part of intelligent manufacturing, pixel-level surface defect detection (SDD) aims to locate defect areas through mask prediction. Previous methods adopt the image-independent static convolution to indiscriminately classify per-pixel features for mask prediction, which leads to suboptimal results for some challenging scenes such as weak defects and cluttered backgrounds. In this paper, inspired by query-based methods, we propose a Wavelet and Prototype Augmented Query-based Transformer (WPFormer) for surface defect detection. Specifically,  a set of dynamic queries for mask prediction is updated through the dual-domain transformer decoder. Firstly, a Wavelet-enhanced Cross-Attention (WCA) is proposed, which aggregates meaningful high- and low-frequency information of image features in the wavelet domain to refine queries. WCA enhances the representation of high-frequency components by capturing multi-scale relationships between different frequency components, enabling queries to focus more on defect details. Secondly, a Prototype-guided Cross-Attention (PCA) is proposed to refine queries through meta-prototypes in the spatial domain. The prototypes aggregate semantically meaningful tokens from image features, facilitating queries to aggregate crucial defect information under the cluttered backgrounds. Extensive experiments on three defect detection datasets (i.e., ESDIs-SOD, CrackSeg9k, and ZJU-Leaper) demonstrate that the proposed method achieves state-of-the-art performance in defect detection. The code will be available at https://github.com/yfhdm/WPFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Wavelet_and_Prototype_Augmented_Query-based_Transformer_for_Pixel-level_Surface_Defect_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Wavelet_and_Prototype_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Feng and Jiang, Xiaoheng and Lu, Yang and Cao, Jiale and Chen, Dong and Xu, Mingliang},
    title     = {Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23860-23869}
}

raw_affiliation:
[{"affiliation": "Zhengzhou University", "city": "Zhengzhou", "country": "China"}, {"affiliation": "Engineering Research Center of Intelligent Swarm Systems, Ministry of Education", "city": "Zhengzhou", "country": "China"}, {"affiliation": "National Supercomputing Center in Zhengzhou", "city": "Zhengzhou", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}]

affiliations:
["Zhengzhou University", "Engineering Research Center of Intelligent Swarm Systems, Ministry of Education", "National Supercomputing Center in Zhengzhou", "Tianjin University"]


title: GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection
authors: José Henrique Lima Marques, Jeffri Murrugarra-Llerena, Claudio R. Jung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3593-3602
affiliations: ["Stony Brook University", "Federal University of Rio Grande do Sul"]
cities: ["Stony Brook", "Porto Alegre"]
countries: ["USA", "Brazil"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Marques_GauCho_Gaussian_Distributions_with_Cholesky_Decomposition_for_Oriented_Object_Detection_CVPR_2025_paper.html

abstract:
Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions based on Gaussian distributions has become attractive since they yield simple and differentiable terms. However, existing solutions are still based on regression heads that produce Oriented Bounding Boxes (OBBs), and the known problem of angular boundary discontinuity persists. In this work, we propose a regression head for OOD that directly produces Gaussian distributions based on the Cholesky matrix decomposition. The proposed head, named Gaucho, theoretically mitigates the boundary discontinuity problem and is fully compatible with recent Gaussian-based regression loss functions. Furthermore, we advocate using Oriented Ellipses (OEs) to represent oriented objects, which relates to GauCho through a bijective function and alleviates the encoding ambiguity problem for circular objects. Our experimental results show that GauCho can be a viable alternative to the traditional OBB head, achieving results comparable to or better than state-of-the-art detectors for the challenging dataset DOTA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Marques_GauCho_Gaussian_Distributions_with_Cholesky_Decomposition_for_Oriented_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Marques_GauCho_Gaussian_Distributions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Marques_2025_CVPR,
    author    = {Marques, Jos\'e Henrique Lima and Murrugarra-Llerena, Jeffri and Jung, Claudio R.},
    title     = {GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3593-3602}
}

raw_affiliation:
[{"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Federal University of Rio Grande do Sul", "city": "Porto Alegre", "country": "Brazil"}]


authors: Yuanhao Zou, Zhaozheng Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29623-29633
affiliations: ["University of Michigan, Ann Arbor", "Stony Brook University"]
cities: ["Ann Arbor", "Stony Brook"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zou_Alignment_Mining_and_Fusion_Representation_Alignment_with_Hard_Negative_Mining_CVPR_2025_paper.html

title:
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering

abstract:
Medical Visual Question Answering (Med-VQA) is a challenging task that requires a deep understanding of both medical images and textual questions. Although recent works leveraging Medical Vision-Language Pre-training (Med-VLP) have shown strong performance on the Med-VQA task, there is still no unified solution for modality alignment, and the issue of hard negatives remains under-explored. Additionally, commonly used knowledge fusion techniques for Med-VQA may introduce irrelevant information. In this work, we propose a framework to address these challenges through three key contributions: (1) a unified solution for heterogeneous modality alignments across multiple levels, modalities, views, and stages, leveraging methods such as contrastive learning and optimal transport theory; (2) a hard negative mining method that employs soft labels for multi-modality alignments and enforces the hard negative pair discrimination; and (3) a Gated Cross-Attention Module for Med-VQA that integrates the answer vocabulary as prior knowledge and select relevant information from it. Our framework outperforms the previous state-of-the-art on widely used Med-VQA datasets like RAD-VQA, SLAKE, PathVQA and VQA-2019. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zou_Alignment_Mining_and_Fusion_Representation_Alignment_with_Hard_Negative_Mining_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zou_Alignment_Mining_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zou_2025_CVPR,
    author    = {Zou, Yuanhao and Yin, Zhaozheng},
    title     = {Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29623-29633}
}

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


authors: Junsung Park, Hwijeong Lee, Inha Kang, Hyunjung Shim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6690-6699
affiliations: ["Korea Advanced Institute of Science and Technology"]
cities: ["Seoul"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_No_Thing_Nothing_Highlighting_Safety-Critical_Classes_for_Robust_LiDAR_Semantic_CVPR_2025_paper.html

title:
No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather

abstract:
Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In typical driving scenes, "things" categories can be dynamic and associated with higher collision risks, making them crucial for safe navigation and planning. Recognizing the importance of "things" categories, we identify their performance drop as a serious bottleneck in existing approaches. We observed that adverse weather induces degradation of semantic-level features and both corruption of local features, leading to a misprediction of "things" as "stuff". To mitigate these corruptions, we suggests our method, NTN - segmeNt Things for No-accident. To address semantic-level feature corruption, we bind each point feature to its superclass, preventing the misprediction of things classes into visually dissimilar categories. Additionally, to enhance robustness against local corruption caused by adverse weather, we define each LiDAR beam as a local region and propose a regularization term that aligns the clean data with its corrupted counterpart in feature space. NTN achieves state-of-the-art performance with a +2.6 mIoU gain on the SemanticKITTI-to-SemanticSTF benchmark and +7.9 mIoU on the SemanticPOSS-to-SemanticSTF benchmark. Notably, NTN achieves a +4.8 and +7.9 mIoU improvement on "things" classes, respectively, highlighting its effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_No_Thing_Nothing_Highlighting_Safety-Critical_Classes_for_Robust_LiDAR_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_No_Thing_Nothing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Junsung and Lee, Hwijeong and Kang, Inha and Shim, Hyunjung},
    title     = {No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6690-6699}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology", "city": "Seoul", "country": "Republic of Korea"}]


title: Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis
authors: Jeonghwan Park, Niall McLaughlin, Ihsen Alouani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10235-10243
affiliations: ["Queen\u2019s University Belfast"]
cities: ["Belfast"]
countries: ["United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Mind_the_Gap_Detecting_Black-box_Adversarial_Attacks_in_the_Making_CVPR_2025_paper.html

abstract:
Adversarial attacks remain a significant threat that can jeopardize the integrity of Machine Learning (ML) models. In particular, query-based black-box attacks can generate malicious noise without having access to the victim model's architecture, making them practical in real-world contexts. The community has proposed several defenses against adversarial attacks, only to be broken by more advanced and adaptive attack strategies.  In this paper, we propose a framework that detects if an adversarial noise instance is being generated. Unlike existing stateful defenses that detect adversarial noise generation by monitoring the input space, our approach learns adversarial patterns in the input update similarity space. In fact, we propose to observe a new metric called Delta Similarity (DS), which we show it captures more efficiently the adversarial behavior.We evaluate our approach against 8 state-of-the-art attacks, including adaptive attacks, where the adversary is aware of the defense and tries to evade detection. We find that our approach is significantly more robust than existing defenses both in terms of specificity and sensitivity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Mind_the_Gap_Detecting_Black-box_Adversarial_Attacks_in_the_Making_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Mind_the_Gap_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jeonghwan and McLaughlin, Niall and Alouani, Ihsen},
    title     = {Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10235-10243}
}

raw_affiliation:
[{"affiliation": "Queen\u2019s University Belfast", "city": "Belfast", "country": "United Kingdom"}]


title: Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph
authors: Rao Fu, Jianmin Zheng, Liang Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16932-16942
affiliations: ["Nanyang Technological University", "Alibaba Group"]
cities: ["Singapore", "Hangzhou"]
countries: ["Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Consistent_Normal_Orientation_for_3D_Point_Clouds_via_Least_Squares_CVPR_2025_paper.html

abstract:
The orientation of surface normals in 3D point cloud is a fundamental problem in computer vision and graphics. Determining a globally consistent orientation solely from the point cloud is however challenging due to the global scope of the problem and the discrete nature of point cloud, particularly in the presence of noise, outliers, holes, thin structures, and complex topologies. This paper presents an efficient, robust, and global algorithm for generating consistent normal orientation of a dense 3D point cloud. The basic idea is to transform the original binary normal orientation problem to finding a relaxed sign field on a Delaunay graph, which can be achieved by solving a sparse linear system. The Delaunay graph is constructed by triangulating a level set of an implicit function defined from the input point cloud. The shape diameter function is estimated to serve as a prior for determining an appropriate level value such that the level set implicitly defines the inner and outer shells enclosing the input point clouds. As such, our algorithm leverages the strengths of the shape diameter function, Delaunay triangulation, and the least-square techniques, making the underlying processes take both geometry and topology into consideration, and thus provides an efficient and robust solution for handling point clouds with complicated geometry and topology. Extensive experiments on various shapes with noise and outliers confirm  the effectiveness and robustness of our algorithm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Consistent_Normal_Orientation_for_3D_Point_Clouds_via_Least_Squares_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Consistent_Normal_Orientation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Rao and Zheng, Jianmin and Yu, Liang},
    title     = {Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16932-16942}
}

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


title: GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction
authors: Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie Zhou, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6772-6781
affiliations: ["Department of Automation, Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zuo_GaussianWorld_Gaussian_World_Model_for_Streaming_3D_Occupancy_Prediction_CVPR_2025_paper.html

abstract:
3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and ignore the strong prior provided by the evolution of 3D scenes (e.g., only dynamic objects move). In this paper, we propose a world-modelbased framework to exploit the scene evolution for perception. We reformulate 3D occupancy prediction as a 4D occupancy forecasting problem conditioned on the current sensor input. We decompose the scene evolution into three factors: 1) ego motion alignment of static scenes; 2) local movements of dynamic objects; and 3) completion of newly-observed scenes. We then employ a Gaussian world model (GaussianWorld) to explicitly exploit these priors and infer the scene evolution in the 3D Gaussian space considering the current RGB observation. We evaluate the effectiveness of our framework on the widely used nuScenes dataset. Our GaussianWorld improves the performance of the single-frame counterpart by over 2% in mIoU without introducing additional computations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zuo_GaussianWorld_Gaussian_World_Model_for_Streaming_3D_Occupancy_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zuo_GaussianWorld_Gaussian_World_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zuo_2025_CVPR,
    author    = {Zuo, Sicheng and Zheng, Wenzhao and Huang, Yuanhui and Zhou, Jie and Lu, Jiwen},
    title     = {GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6772-6781}
}

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


title: ICP: Immediate Compensation Pruning for Mid-to-high Sparsity
authors: Xin Luo, Xueming Fu, Zihang Jiang, S. Kevin Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9487-9496
cities: ["Hefei", "Suzhou", "Beijing", "Hefei"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_ICP_Immediate_Compensation_Pruning_for_Mid-to-high_Sparsity_CVPR_2025_paper.html

abstract:
The increasing adoption of large-scale models under 7 billion parameters in both language and vision domains enables inference tasks on a single consumer-grade GPU but makes fine-tuning models of this scale, especially 7B models, challenging. This limits the applicability of pruning methods that require full fine-tuning. Meanwhile, pruning methods that do not require fine-tuning perform well at low sparsity levels (10%-50%) but struggle at mid-to-high sparsity levels (50%-70%), where the error behaves equivalently to that of semi-structured pruning. To address these issues, this paper introduces ICP, which finds a balance between full fine-tuning and zero fine-tuning. First, Sparsity Rearrange is used to reorganize the predefined sparsity levels, followed by Block-wise Compensate Pruning, which alternates pruning and compensation on the model's backbone, fully utilizing inference results while avoiding full model fine-tuning. Experiments show that ICP improves performance at mid-to-high sparsity levels compared to baselines, with only a slight increase in pruning time and no additional peak memory overhead.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_ICP_Immediate_Compensation_Pruning_for_Mid-to-high_Sparsity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_ICP_Immediate_Compensation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Xin and Fu, Xueming and Jiang, Zihang and Zhou, S. Kevin},
    title     = {ICP: Immediate Compensation Pruning for Mid-to-high Sparsity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9487-9496}
}

raw_affiliation:
[{"affiliation": "School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC", "city": "Hefei", "country": "China"}, {"affiliation": "MIRACLE Center, Suzhou Institute for Advance Research, USTC", "city": "Suzhou", "country": "China"}, {"affiliation": "Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Precision and Intelligent Chemistry, USTC", "city": "Hefei", "country": "China"}]

affiliations:
["School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC", "MIRACLE Center, Suzhou Institute for Advance Research, USTC", "Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS", "State Key Laboratory of Precision and Intelligent Chemistry, USTC"]


title: VinaBench: Benchmark for Faithful and Consistent Visual Narratives
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2870-2879
affiliations: ["EPFL", "Sony Group Corporation", "Carnegie Mellon University"]
cities: ["Lausanne", "Tokyo", "Pittsburgh"]
countries: ["Switzerland", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_VinaBench_Benchmark_for_Faithful_and_Consistent_Visual_Narratives_CVPR_2025_paper.html

authors:
Silin Gao, Sheryl Mathew, Li Mi, Sepideh Mamooler, Mengjie Zhao, Hiromi Wakaki, Yuki Mitsufuji, Syrielle Montariol, Antoine Bosselut

abstract:
Visual narrative generation transforms textual narratives into sequences of images illustrating the content of the text. However, generating visual narratives that are faithful to the input text and self-consistent across generated images remains an open challenge, due to the lack of knowledge constraints used for planning the stories. In this work, we propose a new benchmark, VinaBench, to address this challenge. Our benchmark annotates the underlying commonsense and discourse constraints in visual narrative samples, offering systematic scaffolds for learning the implicit strategies of visual storytelling. Based on the incorporated narrative constraints, we further propose novel metrics to closely evaluate the consistency of generated narrative images and the alignment of generations with the input textual narrative. Our results across three generative vision models demonstrate that learning with VinaBench's knowledge constraints effectively improves the faithfulness and cohesion of generated visual narratives.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_VinaBench_Benchmark_for_Faithful_and_Consistent_Visual_Narratives_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_VinaBench_Benchmark_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Silin and Mathew, Sheryl and Mi, Li and Mamooler, Sepideh and Zhao, Mengjie and Wakaki, Hiromi and Mitsufuji, Yuki and Montariol, Syrielle and Bosselut, Antoine},
    title     = {VinaBench: Benchmark for Faithful and Consistent Visual Narratives},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2870-2879}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Sony Group Corporation", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: ATA: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18335-18345
affiliations: ["Shanghai Jiao Tong University", "Tencent"]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_ATA_Adaptive_Transformation_Agent_for_Text-Guided_Subject-Position_Variable_Background_Inpainting_CVPR_2025_paper.html

authors:
Yizhe Tang, Zhimin Sun, Yuzhen Du, Ran Yi, Guangben Lu, Teng Hu, Luying Li, Lizhuang Ma, Fangyuan Zou

abstract:
Image inpainting aims to fill the missing region of an image.Recently, there has been a surge of interest in foreground-conditioned background inpainting, a sub-task that fills the background of an image while the foreground subject and associated text prompt are provided.Existing background inpainting methods typically strictly preserve the subject's original position from the source image,resulting in inconsistencies between the subject and the generated background.To address this challenge, we propose a new task, the "Text-Guided Subject-Position Variable Background Inpainting", which aims to dynamically adjust the subject position to achieve a harmonious relationship between the subject andthe inpainted background, and propose the Adaptive Transformation Agent (A^\text T A) for this task.Firstly, we design a PosAgent Block that adaptively predicts an appropriate displacement based on given features to achieve variable subject-position.Secondly, we design the Reverse Displacement Transform (RDT) module, which arranges multiple PosAgent blocks in a reverse structure, to transform hierarchical feature maps from deep to shallow based on semantic information.Thirdly, we equip A^\text T A with a Position Switch Embedding to control whether the subject's position in the generated image is adaptively predicted or fixed.Extensive comparative experiments validate the effectiveness of our A^\text T A approach, which not only demonstrates superior inpainting capabilities in subject-position variable inpainting, but also ensures good performance on subject-position fixed inpainting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_ATA_Adaptive_Transformation_Agent_for_Text-Guided_Subject-Position_Variable_Background_Inpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_ATA_Adaptive_Transformation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yizhe and Sun, Zhimin and Du, Yuzhen and Yi, Ran and Lu, Guangben and Hu, Teng and Li, Luying and Ma, Lizhuang and Zou, Fangyuan},
    title     = {ATA: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18335-18345}
}

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


title: Optimizing for the Shortest Path in Denoising Diffusion Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18021-18030
cities: ["Beijing", "Beijing", "Shenzhen", "Chengdu"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Optimizing_for_the_Shortest_Path_in_Denoising_Diffusion_Model_CVPR_2025_paper.html

authors:
Ping Chen, Xingpeng Zhang, Zhaoxiang Liu, Huan Hu, Xiang Liu, Kai Wang, Min Wang, Yanlin Qian, Shiguo Lian

abstract:
In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior methods. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://github.com/UnicomAI/ShortDF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Optimizing_for_the_Shortest_Path_in_Denoising_Diffusion_Model_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ping and Zhang, Xingpeng and Liu, Zhaoxiang and Hu, Huan and Liu, Xiang and Wang, Kai and Wang, Min and Qian, Yanlin and Lian, Shiguo},
    title     = {Optimizing for the Shortest Path in Denoising Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18021-18030}
}

raw_affiliation:
[{"affiliation": "Data Science & Artificial Intelligence Research Institute, China Unicom", "city": "Beijing", "country": "China"}, {"affiliation": "Unicom Data Intelligence, China Unicom", "city": "Beijing", "country": "China"}, {"affiliation": "DJI Technology Co.,Ltd.", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science and Software Engineering, Southwest Petroleum University", "city": "Chengdu", "country": "China"}]

affiliations:
["Data Science & Artificial Intelligence Research Institute, China Unicom", "Unicom Data Intelligence, China Unicom", "DJI Technology Co.,Ltd.", "School of Computer Science and Software Engineering, Southwest Petroleum University"]


authors: Yuanchen Wu, Lu Zhang, Hang Yao, Junlong Du, Ke Yan, Shouhong Ding, Yunsheng Wu, Xiaoqiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14646-14656
affiliations: ["School of Computer Engineering & Science, Shanghai University", "Tencent YouTu Lab"]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Antidote_A_Unified_Framework_for_Mitigating_LVLM_Hallucinations_in_Counterfactual_CVPR_2025_paper.html

title:
Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception

abstract:
Large Vision-Language Models (LVLMs) have achieved impressive results across various cross-modal tasks. However, hallucinations, i.e., the models generating counterfactual responses, remain a challenge. Though recent studies have attempted to alleviate object perception hallucinations, they focus on the models' response generation, and overlook the task question itself. This paper discusses the vulnerability of LVLMs in solving counterfactual presupposition questions (CPQs), where the models are prone to accept the presuppositions of counterfactual objects and produce severe hallucinatory responses. To this end, we introduce "Antidote", a unified, synthetic data-driven post-training framework for mitigating both types of hallucination above. It leverages synthetic data to incorporate factual priors into questions to achieve self-correction, and decouples the mitigation process into a preference optimization problem. Furthermore, we construct "CP-Bench", a novel benchmark to evaluate LVLMs' ability to correctly handle CPQs and produce factual responses. Applied to the LLaVA series, Antidote can simultaneously enhance performance on CP-Bench by over 50%, POPE by 1.8-3.3%, and CHAIR & SHR by 30-50%, all without relying on external supervision from stronger LVLMs or human feedback and without introducing noticeable catastrophic forgetting issues.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Antidote_A_Unified_Framework_for_Mitigating_LVLM_Hallucinations_in_Counterfactual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Antidote_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yuanchen and Zhang, Lu and Yao, Hang and Du, Junlong and Yan, Ke and Ding, Shouhong and Wu, Yunsheng and Li, Xiaoqiang},
    title     = {Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14646-14656}
}

raw_affiliation:
[{"affiliation": "School of Computer Engineering & Science, Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent YouTu Lab", "city": "Shenzhen", "country": "China"}]


title: Language-Guided Audio-Visual Learning for Long-Term Sports Assessment
authors: Huangbiao Xu, Xiao Ke, Huanqi Wu, Rui Xu, Yuezhou Li, Wenzhong Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23967-23977
cities: ["Fuzhou", "Fuzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Language-Guided_Audio-Visual_Learning_for_Long-Term_Sports_Assessment_CVPR_2025_paper.html

abstract:
Long-term sports assessment is a challenging task in video understanding since it requires judging complex movement variations and action-music coordination. However, there is no direct correlation between the diverse background music and movements in sporting events. Previous works require a large number of model parameters to learn potential associations between actions and music. To address this issue, we propose a language-guided audio-visual learning (MLAVL) framework that models "audio-action-visual" correlations guided by low-cost language modality. In our framework, multidimensional domain-based actions form action knowledge graphs, motivating audio-visual modalities to focus on task-relevant actions. We further design a shared-specific context encoder to integrate deep multimodal semantics, and an audio-visual cross-modal fusion module to evaluate action-music consistency. To match the sport's rules, we then propose a dual-branch prompt-guided grading module to weigh both visual and audio-visual performance. Extensive experiments demonstrate that our approach achieves state-of-the-art on four public long-term sports benchmarks while maintaining low parameters. Our code is available at https://github.com/XuHuangbiao/MLAVL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Language-Guided_Audio-Visual_Learning_for_Long-Term_Sports_Assessment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Language-Guided_Audio-Visual_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Huangbiao and Ke, Xiao and Wu, Huanqi and Xu, Rui and Li, Yuezhou and Guo, Wenzhong},
    title     = {Language-Guided Audio-Visual Learning for Long-Term Sports Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23967-23977}
}

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

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


title: Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20602-20611
affiliations: ["Department of Computer Science and Artificial Intelligence at Dongguk University"]
cities: ["Seoul"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Dynamic_Pseudo_Labeling_via_Gradient_Cutting_for_High-Low_Entropy_Exploration_CVPR_2025_paper.html

authors:
Jae Hyeon Park, Joo Hyeon Jeon, Jae Yun Lee, Sangyeon Ahn, Min Hee Cha, Min Geol Kim, Hyeok Nam, Sung In Cho

abstract:
This study addresses the limitations of existing dynamic pseudo-labeling (DPL) techniques, which often utilize static or dynamic thresholds for confident sample selection. The existing methods fail to capture the non-linear relationship between task accuracy and model confidence, particularly in the context of overconfidence. This can limit the model's learning opportunities for high entropy samples that significantly influence a model's generalization ability. To solve this, we propose a novel gradient pass-based DPL technique that incorporates the high-entropy samples, which are typically overlooked. Our approach introduces two classifiers--low gradient pass (LGP) and high gradient pass (HGP)--to derive over- and under-confident dynamic thresholds that indicate the class-wise overconfidence acceleration, respectively. By combining the under- and over-confident states from the GP classifiers, we create a more adaptive and accurate PL method. Our main contributions highlight the importance of considering both low and high-confidence samples in enhancing the model's robustness and generalization for improved PL performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Dynamic_Pseudo_Labeling_via_Gradient_Cutting_for_High-Low_Entropy_Exploration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Dynamic_Pseudo_Labeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jae Hyeon and Jeon, Joo Hyeon and Lee, Jae Yun and Ahn, Sangyeon and Cha, Min Hee and Kim, Min Geol and Nam, Hyeok and Cho, Sung In},
    title     = {Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20602-20611}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Artificial Intelligence at Dongguk University", "city": "Seoul", "country": "Republic of Korea"}]


title: VODiff: Controlling Object Visibility Order in Text-to-Image Generation
authors: Dong Liang, Jinyuan Jia, Yuhao Liu, Zhanghan Ke, Hongbo Fu, Rynson W. H. Lau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18379-18389
affiliations: ["Tongji University", "City University of Hong Kong", "HKUST(GZ)", "HKUST"]
cities: ["Shanghai", "Hong Kong", "Guangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_VODiff_Controlling_Object_Visibility_Order_in_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Recent advancements in diffusion models have significantly enhanced the performance of text-to-image models in image synthesis. To enable control over the the spatial locations of the generated objects,diffusion-based methods typically utilizeobject layout as an auxiliary input. However, we observe that this approach treats all objects as being on the same layer and neglect their visibility order, leading to the synthesis of overlapping objects with incorrect occlusions.To address this limitation, we introduce in this paper a new training-free framework that considers object visibility order explicitly and allows users to place overlapping objects in a stack of layers. Our framework consists of two visibility-based designs. First, we propose a novel Sequential Denoising Process (SDP) to divide the whole image generation into multiple stages for different objects, each stage primarily focuses on an object. Second, we propose a novel Visibility-Order-Aware (VOA) Loss to transform the layout and occlusion constraints into an attention map optimization process to improve the accuracy of synthesizing object occlusions in complex scenes. By merging these two novel components, our framework, dubbed VODiff, enables the generation of photorealistic images that satisfy user-specified spatial constraints and object occlusion relationships. In addition, we introduce VOBench, a diverse benchmark dataset containing 200 curated samples, each with a reference image, text prompts, object visibility orders and layout maps. We conduct extensive evaluations on this dataset to demonstrate the superiority of our approach.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_VODiff_Controlling_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Dong and Jia, Jinyuan and Liu, Yuhao and Ke, Zhanghan and Fu, Hongbo and Lau, Rynson W. H.},
    title     = {VODiff: Controlling Object Visibility Order in Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18379-18389}
}

raw_affiliation:
[{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]


title: Dual Diffusion for Unified Image Generation and Understanding
authors: Zijie Li, Henry Li, Yichun Shi, Amir Barati Farimani, Yuval Kluger, Linjie Yang, Peng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2779-2790
affiliations: ["Carnegie Mellon University", "Yale University", "ByteDance Seed"]
cities: ["Pittsburgh", "New Haven", "Beijing"]
countries: ["USA", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Dual_Diffusion_for_Unified_Image_Generation_and_Understanding_CVPR_2025_paper.html

abstract:
Diffusion models have gained tremendous success in text-to-image generation, yet still struggle with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities. Inspired by the multimodal diffusion transformer (MM-DiT) and recent advances in discrete diffusion language modeling, we leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly under a single loss function, which is back-propagated through both branches of the diffusion transformer. The resulting model is highly flexible and capable of a wide range of tasks including image generation, captioning, and visual question answering. Our model attained competitive performance compared to recent unified image understanding and generation models, demonstrating the potential of multimodal diffusion modeling as a promising alternative to autoregressive next-token prediction models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Dual_Diffusion_for_Unified_Image_Generation_and_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Dual_Diffusion_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zijie and Li, Henry and Shi, Yichun and Farimani, Amir Barati and Kluger, Yuval and Yang, Linjie and Wang, Peng},
    title     = {Dual Diffusion for Unified Image Generation and Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2779-2790}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Yale University", "city": "New Haven", "country": "USA"}, {"affiliation": "ByteDance Seed", "city": "Beijing", "country": "China"}]


authors: Silin Cheng, Yang Liu, Xinwei He, Sebastien Ourselin, Lei Tan, Gen Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9175-9185
cities: ["Hong Kong", "London", "Wuhan", "Singapore", "Shanghai"]
countries: ["China", "United Kingdom", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_WeakMCN_Multi-task_Collaborative_Network_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2025_paper.html

title:
WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation

abstract:
Weakly supervised referring expression comprehension (WREC) and segmentation (WRES) aim to learn object grounding based on a given expression using weak supervision signals like image-text pairs. While these tasks have traditionally been modeled separately, we argue that they can benefit from joint learning in a multi-task framework. To this end, we propose WeakMCN, a novel multi-task collaborative network that effectively combines WREC and WRES with a dual-branch architecture. Specifically, the WREC branch is formulated as anchor-based contrastive learning, which also acts as a teacher to supervise the WRES branch. In WeakMCN, we propose two innovative designs to facilitate multi-task collaboration, namely Dynamic Visual Feature Enhancement (DVFE) and Collaborative Consistency Module (CCM). DVFE dynamically combines various pre-trained visual knowledge to meet different task requirements, while CCM promotes cross-task consistency from the perspective of optimization. Extensive experimental results on three popular REC and RES benchmarks, i.e., RefCOCO, RefCOCO+, and RefCOCOg, consistently demonstrate performance gains of WeakMCN over state-of-the-art single-task alternatives, e.g., up to 3.91% and 13.11% on RefCOCO for WREC and WRES tasks, respectively. Furthermore, experiments also validate the strong generalization ability of WeakMCN in both semi-supervised REC and RES settings against existing methods, e.g., +8.94% for semi-REC and +7.71% for semi-RES on 1% RefCOCO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_WeakMCN_Multi-task_Collaborative_Network_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_WeakMCN_Multi-task_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Silin and Liu, Yang and He, Xinwei and Ourselin, Sebastien and Tan, Lei and Luo, Gen},
    title     = {WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9175-9185}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "Huazhong Agricultural University", "city": "Wuhan", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "OpenGVLab, Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["The University of Hong Kong", "King\u2019s College London", "Huazhong Agricultural University", "National University of Singapore", "OpenGVLab, Shanghai AI Laboratory"]


authors: Jiahao Li, Weijian Ma, Xueyang Li, Yunzhong Lou, Guichun Zhou, Xiangdong Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18563-18573
affiliations: ["School of Computer Science and Technology, Fudan University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CAD-Llama_Leveraging_Large_Language_Models_for_Computer-Aided_Design_Parametric_3D_CVPR_2025_paper.html

title:
CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation

abstract:
Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-specific areas. This study investigates the generation of parametric sequences for computer-aided design (CAD) models using LLMs. This endeavor represents an initial step towards creating parametric 3D shapes with LLMs, as CAD model parameters directly correlate with shapes in three-dimensional space. Despite the formidable generative capacities of LLMs, this task remains challenging, as these models neither encounter parametric sequences during their pretraining phase nor possess direct awareness of 3D structures. To address this, we present CAD-Llama, a framework designed to enhance pretrained LLMs for generating parametric 3D CAD models. Specifically, we develop a hierarchical annotation pipeline and a code-like format to translate parametric 3D CAD command sequences into Structured Parametric CAD Code (SPCC), incorporating hierarchical semantic descriptions. Furthermore, we propose an adaptive pretraining approach utilizing SPCC, followed by an instruction tuning process aligned with CAD-specific guidelines. This methodology aims to equip LLMs with the spatial knowledge inherent in parametric sequences. Experimental results demonstrate that our framework significantly outperforms prior autoregressive methods and existing LLM baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CAD-Llama_Leveraging_Large_Language_Models_for_Computer-Aided_Design_Parametric_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CAD-Llama_Leveraging_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiahao and Ma, Weijian and Li, Xueyang and Lou, Yunzhong and Zhou, Guichun and Zhou, Xiangdong},
    title     = {CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18563-18573}
}

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


title: Leveraging SD Map to Augment HD Map-based Trajectory Prediction
authors: Zhiwei Dong, Ran Ding, Wei Li, Peng Zhang, Guobin Tang, Jia Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17219-17228
affiliations: ["Riemann Lab, 2012 Laboratories, Huawei Technologies."]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Leveraging_SD_Map_to_Augment_HD_Map-based_Trajectory_Prediction_CVPR_2025_paper.html

abstract:
Latest trajectory prediction models in real-world autonomous driving systems often rely on online High-Definition (HD) maps to understand the road environment.However, online HD maps suffer from perception errors and feature redundancy, which hinder the performance of HD map-based trajectory prediction models.To address these issues, we introduce a framework, termed SD map-Augmented Trajectory Prediction (SATP), which leverages Standard-Definition (SD) maps to enhance HD map-based trajectory prediction models.First, we propose an SD-HD fusion approach to leverage SD maps across the diverse range of HD map-based trajectory prediction models. Second, we design a novel AlignNet to align the SD map with the HD map, further improving the effectiveness of SD maps. Experiments on real-world autonomous driving benchmarks demonstrate that SATP not only improves the performance of HD map-based trajectory prediction up to 25% in real-world scenarios using online HD maps but also brings benefits in ideal scenarios with ground-truth HD maps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Leveraging_SD_Map_to_Augment_HD_Map-based_Trajectory_Prediction_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Zhiwei and Ding, Ran and Li, Wei and Zhang, Peng and Tang, Guobin and Guo, Jia},
    title     = {Leveraging SD Map to Augment HD Map-based Trajectory Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17219-17228}
}

raw_affiliation:
[{"affiliation": "Riemann Lab, 2012 Laboratories, Huawei Technologies.", "city": "Shenzhen", "country": "China"}]


title: 4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 875-885
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_4DGC_Rate-Aware_4D_Gaussian_Compression_for_Efficient_Streamable_Free-Viewpoint_Video_CVPR_2025_paper.html

authors:
Qiang Hu, Zihan Zheng, Houqiang Zhong, Sihua Fu, Li Song, Xiaoyun Zhang, Guangtao Zhai, Yanfeng Wang

abstract:
3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences. However, the vast number of Gaussians and their associated attributes poses significant challenges for storage and transmission. Existing methods typically handle dynamic 3DGS representation and compression separately, neglecting motion information and the rate-distortion (RD) trade-off during training, leading to performance degradation and increased model redundancy. To address this gap, we propose 4DGC, a novel rate-aware 4D Gaussian compression framework that  significantly reduces storage size while maintaining superior RD performance for FVV. Specifically, 4DGC introduces a motion-aware dynamic Gaussian representation that utilizes a compact motion grid combined with sparse compensated Gaussians to exploit inter-frame similarities. This representation effectively handles large motions, preserving quality and reducing temporal redundancy. Furthermore, we present an end-to-end compression scheme that employs differentiable quantization and a tiny implicit entropy model to compress the motion grid and compensated Gaussians efficiently. The entire framework is jointly optimized using a rate-distortion trade-off. Extensive experiments demonstrate that 4DGC supports variable bitrates and consistently outperforms existing methods in RD performance across multiple datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_4DGC_Rate-Aware_4D_Gaussian_Compression_for_Efficient_Streamable_Free-Viewpoint_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_4DGC_Rate-Aware_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Qiang and Zheng, Zihan and Zhong, Houqiang and Fu, Sihua and Song, Li and Zhang, Xiaoyun and Zhai, Guangtao and Wang, Yanfeng},
    title     = {4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {875-885}
}

raw_affiliation:
[{"affiliation": "Cooperative Medianet Innovation Center, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Cooperative Medianet Innovation Center, Shanghai Jiao Tong University", "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University", "School of Artificial Intelligence, Shanghai Jiao Tong University"]


title: ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation
authors: Tao Tan, Qiulei Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16829-16838
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_ONDA-Pose_Occlusion-Aware_Neural_Domain_Adaptation_for_Self-Supervised_6D_Object_Pose_CVPR_2025_paper.html

abstract:
Self-supervised 6D object pose estimation has received increasing attention in computer vision recently. Some typical works in literature attempt to translate the synthetic images with object pose labels generated by object CAD models into the real domain, and then use the translated data for training. However, their performance is generally limited, since (i) there still exists a domain gap between the translated images and the real images and (ii) the translated images can not sufficiently reflect occlusions that exist in many real images. To address these problems, we propose an Occlusion-Aware Neural Domain Adaptation method for self-supervised 6D object Pose estimation, called ONDA-Pose. The proposed method comprises three main steps. Firstly, by utilizing both the training real images without pose labels and a CAD model, we explore a CAD-like radiance field for rendering corresponding synthetic images that have similar textures to those generated by the CAD model. Then, a backbone pose estimator trained on the synthetic data is employed to provide initial pose estimations for the synthetic images rendered from the CAD-like radiance field, and the initial object poses are refined by a global object pose refiner to generate pseudo object pose labels. Finally, the backbone pose estimator is further self-supervised as the final pose estimator by jointly utilizing the real images with pseudo object pose labels and the synthetic images rendered from the CAD-like radiance field. Experimental results on three public datasets demonstrate that ONDA-Pose significantly outperforms the comparative state-of-the-art methods in most cases.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_ONDA-Pose_Occlusion-Aware_Neural_Domain_Adaptation_for_Self-Supervised_6D_Object_Pose_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Tao and Dong, Qiulei},
    title     = {ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16829-16838}
}

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

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


title: Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation
authors: Dingcheng Zhen, Shunshun Yin, Shiyang Qin, Hou Yi, Ziwei Zhang, Siyuan Liu, Gan Qi, Ming Tao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21075-21085
raw_affiliation: [{"affiliation": "Shanghai Soulgate Techonolgy Co.tl.", "city": "Shanghai", "country": "China"}]
affiliations: ["Shanghai Soulgate Techonolgy Co.tl."]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhen_Teller_Real-Time_Streaming_Audio-Driven_Portrait_Animation_with_Autoregressive_Motion_Generation_CVPR_2025_paper.html

abstract:
In this work, we introduce the first autoregressive framework for real-time, audio-driven portrait animation, a.k.a, talking head. Beyond the challenge of lengthy animation times, a critical challenge in realistic talking head generation lies in preserving the natural movement of diverse body parts. To this end, we propose Teller, the first streaming audio-driven protrait animation framework with autoregressive motion generation. Specifically, Teller first decomposes facial and body detail animation into two components: Facial Motion Latent Generation (FMLG) based on an autoregressive transfromer, and movement authenticity refinement using a Efficient Temporal Module (ETM).Concretely, FMLG employs a Residual VQ model to map the facial motion latent from the implicit keypoint-based model into discrete motion tokens, which are then temporally sliced with audio embeddings. This enables the AR tranformer to learn real-time, stream-based mappings from audio to motion.Furthermore, Teller incorporate ETM to capture finer motion details. This module ensures the physical consistency of body parts and accessories, such as neck muscles and earrings, improving the realism of these movements.Teller is designed to be efficient, surpassing the inference speed of diffusion-based models (Hallo 20.93s vs. Teller 0.92s for one second video generation), and achieves a real-time streaming performance of up to 25 FPS. Extensive experiments demonstrate that our method outperforms recent audio-driven portrait animation models, especially in small movements, as validated by human evaluations with a significant margin in quality and realism.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhen_Teller_Real-Time_Streaming_Audio-Driven_Portrait_Animation_with_Autoregressive_Motion_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhen_Teller_Real-Time_Streaming_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhen_2025_CVPR,
    author    = {Zhen, Dingcheng and Yin, Shunshun and Qin, Shiyang and Yi, Hou and Zhang, Ziwei and Liu, Siyuan and Qi, Gan and Tao, Ming},
    title     = {Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21075-21085}
}


title: GASP: Gaussian Avatars with Synthetic Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 271-280
affiliations: ["University of Bath", "Microsoft", "Georgia Institute of Technology"]
cities: ["Bath", "Redmond", "Atlanta"]
countries: ["UK", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Saunders_GASP_Gaussian_Avatars_with_Synthetic_Priors_CVPR_2025_paper.html

authors:
Jack Saunders, Charlie Hewitt, Yanan Jian, Marek Kowalski, Tadas Baltrusaitis, Yiye Chen, Darren Cosker, Virginia Estellers, Nicholas Gydé, Vinay P. Namboodiri, Benjamin E. Lundell

abstract:
Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries of quality and rendering efficiency but suffer from two main limitations. Either they require expensive multi-camera rigs to produce avatars with free-view rendering, or they can be trained with a single camera but only rendered at high quality from this fixed viewpoint. An ideal model would be trained using a short monocular video or image from available hardware, such as a webcam, and rendered from any view. To this end, we propose GASP: Gaussian Avatars with Synthetic Priors. To overcome the limitations of existing datasets, we exploit the pixel-perfect nature of synthetic data to train a Gaussian Avatar prior. By fitting this prior model to a single photo or videoand fine-tuning it, we get a high-quality Gaussian Avatar, which supports 360^\circ rendering. Our prior is only required for fitting, not inference, enabling real-time application. Through our method, we obtain high-quality, animatable Avatars from limited data which can be animated and rendered at 70fps on commercial hardware.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Saunders_GASP_Gaussian_Avatars_with_Synthetic_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Saunders_GASP_Gaussian_Avatars_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Saunders_2025_CVPR,
    author    = {Saunders, Jack and Hewitt, Charlie and Jian, Yanan and Kowalski, Marek and Baltrusaitis, Tadas and Chen, Yiye and Cosker, Darren and Estellers, Virginia and Gyd\'e, Nicholas and Namboodiri, Vinay P. and Lundell, Benjamin E.},
    title     = {GASP: Gaussian Avatars with Synthetic Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {271-280}
}

raw_affiliation:
[{"affiliation": "University of Bath", "city": "Bath", "country": "UK"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


authors: Jie Liu, Tiexin Qin, Hui Liu, Yilei Shi, Lichao Mou, Xiao Xiang Zhu, Shiqi Wang, Haoliang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15560-15569
cities: ["Hong Kong", "Wuxi", "Munich"]
countries: ["China", "China", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Q-PART_Quasi-Periodic_Adaptive_Regression_with_Test-time_Training_for_Pediatric_Left_CVPR_2025_paper.html

title:
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression

abstract:
In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this task, they suffer from two significant limitations. Existing TTT works are primarily designed for classification tasks rather than continuous value regression, and they lack mechanisms to handle the quasi-periodic nature of cardiac signals. To tackle these issues, we propose a novel  Quasi-Periodic Adaptive Regression with Test-time Training (Q-PART) framework. In the training stage, the proposed Quasi-Period Network decomposes the echocardiogram into periodic and aperiodic components within latent space by combining parameterized helix trajectories with Neural Controlled Differential Equations. During inference, our framework further employs a variance minimization strategy across image augmentations that simulate common quality issues in echocardiogram acquisition, along with differential adaptation rates for periodic and aperiodic components. Theoretical analysis is provided to demonstrate that our variance minimization objective effectively bounds the regression error under mild conditions. Furthermore, extensive experiments across three pediatric age groups demonstrate that Q-PART not only significantly outperforms existing approaches in pediatric LVEF prediction, but also exhibits strong clinical screening capability with high mAUROC scores (up to 0.9747) and maintains gender-fair performance across all metrics, validating its robustness and practical utility in pediatric echocardiography analysis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Q-PART_Quasi-Periodic_Adaptive_Regression_with_Test-time_Training_for_Pediatric_Left_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Q-PART_Quasi-Periodic_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jie and Qin, Tiexin and Liu, Hui and Shi, Yilei and Mou, Lichao and Zhu, Xiao Xiang and Wang, Shiqi and Li, Haoliang},
    title     = {Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15560-15569}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "MedAI Technology (Wuxi) Co. Ltd.", "city": "Wuxi", "country": "China"}, {"affiliation": "Technische Universit\n\u00a8at M\n\u00a8unchen", "city": "Munich", "country": "Germany"}]

affiliations:
["City University of Hong Kong", "MedAI Technology (Wuxi) Co. Ltd.", "Technische Universit\n\u00a8at M\n\u00a8unchen"]


title: Composing Parts for Expressive Object Generation
authors: Harsh Rangwani, Aishwarya Agarwal, Kuldeep Kulkarni, R. Venkatesh Babu, Srikrishna Karanam
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13209-13219
affiliations: ["Adobe Research", "Indian Institute of Science"]
cities: ["San Jose", "Bangalore"]
countries: ["USA", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rangwani_Composing_Parts_for_Expressive_Object_Generation_CVPR_2025_paper.html

abstract:
Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, like Stable Diffusion, cannot handle fine-grained part-level attributes in the text prompts. Specifically, when additional attribute details are added to the base text prompt, these text-to-image models either generate an image vastly different from the image generated from the base prompt or ignore the attribute details. To mitigate these issues, we introduce PartComposer, a training-free method that enables image generation based on fine-grained part-level attributes specified for objects in the base text prompt. This allows more control for artists and enables novel object compositions by combining distinctive object parts. PartComposer first localizes object parts by denoising the object region from a specific diffusion process. This enables each part token to be localized to the right region. After obtaining part masks, we run a localized diffusion process in each part region based on fine-grained part attributes and combine them to produce the final image. All stages of PartComposer are based on repurposing a pre-trained diffusion model, which enables it to generalize across domains. We demonstrate the effectiveness of part-level control provided by PartComposer through qualitative visual examples and quantitative comparisons with contemporary baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rangwani_Composing_Parts_for_Expressive_Object_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rangwani_Composing_Parts_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rangwani_2025_CVPR,
    author    = {Rangwani, Harsh and Agarwal, Aishwarya and Kulkarni, Kuldeep and Babu, R. Venkatesh and Karanam, Srikrishna},
    title     = {Composing Parts for Expressive Object Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13209-13219}
}

raw_affiliation:
[{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Indian Institute of Science", "city": "Bangalore", "country": "India"}]


authors: Dongkun Zhang, Jiaming Liang, Ke Guo, Sha Lu, Qi Wang, Rong Xiong, Zhenwei Miao, Yue Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17239-17248
affiliations: ["Zhejiang University", "Cainiao Network"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_CarPlanner_Consistent_Auto-regressive_Trajectory_Planning_for_Large-Scale_Reinforcement_Learning_in_CVPR_2025_paper.html

title:
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-Scale Reinforcement Learning in Autonomous Driving

abstract:
Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing large-scale, real-world driving scenarios.In this paper, we introduce CarPlanner, a Consistent auto-regressive Planner that uses RL to generate multi-modal trajectories. The auto-regressive structure enables efficient large-scale RL training, while the incorporation of consistency ensures stable policy learning by maintaining coherent temporal consistency across time steps. Moreover, CarPlanner employs a generation-selection framework with an expert-guided reward function and an invariant-view module, simplifying RL training and enhancing policy performance.Extensive analysis demonstrates that our proposed RL framework effectively addresses the challenges of training efficiency and performance enhancement, positioning CarPlanner as a promising solution for trajectory planning in autonomous driving.To the best of our knowledge, we are the first to demonstrate that the RL-based planner can surpass both IL- and rule-based state-of-the-arts (SOTAs) on the challenging large-scale real-world dataset nuPlan. Our proposed CarPlanner surpasses  RL-, IL-, and rule-based SOTA approaches within this demanding dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_CarPlanner_Consistent_Auto-regressive_Trajectory_Planning_for_Large-Scale_Reinforcement_Learning_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_CarPlanner_Consistent_Auto-regressive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Dongkun and Liang, Jiaming and Guo, Ke and Lu, Sha and Wang, Qi and Xiong, Rong and Miao, Zhenwei and Wang, Yue},
    title     = {CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-Scale Reinforcement Learning in Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17239-17248}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Cainiao Network", "city": "Hangzhou", "country": "China"}]


title: Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation
authors: Yiming Qin, Zhu Xu, Yang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18521-18530
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Apply_Hierarchical-Chain-of-Generation_to_Complex_Attributes_Text-to-3D_Generation_CVPR_2025_paper.html

abstract:
Recent text-to-3D generation models have demonstrated remarkable abilities in producing high-quality 3D assets. Despite their great advancements, current models struggle to generate satisfying 3D objects with complex attributes. The difficulty for such complex attributes 3D generation arises from two aspects: (1) existing text-to-3D approaches typically lift text-to-image models to extract semantics via text encoders, while the text encoder exhibits limited comprehension ability for long descriptions, leading to deviated cross-attention focus, subsequently wrong attribute binding in generated results. (2) Objects with complex attributes often exhibit occlusion relationships between different parts, which demands a reasonable generation order as well as explicit disentanglement of different parts to enable structural coherent and attribute following results. Though some works introduce manual efforts to alleviate the above issues, their quality is unstable and highly reliant on manual information. To tackle above problems, we propose a automated method Hierarchical-Chain-of-Generation (HCoG).  It leverages a large language model to analyze the long description, decomposes it into several blocks representing different object parts, and organizes an optimal generation order from in to out according to the occlusion relationship between parts, turning the whole generation process into a hierarchical chain. For optimization within each block, we first generate the necessary components coarsely, then bind their attributes precisely by target region localization and corresponding 3D Gaussian kernel optimization. For optimization between blocks, we introduce Gaussian Extension and Label Elimination to seamlessly generate new parts by extending new Gaussian kernels, re-assigning semantic labels, and eliminating unnecessary kernels, ensuring that only relevant parts are added without disrupting previously optimized parts. Experiments validate HCoG's effectiveness in handling complex attributes 3D assets and witnesses high-quality results. The code is available at https://github.com/Wakals/GASCOL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Apply_Hierarchical-Chain-of-Generation_to_Complex_Attributes_Text-to-3D_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Yiming and Xu, Zhu and Liu, Yang},
    title     = {Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18521-18530}
}

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


title: PersonaHOI: Effortlessly Improving Face Personalization in Human-Object Interaction Generation
authors: Xinting Hu, Haoran Wang, Jan Eric Lenssen, Bernt Schiele
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23775-23784
affiliations: ["Max Planck Institute for Informatics, Saarland Informatics Campus"]
cities: ["Saarbr\u00fccken"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_PersonaHOI_Effortlessly_Improving_Face_Personalization_in_Human-Object_Interaction_Generation_CVPR_2025_paper.html

abstract:
We introduce PersonaHOI, a training- and tuning-free framework that fuses a general StableDiffusion model with a personalized face diffusion (PFD) model to generate identity-consistent human-object interaction (HOI) images. While existing PFD models have advanced significantly, they often overemphasize facial features at the expense of full-body coherence, PersonaHOI introduces an additional StableDiffusion (SD) branch guided by HOI-oriented text inputs. By incorporating cross-attention constraints in the PFD branch and spatial merging at both latent and residual levels, PersonaHOI preserves personalized facial details while ensuring interactive non-facial regions.   Experiments, validated by a novel interaction alignment metric, demonstrate the superior realism and scalability of PersonaHOI, establishing a new standard for practical personalized face with HOI generation. Code is available at https://github.com/JoyHuYY1412/PersonaHOI.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_PersonaHOI_Effortlessly_Improving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Xinting and Wang, Haoran and Lenssen, Jan Eric and Schiele, Bernt},
    title     = {PersonaHOI: Effortlessly Improving Face Personalization in Human-Object Interaction Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23775-23784}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics, Saarland Informatics Campus", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models
authors: Jinhui Yi, Syed Talal Wasim, Yanan Luo, Muzammal Naseer, Juergen Gall
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24119-24128
cities: ["Bonn", "Bonn", "Abu Dhabi"]
countries: ["Germany", "Germany", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yi_Video-Panda_Parameter-efficient_Alignment_for_Encoder-free_Video-Language_Models_CVPR_2025_paper.html

abstract:
We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current video-language models typically rely on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B parameters), creating a substantial computational burden when processing multi-frame videos. Our method introduces a novel Spatio-Temporal Alignment Block (STAB) that directly processes video inputs without requiring pre-trained encoders while using only 45M parameters for visual processing - at least a 6.5x reduction compared to traditional approaches. The STAB architecture combines Local Spatio-Temporal Encoding for fine-grained feature extraction, efficient spatial downsampling through learned attention and separate mechanisms for modeling frame-level and video-level relationships. Our model achieves comparable or superior performance to encoder-based approaches for open-ended video question answering on standard benchmarks. The fine-grained video question-answering evaluation demonstrates our model's effectiveness, outperforming the encoder-based approaches Video-ChatGPT and Video-LLaVA in key aspects like correctness and temporal understanding. Extensive ablation studies validate our architectural choices and demonstrate the effectiveness of our spatio-temporal modeling approach while achieving 3-4x faster processing speeds than previous methods. Code is available at https://jh-yi.github.io/Video-Panda.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yi_Video-Panda_Parameter-efficient_Alignment_for_Encoder-free_Video-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yi_Video-Panda_Parameter-efficient_Alignment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yi_2025_CVPR,
    author    = {Yi, Jinhui and Wasim, Syed Talal and Luo, Yanan and Naseer, Muzammal and Gall, Juergen},
    title     = {Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24119-24128}
}

raw_affiliation:
[{"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Lamarr Institute for Machine Learning and Artificial Intelligence", "city": "Bonn", "country": "Germany"}, {"affiliation": "Khalifa University", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["University of Bonn", "Lamarr Institute for Machine Learning and Artificial Intelligence", "Khalifa University"]


title: COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation
authors: Fanding Huang, Jingyan Jiang, Qinting Jiang, Hebei Li, Faisal Nadeem Khan, Zhi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9772-9781
cities: ["Shenzhen", "Shenzhen", "Hefei"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_COSMIC_Clique-Oriented_Semantic_Multi-space_Integration_for_Robust_CLIP_Test-Time_Adaptation_CVPR_2025_paper.html

abstract:
Recent vision-language models (VLMs) face significant challenges in test-time adaptation to novel domains. While cache-based methods show promise by leveraging historical information, they struggle with both caching unreliable feature-label pairs and indiscriminately using single-class information during querying, significantly compromising adaptation accuracy. To address these limitations, we propose COSMIC (\underline C lique-\underline O riented \underline S emantic \underline M ulti-space \underline I ntegration for \underline C LIP), a robust test-time adaptation framework that enhances adaptability through multi-granular, cross-modal semantic caching and graph-based querying mechanisms. Our framework introduces two key innovations: Dual Semantics Graph (DSG) and Clique Guided Hyper-class (CGH). The Dual Semantics Graph constructs complementary semantic spaces by incorporating textual features, coarse-grained CLIP features, and fine-grained DINOv2 features to capture rich semantic relationships. Building upon these dual graphs, the Clique Guided Hyper-class component leverages structured class relationships to enhance prediction robustness through correlated class selection. Extensive experiments demonstrate COSMIC's superior performance across multiple benchmarks, achieving significant improvements over state-of-the-art methods: 15.81% gain on out-of-distribution tasks and 5.33% on cross-domain generation with CLIP RN-50.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_COSMIC_Clique-Oriented_Semantic_Multi-space_Integration_for_Robust_CLIP_Test-Time_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_COSMIC_Clique-Oriented_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Fanding and Jiang, Jingyan and Jiang, Qinting and Li, Hebei and Khan, Faisal Nadeem and Wang, Zhi},
    title     = {COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9772-9781}
}

raw_affiliation:
[{"affiliation": "Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen Technology University", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["Shenzhen International Graduate School, Tsinghua University", "Shenzhen Technology University", "University of Science and Technology of China"]


title: MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models
authors: Yifan Liu, Keyu Fan, Weihao Yu, Chenxin Li, Hao Lu, Yixuan Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21570-21579
affiliations: ["The Chinese University of Hong Kong", "Tsinghua University (Shenzhen)", "HKUST (Guangzhou)"]
cities: ["Hong Kong", "Shenzhen", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MonoSplat_Generalizable_3D_Gaussian_Splatting_from_Monocular_Depth_Foundation_Models_CVPR_2025_paper.html

abstract:
Recent advances in generalizable 3D Gaussian Splatting have demonstrated promising results in real-time high-fidelity rendering without per-scene optimization, yet existing approaches still struggle to handle unfamiliar visual content during inference on novel scenes due to limited generalizability. To address this challenge, we introduce MonoSplat, a novel framework that leverages rich visual priors from pre-trained monocular depth foundation models for robust Gaussian reconstruction. Our approach consists of two key components: a Mono-Multi Feature Adapter that transforms monocular features into multi-view representations, coupled with an Integrated Gaussian Prediction module that effectively fuses both feature types for precise Gaussian generation. Through the Adapter's lightweight attention mechanism, features are seamlessly aligned and aggregated across views while preserving valuable monocular priors, enabling the Prediction module to generate Gaussian primitives with accurate geometry and appearance. Through extensive experiments on diverse real-world datasets, we convincingly demonstrate that MonoSplat achieves superior reconstruction quality and generalization capability compared to existing methods while maintaining computational efficiency with minimal trainable parameters. Codes are available at \href https://github.com/CUHK-AIM-Group/MonoSplat  https://github.com/CUHK-AIM-Group/MonoSplat .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MonoSplat_Generalizable_3D_Gaussian_Splatting_from_Monocular_Depth_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MonoSplat_Generalizable_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yifan and Fan, Keyu and Yu, Weihao and Li, Chenxin and Lu, Hao and Yuan, Yixuan},
    title     = {MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21570-21579}
}

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


authors: Ting Liu, Siyuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29634-29643
cities: ["Xi'an", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid_Global-Local_Representation_with_Augmented_Spatial_Guidance_for_Zero-Shot_Referring_CVPR_2025_paper.html

title:
Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentation

abstract:
Recent advances in zero-shot referring image segmentation (RIS), driven by models such as the Segment Anything Model (SAM) and CLIP, have made substantial progress in aligning visual and textual information. Despite these successes, the extraction of precise and high-quality mask region representations remains a critical challenge, limiting the full potential of RIS tasks. In this paper, we introduce a training-free, hybrid global-local feature extraction approach that integrates detailed mask-specific features with contextual information from the surrounding area, enhancing mask region representation. To further strengthen alignment between mask regions and referring expressions, we propose a spatial guidance augmentation strategy that improves spatial coherence, which is essential for accurately localizing described areas. By incorporating multiple spatial cues, this approach facilitates more robust and precise referring segmentation. Extensive experiments on standard RIS benchmarks demonstrate that our method significantly outperforms existing zero-shot referring segmentation models, achieving substantial performance gains. We believe our approach advances RIS tasks and establishes a versatile framework for region-text alignment, offering broader implications for cross-modal understanding and interaction. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid_Global-Local_Representation_with_Augmented_Spatial_Guidance_for_Zero-Shot_Referring_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Ting and Li, Siyuan},
    title     = {Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29634-29643}
}

raw_affiliation:
[{"affiliation": "ASGO, School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shenzhen Research Institute of Northwestern Polytechnical University", "city": "Shenzhen", "country": "China"}]

affiliations:
["ASGO, School of Computer Science, Northwestern Polytechnical University", "Shenzhen Research Institute of Northwestern Polytechnical University"]


title: SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving
authors: Xuesong Chen, Linjiang Huang, Tao Ma, Rongyao Fang, Shaoshuai Shi, Hongsheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12068-12077
cities: ["Hong Kong", "Beijing", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SOLVE_Synergy_of_Language-Vision_and_End-to-End_Networks_for_Autonomous_Driving_CVPR_2025_paper.html

abstract:
The integration of Vision-Language Models (VLMs) into autonomous driving systems has shown promise in addressing key challenges such as learning complexity, interpretability, and common-sense reasoning. However, existing approaches often struggle with efficient integration and real-time decision-making due to computational demands. In this paper, we introduce SOLVE, an innovative framework that synergizes VLMs with end-to-end (E2E) models to enhance autonomous vehicle planning. Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components. We propose a Trajectory Chain-of-Thought (T-CoT) paradigm, which progressively refines trajectory predictions, reducing uncertainty and improving accuracy. By employing a temporal decoupling strategy, SOLVE achieves efficient asynchronous cooperation, aligning high-quality VLM outputs with E2E real-time performance. Evaluated on the nuScenes dataset, our method demonstrates significant improvements in trajectory prediction accuracy, paving the way for more robust and reliable autonomous driving systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SOLVE_Synergy_of_Language-Vision_and_End-to-End_Networks_for_Autonomous_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SOLVE_Synergy_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xuesong and Huang, Linjiang and Ma, Tao and Fang, Rongyao and Shi, Shaoshuai and Li, Hongsheng},
    title     = {SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12068-12077}
}

raw_affiliation:
[{"affiliation": "MMLab, CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Institute of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Voyager Research, Didi Chuxing", "city": "Beijing", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["MMLab, CUHK", "Institute of Artificial Intelligence, Beihang University", "Voyager Research, Didi Chuxing", "CPII under InnoHK"]


title: Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection
authors: Ji Du, Fangwei Hao, Mingyang Yu, Desheng Kong, Jiesheng Wu, Bin Wang, Jing Xu, Ping Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19271-19282
cities: ["Tianjin", "Hong Kong", "Hong Kong"]
countries: ["China", "Hong Kong", "Hong Kong"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_Shift_the_Lens_Environment-Aware_Unsupervised_Camouflaged_Object_Detection_CVPR_2025_paper.html

abstract:
Camouflaged Object Detection (COD) seeks to distinguish objects from their highly similar backgrounds. Existing work has essentially focused on isolating camouflaged objects from the environment, demonstrating ever-improving performance but at the cost of extensive annotations and complex optimizations. In this paper, we diverge from this paradigm and shift the lens to isolating the salient environment from the camouflaged object. We introduce EASE, an Environment-Aware unSupErvised COD framework that identifies the environment by referencing an environment prototype library and detects camouflaged objects by inverting the retrieved environmental features. Specifically, our approach (DiffPro) uses large multimodal models, diffusion models, and vision-foundation models to construct the environment prototype library. To retrieve environments from the library and refrain from confusing foreground and background, we incorporate three retrieval schemes: Kernel Density Estimation-based Adaptive Threshold (KDE-AT), Global-to-Local pixel-level retrieval (G2L), and Self-Retrieval (SR). Our experiments demonstrate significant improvements over current unsupervised methods, with EASE achieving an average gain of over 10% on the COD10K dataset. When integrated with SAM, EASE surpasses prompt-based segmentation approaches and performs competitively with state-of-the-art fully-supervised methods. Code is available at https://github.com/xiaohainku/EASE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_Shift_the_Lens_Environment-Aware_Unsupervised_Camouflaged_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_Shift_the_Lens_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Ji and Hao, Fangwei and Yu, Mingyang and Kong, Desheng and Wu, Jiesheng and Wang, Bin and Xu, Jing and Li, Ping},
    title     = {Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19271-19282}
}

raw_affiliation:
[{"affiliation": "College of Arti\ufb01cial Intelligence, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Department of Computing, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "School of Design, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong"}]

affiliations:
["College of Arti\ufb01cial Intelligence, Nankai University", "Department of Computing, The Hong Kong Polytechnic University", "School of Design, The Hong Kong Polytechnic University"]


title: Probability Density Geodesics in Image Diffusion Latent Space
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27989-27998
affiliations: ["Australian National University", "GE Research"]
cities: ["Canberra", "Niskayuna"]
countries: ["Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Probability_Density_Geodesics_in_Image_Diffusion_Latent_Space_CVPR_2025_paper.html

authors:
Qingtao Yu, Jaskirat Singh, Zhaoyuan Yang, Peter Henry Tu, Jing Zhang, Hongdong Li, Richard Hartley, Dylan Campbell

abstract:
Diffusion models indirectly estimate the probability density over a data space, which can be used to study its structure. In this work, we show that geodesics can be computed in diffusion latent space, where the norm induced by the spatially-varying inner product is inversely proportional to the probability density. In this formulation, a path that traverses a high density (that is, probable) region of image latent space is shorter than the equivalent path through a low density region. We present algorithms for solving the associated initial and boundary value problems and show how to compute the probability density along the path and the geodesic distance between two points. Using these techniques, we analyze how closely video clips approximate geodesics in a pre-trained image diffusion space. Finally, we demonstrate how these techniques can be applied to training-free image sequence interpolation and extrapolation, given a pre-trained image diffusion model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Probability_Density_Geodesics_in_Image_Diffusion_Latent_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Probability_Density_Geodesics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Qingtao and Singh, Jaskirat and Yang, Zhaoyuan and Tu, Peter Henry and Zhang, Jing and Li, Hongdong and Hartley, Richard and Campbell, Dylan},
    title     = {Probability Density Geodesics in Image Diffusion Latent Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27989-27998}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "GE Research", "city": "Niskayuna", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1287-1296
raw_affiliation: [{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]
affiliations: ["Nanjing University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_High-quality_Point_Cloud_Oriented_Normal_Estimation_via_Hybrid_Angular_and_CVPR_2025_paper.html

title:
High-quality Point Cloud Oriented Normal Estimation via Hybrid Angular and Euclidean Distance Encoding

authors:
Yuanqi Li, Jingcheng Huang, Hongshen Wang, Peiyuan Lv, Yansong Liu, Jiuming Zheng, Jie Guo, Yanwen Guo

abstract:
The proliferation of Light Detection and Ranging (LiDAR) technology has facilitated the acquisition of three-dimensional point clouds, which are integral to applications in VR, AR, and Digital Twin. Oriented normals, critical for 3D reconstruction and scene analysis, cannot be directly extracted from scenes using LiDAR due to its operational principles. Previous traditional or learning-based methods are prone to inaccuracies due to uneven distribution and noise due to the dependence on local geometry features. This paper addresses the challenge of estimating oriented point normals by introducing a point cloud normal estimation framework via hybrid angular and Euclidean distance encoding (HAE). Our method overcomes the limitations of local geometric information by combining angular and Euclidean spaces to extract features from both point cloud coordinates and light rays, leading to more accurate normal estimation. The core of our network consists of an angular distance encoding module, which leverages both ray directions and point coordinates for unoriented normal refinement, and a ray feature fusion module for normal orientation, that is robust to noise. We also provide a point cloud dataset with ground truth normals, generated a virtual scanner, which reflects real scanning distributions and noise profiles.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_High-quality_Point_Cloud_Oriented_Normal_Estimation_via_Hybrid_Angular_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_High-quality_Point_Cloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuanqi and Huang, Jingcheng and Wang, Hongshen and Lv, Peiyuan and Liu, Yansong and Zheng, Jiuming and Guo, Jie and Guo, Yanwen},
    title     = {High-quality Point Cloud Oriented Normal Estimation via Hybrid Angular and Euclidean Distance Encoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1287-1296}
}


title: DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion
authors: Wei Wu, Xi Guo, Weixuan Tang, Tingxuan Huang, Chiyu Wang, Chenjing Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17187-17196
affiliations: ["Tsinghua University", "Sensetime Research", "Northeastern University"]
cities: ["Beijing", "Beijing", "Shenyang"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DriveScape_High-Resolution_Driving_Video_Generation_by_Multi-View_Feature_Fusion_CVPR_2025_paper.html

abstract:
Recent advancements in generative models offer promising solutions for synthesizing realistic driving videos, aiding in training autonomous driving perception models. However, existing methods often struggle with high-resolution multi-view generation, mainly due to the significant memory and computational overhead caused by simultaneously inputting multi-view videos into denoising diffusion models.In this paper, we propose a driving video generation framework based on multi-view feature fusion named DriveScape for multi-view 3D condition-guided video generation. We introduce a Bi-Directional Modulated Transformer (BiMoT) module to encode, fuse and inject multi-view features along with various 3D road structures and objects, which enables high-resolution multi-view generation. Consequently, our approach allows precise control over video generation, greatly enhancing realism and providing a robust solution for creating high-quality, multi-view driving videos.Our framework achieves state-of-the-art results on the nuScenes dataset, demonstrating impressive generative quality metrics with an FID score of 8.34 and an FVD score of 76.39, as well as superior performance across various perception tasks. This lays the foundation for more accurate environment simulation in autonomous driving. We plan to make our code and pre-trained model publicly available.Please refer to index.html webpage in the supplementary materials for more visualization results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DriveScape_High-Resolution_Driving_Video_Generation_by_Multi-View_Feature_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_DriveScape_High-Resolution_Driving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Wei and Guo, Xi and Tang, Weixuan and Huang, Tingxuan and Wang, Chiyu and Ding, Chenjing},
    title     = {DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17187-17196}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Sensetime Research", "city": "Beijing", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}]


title: Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights
authors: Ondrej Tybl, Lukas Neumann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14881-14890
cities: ["Prague"]
countries: ["Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tybl_Training-free_Neural_Architecture_Search_through_Variance_of_Knowledge_of_Deep_CVPR_2025_paper.html

abstract:
Deep learning has revolutionized computer vision, but it achieved its tremendous success using deep network architectures which are mostly hand-crafted and therefore likely suboptimal. Neural Architecture Search (NAS) aims to bridge this gap by following a well-defined optimization paradigm which systematically looks for the best architecture, given objective criterion such as maximal classification accuracy. The main limitation of NAS is however its astronomical computational cost, as it typically requires training each candidate network architecture from scratch.In this paper, we aim to alleviate this limitation by proposing a novel training-free proxy for image classification accuracy based on Fisher Information. The proposed proxy has a strong theoretical background in statistics and it allows estimating expected image classification accuracy of a given deep network without training the network, thus significantly reducing computational cost of standard NAS algorithms. Our training-free proxy achieves state-of-the-art results on three public datasets and in two search spaces, both when evaluated using previously proposed metrics, as well as using a new metric that we propose which we demonstrate is more informative for practical NAS applications. The source code is publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tybl_Training-free_Neural_Architecture_Search_through_Variance_of_Knowledge_of_Deep_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tybl_Training-free_Neural_Architecture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tybl_2025_CVPR,
    author    = {Tybl, Ondrej and Neumann, Lukas},
    title     = {Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14881-14890}
}

raw_affiliation:
[{"affiliation": "CMP Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}]

affiliations:
["CMP Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague"]


title: Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond
authors: Guanyao Wu, Haoyu Liu, Hongming Fu, Yichuan Peng, Jinyuan Liu, Xin Fan, Risheng Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17882-17891
cities: ["Dalian", "Dalian"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Every_SAM_Drop_Counts_Embracing_Semantic_Priors_for_Multi-Modality_Image_CVPR_2025_paper.html

abstract:
Multi-modality image fusion, particularly infrared and visible, plays a crucial role in integrating diverse modalities to enhance scene understanding. Although early research prioritized visual quality, preserving fine details and adapting to downstream tasks remains challenging. Recent approaches attempt task-specific design but rarely achieve "The Best of Both Worlds" due to inconsistent optimization goals. To address these issues, we propose a novel method that leverages the semantic knowledge from the Segment Anything Model (SAM) to grow the quality of fusion results and enable downstream task adaptability, namely SAGE. Specifically, we design a Semantic Persistent Attention (SPA) Module that efficiently maintains source information via the persistent repository while extracting high-level semantic priors from SAM. More importantly, to eliminate the impractical dependence on SAM during inference, we introduce a bi-level optimization-driven distillation mechanism with triplet losses, which allow the student network to effectively extract knowledge. Extensive experiments show that our method achieves a balance between high-quality visual results and downstream task adaptability while maintaining practical deployment efficiency. The code is available at https://github.com/RollingPlain/SAGE_IVIF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Every_SAM_Drop_Counts_Embracing_Semantic_Priors_for_Multi-Modality_Image_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Guanyao and Liu, Haoyu and Fu, Hongming and Peng, Yichuan and Liu, Jinyuan and Fan, Xin and Liu, Risheng},
    title     = {Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17882-17891}
}

raw_affiliation:
[{"affiliation": "School of Software Technology, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "School of Mechanical Engineering, Dalian University of Technology", "city": "Dalian", "country": "China"}]

affiliations:
["School of Software Technology, Dalian University of Technology", "School of Mechanical Engineering, Dalian University of Technology"]


title: EgoLife: Towards Egocentric Life Assistant
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28885-28900
cities: ["Singapore", "Singapore", "Beijing", "Beijing", "Madrid", "Brescia", "Milan"]
countries: ["Singapore", "Singapore", "China", "China", "Spain", "Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_EgoLife_Towards_Egocentric_Life_Assistant_CVPR_2025_paper.html

authors:
Jingkang Yang, Shuai Liu, Hongming Guo, Yuhao Dong, Xiamengwei Zhang, Sicheng Zhang, Pengyun Wang, Zitang Zhou, Binzhu Xie, Ziyue Wang, Bei Ouyang, Zhengyu Lin, Marco Cominelli, Zhongang Cai, Bo Li, Yuanhan Zhang, Peiyuan Zhang, Fangzhou Hong, Joerg Widmer, Francesco Gringoli, Lei Yang, Ziwei Liu

abstract:
We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses. To lay the foundation for this assistant, we conducted a comprehensive data collection study where six participants lived together for one week, continuously recording their daily activities - including discussions, shopping, cooking, socializing, and entertainment - using AI glasses for multimodal egocentric video capture, along with synchronized third-person-view video references. This effort resulted in the EgoLife Dataset, a comprehensive 300-hour egocentric, interpersonal, multiview, and multimodal daily life dataset with intensive annotation. Leveraging this dataset, we introduce EgoLifeQA, a suite of long-context, life-oriented question-answering tasks designed to provide meaningful assistance in daily life by addressing practical questions such as recalling past relevant events, monitoring health habits, and offering personalized recommendations. To address the key technical challenges of (1) developing robust visual-audio models for egocentric data, (2) enabling identity recognition, and (3) facilitating long-context question answering over extensive temporal information, we introduce EgoButler, an integrated system comprising EgoGPT and EgoRAG. EgoGPT is an omni-modal model trained on egocentric datasets, achieving state-of-the-art performance on egocentric video understanding. EgoRAG is a retrieval-based component that supports answering ultra-long-context questions. Our experimental studies verify their working mechanisms and reveal critical factors and bottlenecks, guiding future improvements. By releasing our datasets, models, and benchmarks, we aim to stimulate further research in egocentric AI assistants.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_EgoLife_Towards_Egocentric_Life_Assistant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_EgoLife_Towards_Egocentric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jingkang and Liu, Shuai and Guo, Hongming and Dong, Yuhao and Zhang, Xiamengwei and Zhang, Sicheng and Wang, Pengyun and Zhou, Zitang and Xie, Binzhu and Wang, Ziyue and Ouyang, Bei and Lin, Zhengyu and Cominelli, Marco and Cai, Zhongang and Li, Bo and Zhang, Yuanhan and Zhang, Peiyuan and Hong, Fangzhou and Widmer, Joerg and Gringoli, Francesco and Yang, Lei and Liu, Ziwei},
    title     = {EgoLife: Towards Egocentric Life Assistant},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28885-28900}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "LMMs-Lab", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Capital Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "IMDEA Networks", "city": "Madrid", "country": "Spain"}, {"affiliation": "University of Brescia", "city": "Brescia", "country": "Italy"}, {"affiliation": "Politecnico di Milano", "city": "Milan", "country": "Italy"}]

affiliations:
["S-Lab, Nanyang Technological University", "LMMs-Lab", "Beijing University of Posts and Telecommunications", "Capital Normal University", "IMDEA Networks", "University of Brescia", "Politecnico di Milano"]


authors: Fan Xing, Zhuo Tian, Xuefeng Fan, Xiaoyi Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20665-20674
raw_affiliation: [{"affiliation": "Hainan University", "city": "Haikou", "country": "China"}]
affiliations: ["Hainan University"]
cities: ["Haikou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_RAEncoder_A_Label-Free_Reversible_Adversarial_Examples_Encoder_for_Dataset_Intellectual_CVPR_2025_paper.html

title:
RAEncoder: A Label-Free Reversible Adversarial Examples Encoder for Dataset Intellectual Property Protection

abstract:
Reversible Adversarial Examples (RAE) are designed to protect the intellectual property of datasets. Such examples can function as imperceptible adversarial examples to erode the model performance of unauthorized users while allowing authorized users to remove the adversarial perturbations and recover the original samples for normal model training. With the rise of Self-Supervised Learning (SSL), an increasing number of unlabeled datasets and pre-trained encoders are available in the community. However, existing RAE methods not only rely on well-labeled datasets for training Supervised Learning (SL) models but also exhibit poor adversarial transferability when attacking SSL pre-trained encoders. To address these challenges, we propose RAEncoder, the first framework for RAEs without the need for labeled samples. RAEncoder aims to generate universal adversarial perturbations by targeting SSL pre-trained encoders. Unlike traditional RAE approaches, the pre-trained encoder outputs the feature distribution of the protected dataset rather than classification labels, enhancing both the attack success rate and transferability of RAEs. Extensive experiments are conducted on six pre-trained encoders and four SL models, covering aspects such as imperceptibility and transferability. Our results demonstrate that RAEncoder effectively protects unlabeled datasets from malicious infringements. Additional robustness experiments further confirm the security of RAEncoder in practical application scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_RAEncoder_A_Label-Free_Reversible_Adversarial_Examples_Encoder_for_Dataset_Intellectual_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Fan and Tian, Zhuo and Fan, Xuefeng and Zhou, Xiaoyi},
    title     = {RAEncoder: A Label-Free Reversible Adversarial Examples Encoder for Dataset Intellectual Property Protection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20665-20674}
}


title: BrepGiff: Lightweight Generation of Complex B-rep with 3D GAT Diffusion
authors: Hao Guo, Xiaoshui Huang, Hao jiacheng, Yunpeng Bai, Hongping Gan, Yilei Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26587-26596
cities: ["Xi'an", "Shanghai", "Nanchang", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_BrepGiff_Lightweight_Generation_of_Complex_B-rep_with_3D_GAT_Diffusion_CVPR_2025_paper.html

abstract:
Despite advancements in Computer-Aided-Design (CAD) generation, direct generation of complex Boundary Representation (B-rep) CAD models remains challenging. This difficulty arises from the parametric nature of B-rep data, complicating the encoding and generation of its geometric and topological information. To address this, we introduce BrepGiff, a lightweight generation approach for high-quality and complex B-rep model based on 3D Graph Diffusion. First, we transfer B-rep models into 3D graphs representation. Specifically, BrepGiff extracts and integrates topological and geometric features to construct a 3D graph where nodes correspond to face centroids in 3D space, preserving adjacency and degree information. Geometric features are derived by sampling points in the UV domain and extracting face and edge features. Then, BrepGiff applies a Graph Attention Network (GAT) to enforce topological constraints from local to global during the degree-guided diffusion process. With the 3D graph representation and efficient diffusion process, our method significantly reduces the computational cost and improves the quality, thus achieving lightweight generation of complex models. Experiments show that BrepGiff can generate complex B-rep models (>100 faces) using only 2 RTX4090 GPUs, achieving state-of-the-art performance in B-rep generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_BrepGiff_Lightweight_Generation_of_Complex_B-rep_with_3D_GAT_Diffusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Hao and Huang, Xiaoshui and jiacheng, Hao and Bai, Yunpeng and Gan, Hongping and Shi, Yilei},
    title     = {BrepGiff: Lightweight Generation of Complex B-rep with 3D GAT Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26587-26596}
}

raw_affiliation:
[{"affiliation": "Northwest Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanchang University", "city": "Nanchang", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Northwest Polytechnical University", "Shanghai Jiaotong University", "Nanchang University", "National University of Singapore"]


authors: Lintong Zhang, Kang Yin, Seong-Whan Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30053-30062
affiliations: ["Dept. of Artificial Intelligence, Korea University"]
cities: ["Seoul"]
countries: ["Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Towards_Fine-Grained_Interpretability_Counterfactual_Explanations_for_Misclassification_with_Saliency_Partition_CVPR_2025_paper.html

title:
Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition

abstract:
Attribution-based explanation techniques capture key patterns to enhance visual interpretability. However, these patterns often lack the granularity needed for insight in fine-grained tasks, particularly in cases of model misclassification, where explanations may be insufficiently detailed. To address this limitation, we propose a fine-grained counterfactual explanation framework that generates both object-level and part-level interpretability, addressing two fundamental questions: (1) which fine-grained features contribute to model misclassification, and (2) where dominant local features influence counterfactual adjustments. Our approach yields explainable counterfactuals in a non-generative manner by quantifying similarity and weighting component contributions within regions of interest between correctly classified and misclassified samples. Furthermore, we introduce an importance-isolation module grounded in Shapley value contributions, isolating features with region-specific relevance. Extensive experiments demonstrate the superiority of our approach in capturing more granular, intuitively meaningful regions, surpassing fine-grained methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Towards_Fine-Grained_Interpretability_Counterfactual_Explanations_for_Misclassification_with_Saliency_Partition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Towards_Fine-Grained_Interpretability_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Lintong and Yin, Kang and Lee, Seong-Whan},
    title     = {Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30053-30062}
}

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


title: Prior-free 3D Object Tracking
authors: Xiuqiang Song, Li Jin, Zhengxian Zhang, Jiachen Li, Fan Zhong, Guofeng Zhang, Xueying Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1200-1209
cities: ["Jinan", "Jinan", "Beijing", "Jinan", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_Prior-free_3D_Object_Tracking_CVPR_2025_paper.html

abstract:
In this paper, we introduce a novel, truly prior-free 3D object tracking method that operates without given any model or training priors. Unlike existing methods that typically require pre-defined 3D models or specific training datasets as priors, which limit their applicability, our method is free from these constraints. Our method consists of a geometry generation module and a pose optimization module. Its core idea is to enable these two modules to automatically and iteratively enhance each other, thereby gradually building all the necessary information for the tracking task. We thus call the method as Bidirectional Iterative Tracking(BIT). The geometry generation module starts without priors and gradually generates high-precision mesh models for tracking, while the pose optimization module generates additional data during object tracking to further refine the generated models. Moreover, the generated 3D models can be stored and easily reused, allowing for seamless integration into various other tracking systems, not just our methods. Experimental results demonstrate that BIT outperforms many existing methods, even those that extensively utilize prior knowledge, while BIT does not rely on such information. Additionally, the generated 3D models deliver results comparable to actual 3D models, highlighting their superior and innovative qualities. The code is available at https://github.com/songxiuqiang/BIT.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Prior-free_3D_Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Prior-free_3D_Object_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Xiuqiang and Jin, Li and Zhang, Zhengxian and Li, Jiachen and Zhong, Fan and Zhang, Guofeng and Qin, Xueying},
    title     = {Prior-free 3D Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1200-1209}
}

raw_affiliation:
[{"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "Engineering Research Center of Digital Media Technology, Ministry of Education, P.R. China", "city": "Beijing", "country": "China"}, {"affiliation": "Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology", "city": "Jinan", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Software, Shandong University", "School of Computer Science and Technology, Shandong University", "Engineering Research Center of Digital Media Technology, Ministry of Education, P.R. China", "Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology", "State Key Lab of CAD&CG, Zhejiang University"]


title: LatentHOI: On the Generalizable Hand Object Motion Generation with Latent Hand Diffusion.
authors: Muchen Li, Sammy Christen, Chengde Wan, Yujun Cai, Renjie Liao, Leonid Sigal, Shugao Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17416-17425
affiliations: ["1,2,3,4", "5", "6"]
cities: ["Vancouver", "Beijing", "Zurich"]
countries: ["Canada", "China", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LatentHOI_On_the_Generalizable_Hand_Object_Motion_Generation_with_Latent_CVPR_2025_paper.html

abstract:
Current research on generating 3D hand-object interaction motion primarily focuses on in-domain objects. Generalization to unseen objects is essential for practical applications, yet it remains both challenging and largely unexplored.In this paper, we propose LatentHOI, a novel approach designed to tackle the challenges of generalizing hand-object interaction to unseen objects.Our main insight lies in decoupling high-level temporal motion from fine-grained spatial hand-object interactions with a latent diffusion model coupled with a Grasping Variational Autoencoder (GraspVAE). This configuration not only enhances the conditional dependency between spatial grasp and temporal motion but also improves data utilization and reduces overfitting through regularization in the latent space. We conducted extensive experiments in an unseen-object setting on both single-hand grasping and bi-manual motion datasets, including GRAB, DexYCB, and OakInk.Quantitative and qualitative evaluations demonstrate that our method significantly enhances the realism and physical plausibility of generated motions for unseen objects, both in single and bimanual manipulations, compared to the state-of-the-art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LatentHOI_On_the_Generalizable_Hand_Object_Motion_Generation_with_Latent_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LatentHOI_On_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Muchen and Christen, Sammy and Wan, Chengde and Cai, Yujun and Liao, Renjie and Sigal, Leonid and Ma, Shugao},
    title     = {LatentHOI: On the Generalizable Hand Object Motion Generation with Latent Hand Diffusion.},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17416-17425}
}

raw_affiliation:
[{"affiliation": "1,2,3,4", "city": "Vancouver", "country": "Canada"}, {"affiliation": "5", "city": "Beijing", "country": "China"}, {"affiliation": "6", "city": "Zurich", "country": "Switzerland"}]


title: Progressive Correspondence Regenerator for Robust 3D Registration
authors: Guiyu Zhao, Sheng Ao, Ye Zhang, Kai Xu, Yulan Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1210-1219
cities: ["Xiamen", "Xiamen", "Guangzhou", "Beijing", "Changsha"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Progressive_Correspondence_Regenerator_for_Robust_3D_Registration_CVPR_2025_paper.html

abstract:
Obtaining enough high-quality correspondences is crucial for robust registration. Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which either fails to correctly identify the accurate correspondences under extreme outlier ratios, or select too few correct correspondences to support robust registration. To address this challenge, we propose a novel approach named Regor, which is a progressive correspondence regenerator that generates higher-quality matches whist sufficiently robust for numerous outliers. In each iteration, we first apply prior-guided local grouping and generalized mutual matching to generate the local region correspondences. A powerful center-aware three-point consistency is then presented to achieve local correspondence correction, instead of removal. Further, we employ global correspondence refinement to obtain accurate correspondences from a global perspective. Through progressive iterations, this process yields a large number of high-quality correspondences. Extensive experiments on both indoor and outdoor datasets demonstrate that the proposed Regor significantly outperforms existing outlier removal techniques. More critically, our approach obtain 10 times more correct correspondences than outlier removal methods. As a result, our method is able to achieve robust registration even with weak features. The code is available at https://github.com/GuiyuZhao/Regor.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Progressive_Correspondence_Regenerator_for_Robust_3D_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Progressive_Correspondence_Regenerator_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Guiyu and Ao, Sheng and Zhang, Ye and Xu, Kai and Guo, Yulan},
    title     = {Progressive Correspondence Regenerator for Robust 3D Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1210-1219}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China", "Sun Yat-sen University", "Beijing Institute of Technology", "National University of Defense Technology"]


title: Cross-Modal 3D Representation with Multi-View Images and Point Clouds
authors: Ziyang Zhou, Pinghui Wang, Zi Liang, Haitao Bai, Ruofei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3728-3739
affiliations: ["MOE KLINNS Lab, Xi\u2019an Jiaotong University", "The Hong Kong Polytechnic University", "Apple"]
cities: ["Xi'an", "Hong Kong", "Cupertino"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Cross-Modal_3D_Representation_with_Multi-View_Images_and_Point_Clouds_CVPR_2025_paper.html

abstract:
The advancement of 3D understanding and representation is a crucial step for the next phase of autonomous driving, robotics, augmented and virtual reality, 3D gaming and 3D e-commerce products. However, existing 3D semantic representation research has primarily focused on point clouds to perceive 3D objects and scenes, overlooking the rich visual details offered by multi-view images, thereby limiting the potential of 3D semantic representation. This paper introduces OpenView, a novel representation method that integrates both point clouds and multi-view images to form a unified 3D representation. OpenView comprises a unique fusion framework, sequence-independent modeling, a cross-modal fusion encoder, and a progressive hard learning strategy. Our experiments demonstrate that OpenView outperforms the state-of-the-art by 11.5% and 5.5% on the R@1 metric for cross-modal retrieval and the Top-1 metric for zero-shot classification tasks, respectively. Furthermore, we showcase some applications of OpenView: 3D retrieval, 3D captioning and hierarchical data clustering, highlighting its generality in the field of 3D representation learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Cross-Modal_3D_Representation_with_Multi-View_Images_and_Point_Clouds_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Ziyang and Wang, Pinghui and Liang, Zi and Bai, Haitao and Zhang, Ruofei},
    title     = {Cross-Modal 3D Representation with Multi-View Images and Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3728-3739}
}

raw_affiliation:
[{"affiliation": "MOE KLINNS Lab, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]


title: Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs
authors: Lucas Ventura, Antoine Yang, Cordelia Schmid, Gül Varol
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18947-18958
cities: ["Paris", "Paris", "London"]
countries: ["France", "France", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ventura_Chapter-Llama_Efficient_Chaptering_in_Hour-Long_Videos_with_LLMs_CVPR_2025_paper.html

abstract:
We address the task of video chaptering, i.e., partitioning a long video timeline into semantic units and generating corresponding chapter titles. While relatively underexplored, automatic chaptering has the potential to enable efficient navigation and content retrieval in long-form videos. In this paper, we achieve strong chaptering performance on hour-long videos by efficiently addressing the problem in the text domain with our "Chapter-Llama" framework. Specifically, we leverage a pre-trained large language model (LLM) with large context window, and feed as input (i) speech transcripts and (ii) captions describing video frames, along with their respective timestamps. Given the inefficiency of exhaustively captioning all frames, we propose a lightweight speech-guided frame selection strategy based on speech transcript content, and experimentally demonstrate remarkable advantages. We train the LLM to output timestamps for the chapter boundaries, as well as free-form chapter titles. This simple yet powerful approach scales to processing one-hour long videos in a single forward pass. Our results demonstrate substantial improvements (e.g., 45.3 vs 26.7 F1 score) over the state of the art on the recent VidChapters-7M benchmark. To promote further research, we release our code and models at our project page.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ventura_Chapter-Llama_Efficient_Chaptering_in_Hour-Long_Videos_with_LLMs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ventura_Chapter-Llama_Efficient_Chaptering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ventura_2025_CVPR,
    author    = {Ventura, Lucas and Yang, Antoine and Schmid, Cordelia and Varol, G\"ul},
    title     = {Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18947-18958}
}

raw_affiliation:
[{"affiliation": "LIGM, \u00b4Ecole des Ponts, IP Paris, Univ Gustave Eiffel, CNRS", "city": "Paris", "country": "France"}, {"affiliation": "Inria, \u00b4Ecole normale sup\u00b4erieure, CNRS, PSL Research University", "city": "Paris", "country": "France"}, {"affiliation": "Google DeepMind", "city": "London", "country": "UK"}]

affiliations:
["LIGM, \u00b4Ecole des Ponts, IP Paris, Univ Gustave Eiffel, CNRS", "Inria, \u00b4Ecole normale sup\u00b4erieure, CNRS, PSL Research University", "Google DeepMind"]


title: Decompositional Neural Scene Reconstruction with Generative Diffusion Prior
authors: Junfeng Ni, Yu Liu, Ruijie Lu, Zirui Zhou, Song-Chun Zhu, Yixin Chen, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6022-6033
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_Decompositional_Neural_Scene_Reconstruction_with_Generative_Diffusion_Prior_CVPR_2025_paper.html

abstract:
Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture of all objects within, is intriguing for downstream applications but remains challenging, particularly with sparse views as input. Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas and fail to recover occluded regions. We argue that the key to solving this problem lies in supplementing missing information for these areas. To this end, we propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views. This provides additional information for the underconstrained areas, but directly incorporating diffusion prior raises potential conflicts between the reconstruction and generative guidance. Therefore, we further introduce a visibility-guided approach to dynamically adjust the per-pixel SDS loss weights. Together these components enhance both geometry and appearance recovery while remaining faithful to input images.  Extensive experiments across Replica and ScanNet++ demonstrate that our method significantly outperforms state-of-the-art methods. Notably, it achieves better object reconstruction under 10 views than the baselines under 100 views. Our method enables seamless text-based editing for geometry and appearance through SDS optimization and produces decomposed object meshes with detailed UV maps that support photorealistic Visual effects (VFX) editing. The project page is available at https://dp-recon.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_Decompositional_Neural_Scene_Reconstruction_with_Generative_Diffusion_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ni_Decompositional_Neural_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Junfeng and Liu, Yu and Lu, Ruijie and Zhou, Zirui and Zhu, Song-Chun and Chen, Yixin and Huang, Siyuan},
    title     = {Decompositional Neural Scene Reconstruction with Generative Diffusion Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6022-6033}
}

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

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


title: Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection
authors: Fuyun Wang, Tong Zhang, Yuanzhi Wang, Yide Qiu, Xin Liu, Xu Guo, Zhen Cui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20416-20426
cities: ["Nanjing", "Nanjing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Distribution_Prototype_Diffusion_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2025_paper.html

abstract:
In Open-set Supervised Anomaly Detection (OSAD), the  existing methods typically generate pseudo anomalies to  compensate for the scarcity of observed anomaly samples,  while overlooking critical priors of normal samples, leading  to less effective discriminative boundaries.  To address  this issue, we propose a Distribution Prototype Diffusion Learning (DPDL) method aimed at enclosing normal samples  within a compact and discriminative distribution space.  Specifically, we construct multiple learnable Gaussian prototypes  to create a latent representation space for abundant  and diverse normal samples and learn a Schr"odinger  bridge to facilitate a diffusive transition toward these prototypes  for normal samples while steering anomaly samples  away.  Moreover, to enhance inter-sample separation,  we design a dispersion feature learning way in hyperspherical  space, which benefits the identification of out-ofdistribution  anomalies.  Experimental results demonstrate  the effectiveness and superiority of our proposed DPDL,  achieving state-of-the-art performance on 9 public datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Distribution_Prototype_Diffusion_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Distribution_Prototype_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Fuyun and Zhang, Tong and Wang, Yuanzhi and Qiu, Yide and Liu, Xin and Guo, Xu and Cui, Zhen},
    title     = {Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20416-20426}
}

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

affiliations:
["Nanjing University of Science and Technology", "Nanjing SeetaCloud Technology", "Beijing Normal University"]


title: Learning Visual Generative Priors without Text
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8051-8061
cities: ["Shenyang", "Hangzhou", "Shanghai", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Learning_Visual_Generative_Priors_without_Text_CVPR_2025_paper.html

authors:
Shuailei Ma, Kecheng Zheng, Ying Wei, Wei Wu, Fan Lu, Yifei Zhang, Chen-Wei Xie, Biao Gong, Jiapeng Zhu, Yujun Shen

abstract:
Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that grasping the cross-modality alignment is not a necessity for a sound visual generative prior, whose focus should be on texture modeling. Such a philosophy inspires us to study image-to-image (I2I) generation, where models can learn from in-the-wild images in a self-supervised manner. We first develop a pure vision-based training framework, Lumos, and confirm the feasibility and the scalability of learning I2I models. We then find that, as an upstream task of T2I, our I2I model serves as a more foundational visual prior and achieves on-par or better performance than existing T2I models using only 1/10 text-image pairs for fine-tuning. We further demonstrate the superiority of I2I priors over T2I priors on some text-irrelevant vision tasks, like image-to-3D and image-to-video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Learning_Visual_Generative_Priors_without_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Learning_Visual_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Shuailei and Zheng, Kecheng and Wei, Ying and Wu, Wei and Lu, Fan and Zhang, Yifei and Xie, Chen-Wei and Gong, Biao and Zhu, Jiapeng and Shen, Yujun},
    title     = {Learning Visual Generative Priors without Text},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8051-8061}
}

raw_affiliation:
[{"affiliation": "College of Information Science and Engineering, Northeastern University", "city": "Shenyang", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]

affiliations:
["College of Information Science and Engineering, Northeastern University", "Ant Group", "Shanghai Jiao Tong University", "Alibaba Group", "HKUST"]


title: Joint Scheduling of Causal Prompts and Tasks for Multi-Task Learning
authors: Chaoyang Li, Jianyang Qin, Jinhao Cui, Zeyu Liu, Ning Hu, Qing Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25124-25134
affiliations: ["Harbin Institute of Technology", "Peng Cheng Laboratory"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Joint_Scheduling_of_Causal_Prompts_and_Tasks_for_Multi-Task_Learning_CVPR_2025_paper.html

abstract:
Multi-task prompt learning has emerged as a promising technique for fine-tuning pre-trained Vision-Language Models (VLMs) to various downstream tasks. However, existing methods ignore challenges caused by spurious correlations and dynamic task relationships, which may reduce the model performance. To tackle these challenges, we propose JSCPT, a novel approach for Joint Scheduling of Causal Prompts and Tasks to enhance multi-task prompt learning. Specifically, we first design a Multi-Task Vison-Language Prompt (MTVLP) model, which learns task-shared and task-specific vison-language prompts and selects useful prompt features via causal intervention, alleviating spurious correlations. Then, we propose the task-prompt scheduler that models inter-task affinities and assesses the causal effect of prompt features to optimize the multi-task prompt learning process. Finally, we formulate the scheduler and the multi-task prompt learning process as a bi-level optimization problem to optimize prompts and tasks adaptively. In the lower optimization, MTVLP is updated with the scheduled gradient, while in the upper optimization, the scheduler is updated with the implicit gradient. Extensive experiments show the superiority of our proposed JSCPT approach over several baselines in terms of multi-task prompt learning for pre-trained VLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Joint_Scheduling_of_Causal_Prompts_and_Tasks_for_Multi-Task_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Joint_Scheduling_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Chaoyang and Qin, Jianyang and Cui, Jinhao and Liu, Zeyu and Hu, Ning and Liao, Qing},
    title     = {Joint Scheduling of Causal Prompts and Tasks for Multi-Task Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25124-25134}
}

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


title: Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers
authors: Ji Zhao, Banglei Guan, Zibin Liu, Laurent Kneip
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11515-11524
cities: ["Beijing", "Beijing", "Shanghai"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Full-DoF_Egomotion_Estimation_for_Event_Cameras_Using_Geometric_Solvers_CVPR_2025_paper.html

abstract:
For event cameras, current sparse geometric solvers for egomotion estimation assume that the rotational displacements are known, such as those provided by an IMU. Thus, they can only recover the translational motion parameters. Recovering full-DoF motion parameters using a sparse geometric solver is a more challenging task, and has not yet been investigated. In this paper, we propose several solvers to estimate both rotational and translational velocities within a unified framework. Our method leverages event manifolds induced by line segments. The problem formulations are based on either an incidence relation for lines or a novel coplanarity relation for normal vectors. We demonstrate the possibility of recovering full-DoF egomotion parameters for both angular and linear velocities without requiring  extra sensor measurements or motion priors. To achieve efficient optimization, we exploit the Adam framework with a first-order approximation of rotations for quick initialization. Experiments on both synthetic and real-world data demonstrate the effectiveness of our method. The code is available at https://github.com/jizhaox/relpose-event.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Full-DoF_Egomotion_Estimation_for_Event_Cameras_Using_Geometric_Solvers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Full-DoF_Egomotion_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Ji and Guan, Banglei and Liu, Zibin and Kneip, Laurent},
    title     = {Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11515-11524}
}

raw_affiliation:
[{"affiliation": "Independent Researcher", "city": "Beijing", "country": "China"}, {"affiliation": "College of Aerospace Science and Engineering, National University of Defense Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Mobile Perception Lab, ShanghaiTech University", "city": "Shanghai", "country": "China"}]

affiliations:
["Independent Researcher", "College of Aerospace Science and Engineering, National University of Defense Technology", "Mobile Perception Lab, ShanghaiTech University"]


title: 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting
authors: Qi Wu, Janick Martinez Esturo, Ashkan Mirzaei, Nicolas Moënne-Loccoz, Zan Gojcic
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26036-26046
affiliations: ["NVIDIA", "University of Toronto"]
cities: ["Santa Clara", "Toronto"]
countries: ["USA", "Canada"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_3DGUT_Enabling_Distorted_Cameras_and_Secondary_Rays_in_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) enables efficient reconstruction and high-fidelity real-time rendering of complex scenes on consumer hardware.  However, due to its rasterization-based formulation, 3DGS is constrained to ideal pinhole cameras and lacks support for secondary lighting effects. Recent methods address these limitations by tracing the particles instead, but, this comes at the cost of significantly slower rendering. In this work, we propose 3D Gaussian Unscented Transform (3DGUT), replacing the EWA splatting formulation with the Unscented Transform that approximates the particles through sigma points, which can be projected exactly under any nonlinear projection function. This modification enables trivial support of distorted cameras with time dependent effects such as rolling shutter, while retaining the efficiency of rasterization. Additionally, we align our rendering formulation with that of tracing-based methods, enabling secondary ray tracing required to represent phenomena such as reflections and refraction within the same 3D representation. The source code is available at: https://github.com/nv-tlabs/3dgrut.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_3DGUT_Enabling_Distorted_Cameras_and_Secondary_Rays_in_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_3DGUT_Enabling_Distorted_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Qi and Esturo, Janick Martinez and Mirzaei, Ashkan and Mo\"enne-Loccoz, Nicolas and Gojcic, Zan},
    title     = {3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26036-26046}
}

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


title: Teaching Large Language Models to Regress Accurate Image Quality Scores Using Score Distribution
authors: Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, Chao Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14483-14494
cities: ["Shenzhen", "Hong Kong", "Shanghai", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/You_Teaching_Large_Language_Models_to_Regress_Accurate_Image_Quality_Scores_CVPR_2025_paper.html

abstract:
With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising performance in linguistic quality description. However, current methods still fall short in accurately scoring image quality. In this work, we aim to leverage MLLMs to regress accurate quality scores. A key challenge is that the quality score is inherently continuous, typically modeled as a Gaussian distribution, whereas MLLMs generate discrete token outputs. This mismatch necessitates score discretization. Previous approaches discretize the mean score into a one-hot label, resulting in information loss and failing to capture inter-image relationships. We propose a distribution-based approach that discretizes the score distribution into a soft label. This method preserves the characteristics of the score distribution, achieving high accuracy and maintaining inter-image relationships. Moreover, to address dataset variation, where different IQA datasets exhibit various distributions, we introduce a fidelity loss based on Thurstone's model. This loss captures intra-dataset relationships, facilitating co-training across multiple IQA datasets. With these designs, we develop the distribution-based Depicted image Quality Assessment model for Score regression (DeQA-Score). Experiments across multiple benchmarks show that DeQA-Score stably outperforms baselines in score regression. Also, DeQA-Score can predict the score distribution that closely aligns with human annotations. Codes and model weights have been released in https://depictqa.github.io/deqa-score/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/You_Teaching_Large_Language_Models_to_Regress_Accurate_Image_Quality_Scores_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/You_Teaching_Large_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{You_2025_CVPR,
    author    = {You, Zhiyuan and Cai, Xin and Gu, Jinjin and Xue, Tianfan and Dong, Chao},
    title     = {Teaching Large Language Models to Regress Accurate Image Quality Scores Using Score Distribution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14483-14494}
}

raw_affiliation:
[{"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "The Chinese University of Hong Kong", "Shanghai AI Laboratory", "Shenzhen University of Advanced Technology"]


title: Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference
authors: Hao Yin, Guangzong Si, Zilei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9382-9391
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Lifting_the_Veil_on_Visual_Information_Flow_in_MLLMs_Unlocking_CVPR_2025_paper.html

abstract:
Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LLMs). However, how MLLMs process and utilize visual information remains unclear. In this paper, a shift in the dominant flow of visual information is uncovered: (1) in shallow layers, strong interactions are observed between image tokens and instruction tokens, where most visual information is injected into instruction tokens to form cross-modal semantic representations; (2) in deeper layers, image tokens primarily interact with each other, aggregating the remaining visual information to optimize semantic representations within the visual modality. Based on these insights, we propose Hierarchical Modality-Aware Pruning (HiMAP), a plug-and-play inference acceleration method that dynamically prunes image tokens at specific layers, reducing computational costs by approximately 65% without sacrificing performance. Our findings offer a new understanding of visual information processing in MLLMs and provide a state-of-the-art solution for efficient inference.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Lifting_the_Veil_on_Visual_Information_Flow_in_MLLMs_Unlocking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_Lifting_the_Veil_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Hao and Si, Guangzong and Wang, Zilei},
    title     = {Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9382-9391}
}


authors: Jialai Wang, Yuxiao Wu, Weiye Xu, Yating Huang, Chao Zhang, Zongpeng Li, Mingwei Xu, Zhenkai Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20103-20112
cities: ["Singapore", "Beijing", "Wuhan", "Beijing"]
countries: ["Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Your_Scale_Factors_are_My_Weapon_Targeted_Bit-Flip_Attacks_on_CVPR_2025_paper.html

title:
Your Scale Factors are My Weapon: Targeted Bit-Flip Attacks on Vision Transformers via Scale Factor Manipulation

abstract:
Vision Transformers (ViTs) have experienced significant progress and are quantized for deployment in resource-constrained applications. Quantized models are vulnerable to targeted bit-flip attacks (BFAs). A targeted BFA prepares a trigger and a corresponding Trojan/backdoor, inserting the latter (with RowHammer bit flipping) into a victim model, to mislead its classification on samples containing the trigger. Existing targeted BFAs on quantized ViTs are limited in that: (1) they require numerous bit-flips, and (2) the separation between flipped bits is below 4 KB, making attacks infeasible with RowHammer in real-world scenarios. We propose a new and practical targeted attack Flip-S against quantized ViTs. The core insight is that in quantized models, a scale factor change ripples through a batch of model weights. Consequently, flipping bits in scale factors, rather than solely in model weights, enables more cost-effective attacks. We design a Scale-Factor-Search (SFS) algorithm to identify critical bits in scale factors for flipping, and adopt a mutual exclusion strategy to guarantee a 4 KB separation between flips. We evaluate Flip-S on CIFAR-10 and ImageNet datasets across five ViT architectures and two quantization levels. Results show that Flip-S achieves attack success rate (ASR) exceeding 90.0% on all models with 50 bits flipped, outperforming baselines with ASR typically below 80.0%. Furthermore, compared to the SOTA, Flip-S reduces the number of required bit-flips by 8x-20xwhile reaching equal or higher ASR. Our source code is publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Your_Scale_Factors_are_My_Weapon_Targeted_Bit-Flip_Attacks_on_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jialai and Wu, Yuxiao and Xu, Weiye and Huang, Yating and Zhang, Chao and Li, Zongpeng and Xu, Mingwei and Liang, Zhenkai},
    title     = {Your Scale Factors are My Weapon: Targeted Bit-Flip Attacks on Vision Transformers via Scale Factor Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20103-20112}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "China Mobile Research Institute", "city": "Beijing", "country": "China"}]

affiliations:
["National University of Singapore", "Tsinghua University", "Huazhong University of Science and Technology", "China Mobile Research Institute"]


title: Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14460-14471
cities: ["Unknown", "Shanghai", "Beijing"]
countries: ["Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Marten_Visual_Question_Answering_with_Mask_Generation_for_Multi-modal_Document_CVPR_2025_paper.html

authors:
Zining Wang, Tongkun Guan, Pei Fu, Chen Duan, Qianyi Jiang, Zhentao Guo, Shan Guo, Junfeng Luo, Wei Shen, Xiaokang Yang

abstract:
Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understanding, i.e., they endow large language models with visual comprehension capabilities; however, how to design a suitable image-text pre-training task for bridging the visual and language modality in document-level MLLMs remains underexplored. In this study, we introduce a novel visual-language alignment method that casts the key issue as a Visual Question Answering with Mask generation (VQAMask) task, optimizing two tasks simultaneously: VQA-based text parsing and mask generation. The former allows the model to implicitly align images and text at the semantic level. The latter introduces an additional mask generator (discarded during inference) to explicitly ensure alignment between visual texts within images and their corresponding image regions at a spatially-aware level. Together, they can prevent model hallucinations when parsing visual text and effectively promote spatially-aware feature representation learning. To support the proposed VQAMask task, we construct a comprehensive image-mask generation pipeline and provide a large-scale dataset with 6M data (MTMask6M). Subsequently, we demonstrate that introducing the proposed mask generation task yields competitive document-level understanding performance. Leveraging the proposed VQAMask, we introduce Marten, a training-efficient MLLM tailored for document-level understanding. Extensive experiments show that our Marten consistently achieves significant improvements among 8B-MLLMs in document-centric tasks. Code and datasets are available at https://github.com/PriNing/Marten.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Marten_Visual_Question_Answering_with_Mask_Generation_for_Multi-modal_Document_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Marten_Visual_Question_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zining and Guan, Tongkun and Fu, Pei and Duan, Chen and Jiang, Qianyi and Guo, Zhentao and Guo, Shan and Luo, Junfeng and Shen, Wei and Yang, Xiaokang},
    title     = {Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14460-14471}
}

raw_affiliation:
[{"affiliation": "Meituan", "city": "Unknown", "country": "Unknown"}, {"affiliation": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["Meituan", "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "Beijing Institute of Technology"]


title: Mamba-Reg: Vision Mamba Also Needs Registers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14944-14953
affiliations: ["Johns Hopkins University", "University of Florida", "UC Santa Cruz"]
cities: ["Baltimore", "Gainesville", "Santa Cruz"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Mamba-Reg_Vision_Mamba_Also_Needs_Registers_CVPR_2025_paper.html

authors:
Feng Wang, Jiahao Wang, Sucheng Ren, Guoyizhe Wei, Jieru Mei, Wei Shao, Yuyin Zhou, Alan Yuille, Cihang Xie

abstract:
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in low-information background areas of images, appear much more severe in Vision Mamba---they exist prevalently even with the tiny-sized model and activate extensively across background regions. To mitigate this issue, we follow the prior solution of introducing register tokens into Vision Mamba. To better cope with Mamba blocks' uni-directional inference paradigm, two key modifications are introduced: 1) evenly inserting registers throughout the input token sequence, and 2) recycling registers for final decision predictions. We term this new architecture MambaReg. Qualitative observations suggest, compared to vanilla Vision Mamba, MambaReg's feature maps appear cleaner and more focused on semantically meaningful regions. Quantitatively, MambaReg attains stronger performance and scales better. For example, on the ImageNet benchmark, our MambaReg-B attains 83.0% accuracy, significantly outperforming Vim-B's 81.8%; furthermore, we provide the first successful scaling to the large model size (i.e., with 340M parameters), attaining a competitive accuracy of 83.6% (84.5% if finetuned with 384x384 inputs). Additional validation on the downstream semantic segmentation task also supports MambaReg's efficacy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Mamba-Reg_Vision_Mamba_Also_Needs_Registers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Mamba-Reg_Vision_Mamba_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Feng and Wang, Jiahao and Ren, Sucheng and Wei, Guoyizhe and Mei, Jieru and Shao, Wei and Zhou, Yuyin and Yuille, Alan and Xie, Cihang},
    title     = {Mamba-Reg: Vision Mamba Also Needs Registers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14944-14953}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "University of Florida", "city": "Gainesville", "country": "USA"}, {"affiliation": "UC Santa Cruz", "city": "Santa Cruz", "country": "USA"}]


title: It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data
authors: Dominik Schnaus, Nikita Araslanov, Daniel Cremers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24983-24992
affiliations: ["TU Munich Munich Center for Machine Learning"]
cities: ["Munich"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Schnaus_Its_a_Blind_Match_Towards_Vision-Language_Correspondence_without_Parallel_Data_CVPR_2025_paper.html

abstract:
The platonic representation hypothesis suggests that vision and language embeddings become more homogeneous as model and dataset sizes increase. In particular, pairwise distances within each modality become more similar. This suggests that as foundation models mature, it may become possible to match vision and language embeddings in a fully unsupervised fashion, i.e. without parallel data. We present the first feasibility study, and investigate conformity of existing vision and language foundation models in the context of unsupervised, or "blind", matching. First, we formulate unsupervised matching as a quadratic assignment problem and introduce a novel heuristic that outperforms previous solvers. We also develop a technique to find optimal matching problems, for which a non-trivial match is very likely. Second, we conduct an extensive study deploying a range of vision and language models on four datasets. Our analysis reveals that for many problem instances, vision and language representations can be indeed matched without supervision. This finding opens up the exciting possibility of embedding semantic knowledge into other modalities virtually annotation-free. As a proof of concept, we showcase an unsupervised classifier, which achieves non-trivial classification accuracy without any image-text annotation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Schnaus_Its_a_Blind_Match_Towards_Vision-Language_Correspondence_without_Parallel_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Schnaus_Its_a_Blind_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Schnaus_2025_CVPR,
    author    = {Schnaus, Dominik and Araslanov, Nikita and Cremers, Daniel},
    title     = {It's a (Blind) Match! Towards Vision-Language Correspondence without Parallel Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24983-24992}
}

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


title: Open Set Label Shift with Test Time Out-of-Distribution Reference
authors: Changkun Ye, Russell Tsuchida, Lars Petersson, Nick Barnes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30619-30629
affiliations: ["Australian National University", "Data61 CSIRO", "Monash University"]
cities: ["ACT", "ACT", "Melbourne"]
countries: ["Australia", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Open_Set_Label_Shift_with_Test_Time_Out-of-Distribution_Reference_CVPR_2025_paper.html

abstract:
Open set label shift (OSLS) occurs when label distributions change from a source to a target distribution, and the target distribution has an additional out-of-distribution (OOD) class.In this work, we build estimators for both source and target open set label distributions using a source domain in-distribution (ID) classifier and an ID/OOD classifier. With reasonable assumptions on the ID/OOD classifier, the estimators are assembled into a sequence of three stages: 1) an estimate of the source label distribution of the OOD class, 2) an EM algorithm for Maximum Likelihood estimates (MLE) of the target label distribution, and 3) an estimate of the target label distribution of OOD class under relaxed assumptions on the OOD classifier.The sampling errors of estimates in 1) and 3) are quantified with a concentration inequality.The estimation result allows us to correct the ID classifier trained on the source distribution to the target distribution without retraining.Experiments on a variety of open set label shift settings demonstrate the effectiveness of our model.Our code is available at https://github.com/ChangkunYe/OpenSetLabelShift.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Open_Set_Label_Shift_with_Test_Time_Out-of-Distribution_Reference_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Open_Set_Label_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Changkun and Tsuchida, Russell and Petersson, Lars and Barnes, Nick},
    title     = {Open Set Label Shift with Test Time Out-of-Distribution Reference},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30619-30629}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "ACT", "country": "Australia"}, {"affiliation": "Data61 CSIRO", "city": "ACT", "country": "Australia"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]


title: Visual Persona: Foundation Model for Full-Body Human Customization
authors: Jisu Nam, Soowon Son, Zhan Xu, Jing Shi, Difan Liu, Feng Liu, Seungryong Kim, Yang Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18630-18641
affiliations: ["KAIST AI", "Adobe Research"]
cities: ["Daejeon", "San Jose"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nam_Visual_Persona_Foundation_Model_for_Full-Body_Human_Customization_CVPR_2025_paper.html

abstract:
We introduce Visual Persona, a foundation model for text-to-image full-body human customization that, given a single in-the-wild human image, generates diverse images of the individual guided by text descriptions. Unlike prior methods that focus solely on preserving facial identity, our approach captures detailed full-body appearance, aligning with text descriptions for body structure and scene variations. Training this model requires large-scale paired human data, consisting of multiple images per individual with consistent full-body identities, which is notoriously difficult to obtain. To address this, we propose a data curation pipeline leveraging vision-language models to evaluate full-body appearance consistency, resulting in Visual Persona-500K--a dataset of 580k paired human images across 100k unique identities. For precise appearance transfer, we introduce a transformer encoder-decoder architecture adapted to a pre-trained text-to-image diffusion model, which augments the input image into distinct body regions, encodes these regions as local appearance features, and projects them into dense identity embeddings independently to condition the diffusion model for synthesizing customized images. Visual Persona consistently surpasses existing approaches, generating high-quality, customized images from in-the-wild inputs. Extensive ablation studies validate design choices, and we demonstrate the versatility of Visual Persona across various downstream tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nam_Visual_Persona_Foundation_Model_for_Full-Body_Human_Customization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nam_Visual_Persona_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nam_2025_CVPR,
    author    = {Nam, Jisu and Son, Soowon and Xu, Zhan and Shi, Jing and Liu, Difan and Liu, Feng and Kim, Seungryong and Zhou, Yang},
    title     = {Visual Persona: Foundation Model for Full-Body Human Customization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18630-18641}
}

raw_affiliation:
[{"affiliation": "KAIST AI", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: SOGS: Second-Order Anchor for Advanced 3D Gaussian Splatting
authors: Jiahui Zhang, Fangneng Zhan, Ling Shao, Shijian Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11167-11176
cities: ["Singapore", "Cambridge", "Cambridge", "Beijing"]
countries: ["Singapore", "USA", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_SOGS_Second-Order_Anchor_for_Advanced_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Anchor-based 3D Gaussian splatting (3D-GS) exploits anchor features in 3D Gaussian prediction, which has achieved impressive 3D rendering quality with reduced Gaussian redundancy. On the other hand, it often encounters the dilemma among anchor features, model size, and rendering quality - large anchor features lead to large 3D models and high-quality rendering whereas reducing anchor features degrades Gaussian attribute prediction which leads to clear artifacts in the rendered textures and geometries. We design SOGS, an anchor-based 3D-GS technique that introduces second-order anchors to achieve superior rendering quality and reduced anchor features and model size simultaneously. Specifically, SOGS incorporates covariance-based second-order statistics and correlation across feature dimensions to augment features within each anchor, compensating for the reduced feature size and improving rendering quality effectively. In addition, it introduces a selective gradient loss to enhance the optimization of scene textures and scene geometries, leading to high-quality rendering with small anchor features. Extensive experiments over multiple widely adopted benchmarks show that SOGS achieves superior rendering quality in novel view synthesis with clearly reduced model size.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_SOGS_Second-Order_Anchor_for_Advanced_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jiahui and Zhan, Fangneng and Shao, Ling and Lu, Shijian},
    title     = {SOGS: Second-Order Anchor for Advanced 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11167-11176}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "UCAS-Terminus AI Lab, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Nanyang Technological University", "Harvard University", "MIT", "UCAS-Terminus AI Lab, University of Chinese Academy of Sciences"]


title: GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction
authors: Yuanhui Huang, Amonnut Thammatadatrakoon, Wenzhao Zheng, Yunpeng Zhang, Dalong Du, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27477-27486
affiliations: ["Department of Automation, Tsinghua University", "Phigent Robotics"]
cities: ["Beijing", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_GaussianFormer-2_Probabilistic_Gaussian_Superposition_for_Efficient_3D_Occupancy_Prediction_CVPR_2025_paper.html

abstract:
3D semantic occupancy prediction has garnered attention as an important task for the robustness of vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most existing methods leverage dense grid-based scene representations, overlooking the spatial sparsity of the driving scenes, which leads to computational redundancy. Although 3D semantic Gaussian serves as an object-centric sparse alternative, most of the Gaussians still describe the empty region with low efficiency. To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry. Furthermore, we adopt the exact Gaussian mixture model for semantics calculation to avoid unnecessary overlapping of Gaussians. To effectively initialize Gaussians in non-empty region, we design a distribution-based initialization module which learns the pixel-aligned occupancy distribution instead of the depth of surfaces. We conduct extensive experiments on nuScenes and KITTI-360 datasets and our GaussianFormer-2 achieves state-of-the-art performance with high efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_GaussianFormer-2_Probabilistic_Gaussian_Superposition_for_Efficient_3D_Occupancy_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_GaussianFormer-2_Probabilistic_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yuanhui and Thammatadatrakoon, Amonnut and Zheng, Wenzhao and Zhang, Yunpeng and Du, Dalong and Lu, Jiwen},
    title     = {GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27477-27486}
}

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


title: MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification
authors: Jianwei Zhao, Xin Li, Fan Yang, Qiang Zhai, Ao Luo, Yang Zhao, Hong Cheng, Huazhu Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20789-20799
affiliations: ["UESTC", "AIQ", "SICAU", "SWJTU", "IHPC, A*STAR"]
cities: ["Chengdu", "Beijing", "Ya'an", "Chengdu", "Singapore"]
countries: ["China", "China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_MExD_An_Expert-Infused_Diffusion_Model_for_Whole-Slide_Image_Classification_CVPR_2025_paper.html

abstract:
Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions, which introduce noise and cause data imbalance during feature aggregation. To address these issues, we propose MExD, an Expert-Infused Diffusion Model that combines the strengths of a Mixture-of-Experts (MoE) mechanism with a diffusion model for enhanced classification. MExD balances patch feature distribution through a novel MoE-based aggregator that selectively emphasizes relevant information, effectively filtering noise, addressing data imbalance, and extracting essential features. These features are then integrated via a diffusion-based generative process to directly yield the class distribution for the WSI. Moving beyond conventional discriminative approaches, MExD represents the first generative strategy in WSI classification, capturing fine-grained details for robust and precise results. Our MExD is validated on three widely-used benchmarks--Camelyon16, TCGA-NSCLC, and BRACS--consistently achieving state-of-the-art performance in both binary and multi-class tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_MExD_An_Expert-Infused_Diffusion_Model_for_Whole-Slide_Image_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_MExD_An_Expert-Infused_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Jianwei and Li, Xin and Yang, Fan and Zhai, Qiang and Luo, Ao and Zhao, Yang and Cheng, Hong and Fu, Huazhu},
    title     = {MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20789-20799}
}

raw_affiliation:
[{"affiliation": "UESTC", "city": "Chengdu", "country": "China"}, {"affiliation": "AIQ", "city": "Beijing", "country": "China"}, {"affiliation": "SICAU", "city": "Ya'an", "country": "China"}, {"affiliation": "SWJTU", "city": "Chengdu", "country": "China"}, {"affiliation": "IHPC, A*STAR", "city": "Singapore", "country": "Singapore"}]


title: Flexible Frame Selection for Efficient Video Reasoning
authors: Shyamal Buch, Arsha Nagrani, Anurag Arnab, Cordelia Schmid
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29071-29082
raw_affiliation: [{"affiliation": "Google DeepMind", "city": "London", "country": "UK"}]
affiliations: ["Google DeepMind"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Buch_Flexible_Frame_Selection_for_Efficient_Video_Reasoning_CVPR_2025_paper.html

abstract:
Video-language models have shown promise for addressing a range of multimodal tasks for video understanding, such as video question-answering. However, the inherent computational challenges of processing long video data and increasing model sizes have led to standard approaches that are limited by the number of frames they can process. In this work, we propose the Flexible Frame Selector (FFS), a learnable policy model with a new flexible selection operation, that helps alleviate input context restrictions by enabling video-language models to focus on the most informative frames for the downstream multimodal task, without adding undue processing cost. Our method differentiates from prior work due to its learnability, efficiency, and flexibility. We verify the efficacy of our method on standard video-question answering and reasoning benchmarks, and observe that our model can improve base video-language model accuracy while reducing the number of downstream processed frames.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Buch_Flexible_Frame_Selection_for_Efficient_Video_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Buch_Flexible_Frame_Selection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Buch_2025_CVPR,
    author    = {Buch, Shyamal and Nagrani, Arsha and Arnab, Anurag and Schmid, Cordelia},
    title     = {Flexible Frame Selection for Efficient Video Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29071-29082}
}


title: EventGPT: Event Stream Understanding with Multimodal Large Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29139-29149
cities: ["Xi'an", "Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EventGPT_Event_Stream_Understanding_with_Multimodal_Large_Language_Models_CVPR_2025_paper.html

authors:
Shaoyu Liu, Jianing Li, Guanghui Zhao, Yunjian Zhang, Xin Meng, Fei Richard Yu, Xiangyang Ji, Ming Li

abstract:
Event cameras capture visual information as asynchronous pixel change streams, excelling in challenging lighting and high-dynamic scenarios. Existing multimodal large language models (MLLMs) concentrate on natural RGB images, failing in scenarios where event data fits better. In this paper, we introduce EventGPT, the first MLLM for event stream understanding, pioneering the integration of large language models (LLMs) with event-based vision. To bridge the huge domain gap, we propose a three-stage optimization paradigm to progressively equip a pre-trained LLM with event understanding. Our EventGPT consists of an event encoder, a spatio-temporal aggregator, a linear projector, an event-language adapter, and an LLM. Firstly, GPT-generated RGB image-text pairs warm up the linear projector, following LLaVA, as the gap between natural images and language is smaller. Secondly, we construct N-ImageNet-Chat, a large synthetic dataset of event data and corresponding texts to enable the use of the spatio-temporal aggregator and to train the event-language adapter, thereby aligning event features more closely with the language space. Finally, we gather an instruction dataset, Event-Chat, which contains extensive real-world data to fine-tune the entire model, further enhancing its generalization ability. We construct a comprehensive benchmark, and experiments show that EventGPT surpasses previous state-of-the-art MLLMs in generation quality, descriptive accuracy, and reasoning capability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EventGPT_Event_Stream_Understanding_with_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Shaoyu and Li, Jianing and Zhao, Guanghui and Zhang, Yunjian and Meng, Xin and Yu, Fei Richard and Ji, Xiangyang and Li, Ming},
    title     = {EventGPT: Event Stream Understanding with Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29139-29149}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)", "city": "Shenzhen", "country": "China"}]

affiliations:
["Xidian University", "Tsinghua University", "Peking University", "Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)"]


title: Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
authors: Lingchen Sun, Rongyuan Wu, Zhiyuan Ma, Shuaizheng Liu, Qiaosi Yi, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2333-2343
affiliations: ["The Hong Kong Polytechnic University", "OPPO Research Institute"]
cities: ["Hong Kong", "Shenzhen"]
countries: ["Hong Kong", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Pixel-level_and_Semantic-level_Adjustable_Super-resolution_A_Dual-LoRA_Approach_CVPR_2025_paper.html

abstract:
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR objectives in the training process, struggling to balance pixel-wise fidelity and perceptual quality. Meanwhile, users have varying preferences on SR results, thus it is demanded to develop an adjustable SR model that can be tailored to different fidelity-perception preferences during inference without re-training. We present Pixel-level and Semantic-level Adjustable SR (PiSA-SR), which learns two LoRA modules upon the pre-trained stable-diffusion (SD) model to achieve improved and adjustable SR results. We first formulate the SD-based SR problem as learning the residual between the low-quality input and the high-quality output, then show that the learning objective can be decoupled into two distinct LoRA weight spaces: one is characterized by the l2-loss for pixel-level regression, and another is characterized by the LPIPS and classifier score distillation losses to extract semantic information from pre-trained classification and SD models.  In its default setting, PiSA-SR can be performed in a single diffusion step, achieving leading real-world SR results in both quality and efficiency. By introducing two adjustable guidance scales on the two LoRA modules to control the strengths of pixel-wise fidelity and semantic-level details during inference, PiSA-SR can offer flexible SR results according to user preference without re-training. The source code of our method can be found at https://github.com/csslc/PiSA-SR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Pixel-level_and_Semantic-level_Adjustable_Super-resolution_A_Dual-LoRA_Approach_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Lingchen and Wu, Rongyuan and Ma, Zhiyuan and Liu, Shuaizheng and Yi, Qiaosi and Zhang, Lei},
    title     = {Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2333-2343}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "OPPO Research Institute", "city": "Shenzhen", "country": "China"}]


title: Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples
authors: Weiwei Li, Junzhuo Liu, Yuanyuan Ren, Yuchen Zheng, Yahao Liu, Wen Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15486-15496
affiliations: ["University of Electronic Science and Technology of China", "Shihezi University"]
cities: ["Chengdu", "Shihezi"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Let_Samples_Speak_Mitigating_Spurious_Correlation_by_Exploiting_the_Clusterness_CVPR_2025_paper.html

abstract:
Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some empirical assumptions (e.g., simplicity of bias). However, these methods may yield unsatisfying performance due to the intricate and elusive nature of spurious correlations in real-world data. In this paper, we propose a data-oriented approach to mitigate the spurious correlation in deep learning models. We observe that samples that are influenced by spurious features tend to exhibit a dispersed distribution in the learned feature space. This allows us to identify the presence of spurious features. Subsequently, we obtain a bias-invariant representation by neutralizing the spuriousfeatures based on a simple grouping strategy. Then, we learn a feature transformation to eliminate the spuriousfeatures by aligning with this bias-invariant representation. Finally, we update the classifier by incorporating the learned feature transformation and obtain an unbiased model. By integrating the aforementioned identifying, neutralizing, eliminating and updating procedures, we build an effective pipeline for mitigating spurious correlation. Experiments on image and NLP debiasing benchmarks show an improvement in worst group accuracy of more than 20% compared to standard empirical risk minimization (ERM).  Codes and checkpoints are available at https://github.com/davelee-uestc/nsf_debiasing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Let_Samples_Speak_Mitigating_Spurious_Correlation_by_Exploiting_the_Clusterness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Let_Samples_Speak_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Weiwei and Liu, Junzhuo and Ren, Yuanyuan and Zheng, Yuchen and Liu, Yahao and Li, Wen},
    title     = {Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15486-15496}
}

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


title: Mr. DETR: Instructive Multi-Route Training for Detection Transformers
authors: Chang-Bin Zhang, Yujie Zhong, Kai Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9933-9943
affiliations: ["Visual AI Lab, The University of Hong Kong", "Meituan Inc."]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Mr._DETR_Instructive_Multi-Route_Training_for_Detection_Transformers_CVPR_2025_paper.html

abstract:
Existing methods enhance the training of detection transformers by incorporating an auxiliary one-to-many assignment. In this work, we treat the model as a multi-task framework, simultaneously performing one-to-one and one-to-many predictions. We investigate the roles of each component in the transformer decoder across these two training targets, including self-attention, cross-attention, and feed-forward network. Our empirical results demonstrate that any independent component in the decoder can effectively learn both targets simultaneously, even when other components are shared. This finding leads us to propose a multi-route training mechanism, featuring a primary route for one-to-one prediction and two auxiliary training routes for one-to-many prediction. We enhance the training mechanism with a novel instructive self-attention that dynamically and flexibly guides object queries for one-to-many prediction. The auxiliary routes are removed during inference, ensuring no impact on model architecture or inference cost. We conduct extensive experiments on various baselines, achieving consistent improvements as shown in Fig. 1. Project page: https://visual-ai.github.io/mrdetr

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Mr._DETR_Instructive_Multi-Route_Training_for_Detection_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Mr._DETR_Instructive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chang-Bin and Zhong, Yujie and Han, Kai},
    title     = {Mr. DETR: Instructive Multi-Route Training for Detection Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9933-9943}
}

raw_affiliation:
[{"affiliation": "Visual AI Lab, The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Meituan Inc.", "city": "Beijing", "country": "China"}]


title: MITracker: Multi-View Integration for Visual Object Tracking
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27176-27185
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_MITracker_Multi-View_Integration_for_Visual_Object_Tracking_CVPR_2025_paper.html

authors:
Mengjie Xu, Yitao Zhu, Haotian Jiang, Jiaming Li, Zhenrong Shen, Sheng Wang, Haolin Huang, Xinyu Wang, Han Zhang, Qing Yang, Qian Wang

abstract:
Multi-view object tracking (MVOT) offers promising solutions to challenges such as occlusion and target loss, which are common in traditional single-view tracking. However, progress has been limited by the lack of comprehensive multi-view datasets and effective cross-view integration methods. To overcome these limitations, we compiled a Multi-View object Tracking (MVTrack) dataset of 234K high-quality annotated frames featuring 27 distinct objects across various scenes. In conjunction with this dataset, we introduce a novel MVOT method, Multi-View Integration Tracker (MITracker), to efficiently integrate multi-view object features and provide stable tracking outcomes. MITracker can track any object in video frames of arbitrary length from arbitrary viewpoints. The key advancements of our method over traditional single-view approaches come from two aspects: (1) MITracker transforms 2D image features into a 3D feature volume and compresses it into a bird's eye view (BEV) plane, facilitating inter-view information fusion; (2) we propose an attention mechanism that leverages geometric information from fused 3D feature volume to refine the tracking results at each view. MITracker outperforms existing methods on the MVTrack and GMTD datasets, achieving state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_MITracker_Multi-View_Integration_for_Visual_Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_MITracker_Multi-View_Integration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Mengjie and Zhu, Yitao and Jiang, Haotian and Li, Jiaming and Shen, Zhenrong and Wang, Sheng and Huang, Haolin and Wang, Xinyu and Zhang, Han and Yang, Qing and Wang, Qian},
    title     = {MITracker: Multi-View Integration for Visual Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27176-27185}
}

raw_affiliation:
[{"affiliation": "School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Biomedical Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Clinical Research and Trial Center", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University", "School of Biomedical Engineering, Shanghai Jiao Tong University", "Shanghai Clinical Research and Trial Center"]


title: Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes
authors: Yiming Dou, Wonseok Oh, Yuqing Luo, Antonio Loquercio, Andrew Owens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1795-1804
affiliations: ["University of Michigan", "University of Pennsylvania"]
cities: ["Ann Arbor", "Philadelphia"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dou_Hearing_Hands_Generating_Sounds_from_Physical_Interactions_in_3D_Scenes_CVPR_2025_paper.html

abstract:
We study the problem of making 3D scene reconstructions interactive by asking the following question: can we predict the sounds of human hands physically interacting with a scene? First, we record a video of a human manipulating objects within a 3D scene using their hands. We then use these action-sound pairs to train a rectified flow model to map 3D hand trajectories to their corresponding audio. At test time, a user can query the model for other actions, parameterized as sequences of hand poses, to estimate their corresponding sounds. In our experiments, we find that our generated sounds accurately convey material properties and actions, and that they are often indistinguishable to human observers from real sounds. Project page: https://www.yimingdou.com/hearing_hands/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dou_Hearing_Hands_Generating_Sounds_from_Physical_Interactions_in_3D_Scenes_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dou_2025_CVPR,
    author    = {Dou, Yiming and Oh, Wonseok and Luo, Yuqing and Loquercio, Antonio and Owens, Andrew},
    title     = {Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1795-1804}
}

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


title: AirRoom: Objects Matter in Room Reidentification
authors: Runmao Yao, Yi Du, Zhuoqun Chen, Haoze Zheng, Chen Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1385-1394
affiliations: ["Spatial AI & Robotics (SAIR) Lab, University at Buffalo"]
cities: ["Buffalo"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_AirRoom_Objects_Matter_in_Room_Reidentification_CVPR_2025_paper.html

abstract:
Room reidentification (ReID) is a challenging yet essential task with numerous applications in fields such as augmented reality (AR) and homecare robotics. Existing visual place recognition (VPR) methods, which typically rely on global descriptors or aggregate local features, often struggle in cluttered indoor environments densely populated with man-made objects. These methods tend to overlook the crucial role of object-oriented information. To address this, we propose AirRoom, an object-aware pipeline that integrates multi-level object-oriented information--from global context to object patches, object segmentation, and keypoints--utilizing a coarse-to-fine retrieval approach. Extensive experiments on four newly constructed datasets--MPReID, HMReID, GibsonReID, and ReplicaReID--demonstrate that AirRoom outperforms state-of-the-art (SOTA) models across nearly all evaluation metrics, with improvements ranging from 6% to 80%. Moreover, AirRoom exhibits significant flexibility, allowing various modules within the pipeline to be substituted with different alternatives without compromising overall performance. It also shows robust and consistent performance under diverse viewpoint variations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_AirRoom_Objects_Matter_in_Room_Reidentification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_AirRoom_Objects_Matter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Runmao and Du, Yi and Chen, Zhuoqun and Zheng, Haoze and Wang, Chen},
    title     = {AirRoom: Objects Matter in Room Reidentification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1385-1394}
}

raw_affiliation:
[{"affiliation": "Spatial AI & Robotics (SAIR) Lab, University at Buffalo", "city": "Buffalo", "country": "USA"}]


title: Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models
authors: Davide Berasi, Matteo Farina, Massimiliano Mancini, Elisa Ricci, Nicola Strisciuglio
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24917-24927
affiliations: ["Fondazione Bruno Kessler", "University of Trento", "University of Twente"]
cities: ["Trento", "Trento", "Enschede"]
countries: ["Italy", "Italy", "Netherlands"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Berasi_Not_Only_Text_Exploring_Compositionality_of_Visual_Representations_in_Vision-Language_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) learn a shared feature space for text and images, enabling the comparison of inputs of different modalities. While prior works demonstrated that VLMs organize natural language representations into regular structures encoding composite meanings, it remains unclear if compositional patterns also emerge in the visual embedding space. In this work, we investigate compositionality in the image domain, where the analysis of compositional properties is challenged by noise and sparsity of visual data. We address these problems and propose a framework, called Geodesically Decomposable Embeddings (GDE), that approximates image representations with geometry-aware compositional structures in the latent space. We demonstrate that visual embeddings of pre-trained VLMs exhibit a compositional arrangement, and evaluate the effectiveness of this property in the tasks of compositional classification and group robustness. GDE achieves stronger performance in compositional classification compared to its counterpart method that assumes linear geometry of the latent space. Notably, it is particularly effective for group robustness, where we achieve higher results than task-specific solutions. Our results indicate that VLMs can automatically develop a human-like form of compositional reasoning in the visual domain, making their underlying processes more interpretable. Code is available at https://github.com/BerasiDavide/vlm_image_compositionality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Berasi_Not_Only_Text_Exploring_Compositionality_of_Visual_Representations_in_Vision-Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Berasi_Not_Only_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Berasi_2025_CVPR,
    author    = {Berasi, Davide and Farina, Matteo and Mancini, Massimiliano and Ricci, Elisa and Strisciuglio, Nicola},
    title     = {Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24917-24927}
}

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


title: DefMamba: Deformable Visual State Space Model
authors: Leiye Liu, Miao Zhang, Jihao Yin, Tingwei Liu, Wei Ji, Yongri Piao, Huchuan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8838-8847
affiliations: ["Dalian University of Technology", "Yale University"]
cities: ["Dalian", "New Haven"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DefMamba_Deformable_Visual_State_Space_Model_CVPR_2025_paper.html

abstract:
Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and performance. However, most existing visual Mamba methods flatten images into 1D sequences using predefined scan orders, which results the model being less capable of utilizing the spatial structural information of the image during the feature extraction process. To address this issue, we proposed a novel visual foundation model called DefMamba. This model includes a multi-scale backbone structure and deformable mamba (DM) blocks, which dynamically adjust the scanning path to prioritize important information, thus enhancing the capture and processing of relevant input features. By combining a deformable scanning (DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details. Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is open source on DefMamba.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DefMamba_Deformable_Visual_State_Space_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_DefMamba_Deformable_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Leiye and Zhang, Miao and Yin, Jihao and Liu, Tingwei and Ji, Wei and Piao, Yongri and Lu, Huchuan},
    title     = {DefMamba: Deformable Visual State Space Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8838-8847}
}

raw_affiliation:
[{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Yale University", "city": "New Haven", "country": "USA"}]


title: HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation
authors: Hongye Cheng, Tianyu Wang, Guangsi Shi, Zexing Zhao, Yanwei Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 906-916
cities: ["Yangling", "Shanghai", "Melbourne", "Yangling", "Shanghai", "Shanghai"]
countries: ["China", "China", "Australia", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_HOP_Heterogeneous_Topology-based_Multimodal_Entanglement_for_Co-Speech_Gesture_Generation_CVPR_2025_paper.html

abstract:
Co-speech gestures are crucial non-verbal cues that enhance speech clarity and expressiveness in human communication, which have attracted increasing attention in multimodal research. While the existing methods have made strides in gesture accuracy, challenges remain in generating diverse and coherent gestures, as most approaches assume independence among multimodal inputs and lack explicit modeling of their interactions. In this work, we propose a novel multimodal learning method named HOP for co-speech gesture generation that captures the heterogeneous entanglement between gesture motion, audio rhythm, and text semantics, enabling the generation of coordinated gestures. By leveraging spatiotemporal graph modeling, we achieve the alignment of audio and action. Moreover, to enhance modality coherence, we build the audio-text semantic representation based on a reprogramming module, which is beneficial for cross-modality adaptation. Our approach enables the trimodal system to learn each other's features and represent them in the form of topological entanglement. Extensive experiments demonstrate that HOP achieves state-of-the-art performance, offering more natural and expressive co-speech gesture generation. More information, codes, and demos are available here: https://star-uu-wang.github.io/HOP/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_HOP_Heterogeneous_Topology-based_Multimodal_Entanglement_for_Co-Speech_Gesture_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_HOP_Heterogeneous_Topology-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Hongye and Wang, Tianyu and Shi, Guangsi and Zhao, Zexing and Fu, Yanwei},
    title     = {HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {906-916}
}

raw_affiliation:
[{"affiliation": "College of Mechanical and Electronic Engineering, Northwest A&F University", "city": "Yangling", "country": "China"}, {"affiliation": "Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "College of Information Engineering, Northwest A&F University", "city": "Yangling", "country": "China"}, {"affiliation": "School of Data Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute of Trustworthy Embodied AI, Fudan University", "city": "Shanghai", "country": "China"}]

affiliations:
["College of Mechanical and Electronic Engineering, Northwest A&F University", "Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University", "Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University", "College of Information Engineering, Northwest A&F University", "School of Data Science, Fudan University", "Institute of Trustworthy Embodied AI, Fudan University"]


title: VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction
authors: Ziyue Zhu, Shenlong Wang, Jin Xie, Jiang-jiang Liu, Jingdong Wang, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6761-6771
cities: ["Tianjin", "Urbana", "Suzhou", "Beijing"]
countries: ["China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_VoxelSplat_Dynamic_Gaussian_Splatting_as_an_Effective_Loss_for_Occupancy_CVPR_2025_paper.html

abstract:
Recent advancements in camera-based occupancy prediction have focused on the simultaneous prediction of 3D semantics and scene flow, a task that presents significant challenges due to specific difficulties, e.g., occlusions and unbalanced dynamic environments. In this paper, we analyze these challenges and their underlying causes. To address them, we propose a novel regularization framework called VoxelSplat. This framework leverages recent developments in 3D Gaussian Splatting to enhance model performance in two key ways: (i)Enhanced Semantics Supervision through 2D Projection: During training, our method decodes sparse semantic 3D Gaussians from 3D representations and projects them onto the 2D camera view. This provides additional supervision signals in the camera-visible space, allowing 2D labels to improve the learning of 3D semantics. (ii) Scene Flow Learning: Our framework uses the predicted scene flow to model the motion of Gaussians, and is thus able to learn the scene flow of moving objects in a self-supervised manner using the labels of adjacent frames. Our method can be seamlessly integrated into various existing occupancy models, enhancingperformance without increasing inference time. Extensive experiments on benchmark datasets demonstrate the effectiveness of VoxelSplat in improving the accuracy of both semantic occupancy and scene flow estimation. The project page and codes are available at https://zzy816.github.io/VoxelSplat-Demo/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_VoxelSplat_Dynamic_Gaussian_Splatting_as_an_Effective_Loss_for_Occupancy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_VoxelSplat_Dynamic_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Ziyue and Wang, Shenlong and Xie, Jin and Liu, Jiang-jiang and Wang, Jingdong and Yang, Jian},
    title     = {VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6761-6771}
}

raw_affiliation:
[{"affiliation": "PCA Lab, VCIP, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "UIUC", "city": "Urbana", "country": "USA"}, {"affiliation": "School of Intelligence Science and Technology, Nanjing University, Suzhou", "city": "Suzhou", "country": "China"}, {"affiliation": "Baidu", "city": "Beijing", "country": "China"}]

affiliations:
["PCA Lab, VCIP, College of Computer Science, Nankai University", "UIUC", "School of Intelligence Science and Technology, Nanjing University, Suzhou", "Baidu"]


title: Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation
authors: Chuandong Liu, Xingxing Weng, Shuguo Jiang, Pengcheng Li, Lei Yu, Gui-Song Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27380-27389
cities: ["Wuhan", "Wuhan", "Haikou", "Wuhan", "Wuhan", "Wuhan"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Exploring_Scene_Affinity_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
This paper explores scene affinity (AIScene), namely intra-scene consistency and inter-scene correlation, for semi-supervised LiDAR semantic segmentation in driving scenes. Adopting teacher-student training, AIScene employs a teacher network to generate pseudo-labeled scenes from unlabeled data, which then supervise the student network's learning. Unlike most methods that include all points in pseudo-labeled scenes for forward propagation but only pseudo-labeled points for backpropagation, AIScene removes points without pseudo-labels, ensuring consistency in both forward and backward propagation within the scene. This simple point erasure strategy effectively prevents unsupervised, semantically ambiguous points (excluded in backpropagation) from affecting the learning of pseudo-labeled points. Moreover, AIScene incorporates patch-based data augmentation, mixing multiple scenes at both scene and instance levels. Compared to existing augmentation techniques that typically perform scene-level mixing between two scenes, our method enhances the semantic diversity of labeled (or pseudo-labeled) scenes, thereby improving the semi-supervised performance of segmentation models. Experiments show that AIScene outperforms previous methods on two popular benchmarks across four settings, achieving notable improvements of 1.9% and 2.1% in the most challenging 1% labeled data. The code will be released at https://github.com/azhuantou/AIScene.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Exploring_Scene_Affinity_for_Semi-Supervised_LiDAR_Semantic_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Chuandong and Weng, Xingxing and Jiang, Shuguo and Li, Pengcheng and Yu, Lei and Xia, Gui-Song},
    title     = {Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27380-27389}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Hainan University", "city": "Haikou", "country": "China"}, {"affiliation": "School of Electronic Information, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "State Key Lab. of LIESMARS, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Institute for Math & AI, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "School of Artificial Intelligence, Wuhan University", "School of Computer Science and Technology, Hainan University", "School of Electronic Information, Wuhan University", "State Key Lab. of LIESMARS, Wuhan University", "Institute for Math & AI, Wuhan University"]


title: ControlFace: Harnessing Facial Parametric Control for Face Rigging
authors: Wooseok Jang, Youngjun Hong, Geonho Cha, Seungryong Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5614-5624
affiliations: ["Korea University", "NA VER Cloud", "KAIST"]
cities: ["Seoul", "Seoul", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_ControlFace_Harnessing_Facial_Parametric_Control_for_Face_Rigging_CVPR_2025_paper.html

abstract:
Manipulation of facial images to meet specific controls such as pose, expression, and lighting, also referred to as face rigging is a complex task in computer vision. Existing methods are limited by their reliance on image datasets, which necessitates individual-specific fine-tuning and limits their ability to retain fine-grained identity and semantic details, reducing practical usability. To overcome these limitations, we introduce ControlFace, a novel face rigging method conditioned on 3DMM renderings that enables flexible, high-fidelity control. ControlFace employs a dual-branch U-Nets: one, referred to as FaceNet, captures identity and fine details, while the other focuses on generation. To enhance control precision, control mixer module encodes the correlated features between the target-aligned control and reference-aligned control, and a novel guidance method, reference control guidance, steers the generation process for better control adherence. By training on a facial video dataset, we fully utilize FaceNet's rich representations while ensuring control adherence. Extensive experiments demonstrate ControlFace's superior performance in identity preservation, and control precision, highlighting its practicality. Code and pre-trained weights will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_ControlFace_Harnessing_Facial_Parametric_Control_for_Face_Rigging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_ControlFace_Harnessing_Facial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Wooseok and Hong, Youngjun and Cha, Geonho and Kim, Seungryong},
    title     = {ControlFace: Harnessing Facial Parametric Control for Face Rigging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5614-5624}
}

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


title: Minority-Focused Text-to-Image Generation via Prompt Optimization
authors: Soobin Um, Jong Chul Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20926-20936
raw_affiliation: [{"affiliation": "KAIST", "city": "Daejeon", "country": "Republic of Korea"}]
affiliations: ["KAIST"]
cities: ["Daejeon"]
countries: ["Republic of Korea"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Um_Minority-Focused_Text-to-Image_Generation_via_Prompt_Optimization_CVPR_2025_paper.html

abstract:
We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined as ones living on low-density regions of text-conditional data distributions. They are valuable for various applications of modern T2I generators, such as data augmentation and creative AI. Unfortunately, existing pretrained T2I diffusion models primarily focus on high-density regions, largely due to the influence of guided samplers (like CFG) that are essential for high-quality generation. To address this, we present a novel framework to counter the high-density-focus of T2I diffusion models. Specifically, we first develop an online prompt optimization framework that encourages emergence of desired properties during inference while preserving semantic contents of user-provided prompts. We subsequently tailor this generic prompt optimizer into a specialized solver that promotes generation of minority features by incorporating a carefully-crafted likelihood objective. Extensive experiments conducted across various types of T2I models demonstrate that our approach significantly enhances the capability to produce high-quality minority instances compared to existing samplers. Code is available at https://github.com/soobin-um/MinorityPrompt.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Um_Minority-Focused_Text-to-Image_Generation_via_Prompt_Optimization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Um_Minority-Focused_Text-to-Image_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Um_2025_CVPR,
    author    = {Um, Soobin and Ye, Jong Chul},
    title     = {Minority-Focused Text-to-Image Generation via Prompt Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20926-20936}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5071-5081
affiliations: ["National University of Defense Technology", "Intelligent Game and Decision Lab"]
cities: ["Changsha", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Imputation-free_and_Alignment-free_Incomplete_Multi-view_Clustering_Driven_by_Consensus_Semantic_CVPR_2025_paper.html

title:
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning

authors:
Yuzhuo Dai, Jiaqi Jin, Zhibin Dong, Siwei Wang, Xinwang Liu, En Zhu, Xihong Yang, Xinbiao Gan, Yu Feng

abstract:
In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-view consistency in paired complete observations, further imputing and aligning the similarity relationships inherently shared across views. Nevertheless, existing methods are constrained by two-tiered limitations: (1) Neither instance- nor cluster-level consistency learning construct a semantic space shared across views to learn consensus semantics. The former enforces cross-view instances alignment, and wrongly regards unpaired observations with semantic consistency as negative pairs; the latter focuses on cross-view cluster counterparts while coarsely handling fine-grained intra-cluster relationships within views. (2) Excessive reliance on consistency results in unreliable imputation and alignment without incorporating view-specific cluster information. Thus, we propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL). To bridge semantic gaps across all observations, we learn consensus prototypes from available data to discover a shared space, where semantically similar observations are pulled closer for consensus semantics learning. To capture semantic relationships within specific views, we design a heuristic graph clustering based on modularity to recover cluster structure with intra-cluster compactness and inter-cluster separation for cluster semantics enhancement. Extensive experiments demonstrate, compared to state-of-the-art competitors, FreeCSL achieves more confident and robust assignments on IMVC task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Imputation-free_and_Alignment-free_Incomplete_Multi-view_Clustering_Driven_by_Consensus_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_Imputation-free_and_Alignment-free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Yuzhuo and Jin, Jiaqi and Dong, Zhibin and Wang, Siwei and Liu, Xinwang and Zhu, En and Yang, Xihong and Gan, Xinbiao and Feng, Yu},
    title     = {Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5071-5081}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Intelligent Game and Decision Lab", "city": "Beijing", "country": "China"}]


title: Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation
authors: Tianran Chen, Jiarui Chen, Baoquan Zhang, Zhehao Yu, Shidong Chen, Rui Ye, Xutao Li, Yunming Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9655-9664
cities: ["Shenzhen", "Guiyang"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Sensitivity-Aware_Efficient_Fine-Tuning_via_Compact_Dynamic-Rank_Adaptation_CVPR_2025_paper.html

abstract:
Parameter-Efficient Fine-Tuning (PEFT) is a fundamental research problem in computer vision, which aims to tune a few of parameters for efficient storage and adaptation of pre-trained vision models. Recently, sensitivity-aware parameter efficient fine-tuning method (SPT) addresses this problem by identifying sensitive parameters and then leveraging its sparse characteristic to combine unstructured and structured tuning for PEFT. However, existing methods only focus on sparse characteristic of sensitive parameters but overlook its distribution characteristic, which results in additional storage burden and limited performance improvement. In this paper, we find that the distribution of sensitive parameters is not chaotic, but concentrates in a small number of rows or columns in each parameter matrix. Inspired by this fact, we propose a Compact Dynamic-Rank Adaptation-based tuning method for Sensitivity-aware Parameter efficient fine-Tuning, called CDRA-SPT. Specifically, we first identify the sensitive parameters that require tuning for each downstream task. Then, we reorganize the sensitive parameters by following its row and column into a compact sub-parameter matrix. Finally, a dynamic-rank adaptation is designed and applied at sub-parameter matrix level for PEFT. Its advantage is that the dynamic-rank characteristic of sub-parameter matrix can be fully exploited for PEFT. Extensive experiments show that our method achieves superior performance over previous state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Sensitivity-Aware_Efficient_Fine-Tuning_via_Compact_Dynamic-Rank_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Sensitivity-Aware_Efficient_Fine-Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Tianran and Chen, Jiarui and Zhang, Baoquan and Yu, Zhehao and Chen, Shidong and Ye, Rui and Li, Xutao and Ye, Yunming},
    title     = {Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9655-9664}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guizhou Power Grid Co., Ltd. Intelligent Operation Center", "city": "Guiyang", "country": "China"}]

affiliations:
["Harbin Institute of Technology, Shenzhen", "Guizhou Power Grid Co., Ltd. Intelligent Operation Center"]


