# papers

title: Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation
authors: Jiho Choi, Seonho Lee, Minhyun Lee, Seungho Lee, Hyunjung Shim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9782-9793
affiliations: ["KAIST", "Samsung Electronics"]
cities: ["Daejeon", "Seoul"]
countries: ["Republic of Korea", "Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_Fine-Grained_Image-Text_Correspondence_with_Cost_Aggregation_for_Open-Vocabulary_Part_Segmentation_CVPR_2025_paper.html

abstract:
Open-Vocabulary Part Segmentation (OVPS) is an emerging field for recognizing fine-grained parts in unseen categories. We identify two primary challenges in OVPS: (1) the difficulty in aligning part-level image-text correspondence, and (2) the lack of structural understanding in segmenting object parts. To address these issues, we propose PartCATSeg, a novel framework that integrates object-aware part-level cost aggregation, compositional loss, and structural guidance from DINO. Our approach employs a disentangled cost aggregation strategy that handles object and part-level costs separately, enhancing the precision of part-level segmentation. We also introduce a compositional loss to better capture part-object relationships, compensating for the limited part annotations. Additionally, structural guidance from DINO features improves boundary delineation and inter-part understanding. Extensive experiments on Pascal-Part-116, ADE20K-Part-234, and PartImageNet datasets demonstrate that our method significantly outperforms state-of-the-art approaches, setting a new baseline for robust generalization to unseen part categories.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_Fine-Grained_Image-Text_Correspondence_with_Cost_Aggregation_for_Open-Vocabulary_Part_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_Fine-Grained_Image-Text_Correspondence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Jiho and Lee, Seonho and Lee, Minhyun and Lee, Seungho and Shim, Hyunjung},
    title     = {Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9782-9793}
}

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


title: NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training
authors: Dar-Yen Chen, Hmrishav Bandyopadhyay, Kai Zou, Yi-Zhe Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7654-7663
affiliations: ["SketchX, CVSSP, University of Surrey", "NetMind.AI"]
cities: ["Guildford", "Unknown"]
countries: ["UK", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_NitroFusion_High-Fidelity_Single-Step_Diffusion_through_Dynamic_Adversarial_Training_CVPR_2025_paper.html

abstract:
We introduce NitroFusion, a fundamentally different approach to single-step diffusion that achieves high-quality generation through a dynamic adversarial framework. While one-step methods offer dramatic speed advantages, they typically suffer from quality degradation compared to their multi-step counterparts. Just as a panel of art critics provides comprehensive feedback by specializing in different aspects like composition, color, and technique, our approach maintains a large pool of specialized discriminator heads that collectively guide the generation process. Each discriminator group develops expertise in specific quality aspects at different noise levels, providing diverse feedback that enables high-fidelity one-step generation. Our framework combines: (i) a dynamic discriminator pool with specialized discriminator groups to improve generation quality, (ii) strategic refresh mechanisms to prevent discriminator overfitting, and (iii) global-local discriminator heads for multi-scale quality assessment, and unconditional/conditional training for balanced generation. Additionally, our framework uniquely supports flexible deployment through bottom-up refinement, allowing users to dynamically choose between 1-4 denoising steps with the same model for direct quality-speed trade-offs. Through comprehensive experiments, we demonstrate that NitroFusion significantly outperforms existing single-step methods across multiple evaluation metrics, particularly excelling in preserving fine details and global consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_NitroFusion_High-Fidelity_Single-Step_Diffusion_through_Dynamic_Adversarial_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_NitroFusion_High-Fidelity_Single-Step_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Dar-Yen and Bandyopadhyay, Hmrishav and Zou, Kai and Song, Yi-Zhe},
    title     = {NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7654-7663}
}

raw_affiliation:
[{"affiliation": "SketchX, CVSSP, University of Surrey", "city": "Guildford", "country": "UK"}, {"affiliation": "NetMind.AI", "city": "Unknown", "country": "Unknown"}]


title: ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12999-13008
cities: ["Shanghai", "Hefei", "Hong Kong", "Shanghai", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bu_ByTheWay_Boost_Your_Text-to-Video_Generation_Model_to_Higher_Quality_in_CVPR_2025_paper.html

authors:
Jiazi Bu, Pengyang Ling, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang

abstract:
The text-to-video (T2V) generation models, offering convenient visual creation, have recently garnered increasing attention. Despite their substantial potential, the generated videos may present artifacts, including structural implausibility, temporal inconsistency, and a lack of motion, often resulting in near-static video. In this work, we have identified a correlation between the disparity of temporal attention maps across different blocks and the occurrence of temporal inconsistencies. Additionally, we have observed that the energy contained within the temporal attention maps is directly related to the magnitude of motion amplitude in the generated videos. Based on these observations, we present ByTheWay, a training-free method to improve the quality of text-to-video generation without introducing additional parameters, augmenting memory or sampling time. Specifically, ByTheWay is composed of two principal components: 1) Temporal Self-Guidance improves the structural plausibility and temporal consistency of generated videos by reducing the disparity between the temporal attention maps across various decoder blocks. 2) Fourier-based Motion Enhancement enhances the magnitude and richness of motion by amplifying the energy of the map. Extensive experiments demonstrate that ByTheWay significantly improves the quality of text-to-video generation with negligible additional cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bu_ByTheWay_Boost_Your_Text-to-Video_Generation_Model_to_Higher_Quality_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bu_ByTheWay_Boost_Your_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bu_2025_CVPR,
    author    = {Bu, Jiazi and Ling, Pengyang and Zhang, Pan and Wu, Tong and Dong, Xiaoyi and Zang, Yuhang and Cao, Yuhang and Lin, Dahua and Wang, Jiaqi},
    title     = {ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12999-13008}
}

raw_affiliation:
[{"affiliation": "SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["SJTU", "USTC", "CUHK", "Shanghai AI Laboratory", "Shanghai Innovation Institute", "CPII under InnoHK"]


title: CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework
authors: Yanlong Xu, Haoxuan Qu, Jun Liu, Wenxiao Zhang, Xun Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6637-6647
affiliations: ["University of Science and Technology of China", "Lancaster University", "Hohai University"]
cities: ["Hefei", "Lancaster", "Nanjing"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_CMMLoc_Advancing_Text-to-PointCloud_Localization_with_Cauchy-Mixture-Model_Based_Framework_CVPR_2025_paper.html

abstract:
The goal of point cloud localization based on linguistic description is to identify a 3D position using textual description in large urban environments, which has potential applications in various fields, such as determining the location for vehicle pickup or goods delivery. Ideally, for a textual description and its corresponding 3D location, the objects around the 3D location should be fully described in the text description. However, in practical scenarios, e.g., vehicle pickup, passengers usually describe only the part of the most significant and nearby surroundings instead of the entire environment. In response to this partially relevant challenge, we propose CMMLoc, an uncertainty-aware Cauchy-Mixture-Model (CMM) based framework for text-to-point-cloud Localization. To model the uncertain semantic relations between text and point cloud, we integrate CMM constraints as a prior during the interaction between the two modalities. We further design a spatial consolidation scheme to enable adaptive aggregation of different 3D objects with varying receptive fields. To achieve precise localization, we propose a cardinal direction integration module alongside a modality pre-alignment strategy, helping capture the spatial relationships among objects and bringing the 3D objects closer to the text modality. Comprehensive experiments validate that CMMLoc outperforms existing methods, achieving state-of-the-art results on the KITTI360Pose dataset. Codes are available in this anonymous GitHub repository https://github.com/anonymous0819/CMMLoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_CMMLoc_Advancing_Text-to-PointCloud_Localization_with_Cauchy-Mixture-Model_Based_Framework_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_CMMLoc_Advancing_Text-to-PointCloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yanlong and Qu, Haoxuan and Liu, Jun and Zhang, Wenxiao and Yang, Xun},
    title     = {CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6637-6647}
}

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


title: Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding
authors: Yan Wang, Baoxiong Jia, Ziyu Zhu, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14125-14136
affiliations: ["State Key Laboratory of General Artificial Intelligence, BIGAI", "Tsinghua University"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Masked_Point-Entity_Contrast_for_Open-Vocabulary_3D_Scene_Understanding_CVPR_2025_paper.html

abstract:
Open-vocabulary 3D scene understanding is pivotal for enhancing physical intelligence, as it enables embodied agents to interpret and interact dynamically within real-world environments. This paper introduces MPEC, a novel Masked Point-Entity Contrastive learning method for open-vocabulary 3D semantic segmentation that leverages both 3D entity-language alignment and point-entity consistency across different point cloud views to foster entity-specific feature representations. Our method improves semantic discrimination and enhances the differentiation of unique instances, achieving state-of-the-art results on ScanNet for open-vocabulary 3D semantic segmentation and demonstrating superior zero-shot scene understanding capabilities. Extensive fine-tuning experiments on 8 datasets, spanning from low-level perception to high-level reasoning tasks, showcase the potential of learned 3D features, driving consistent performance gains across varied 3D scene understanding tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Masked_Point-Entity_Contrast_for_Open-Vocabulary_3D_Scene_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Masked_Point-Entity_Contrast_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yan and Jia, Baoxiong and Zhu, Ziyu and Huang, Siyuan},
    title     = {Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14125-14136}
}

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


title: Decoupling Training-Free Guided Diffusion by ADMM
authors: Youyuan Zhang, Zehua Liu, Zenan Li, Zhaoyu Li, James J. Clark, Xujie Si
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23292-23302
cities: ["Montreal", "Hong Kong", "Nanjing", "Toronto", "Montreal"]
countries: ["Canada", "China", "China", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Decoupling_Training-Free_Guided_Diffusion_by_ADMM_CVPR_2025_paper.html

abstract:
In this paper, we consider the conditional generation problem by guiding off-the-shelf unconditional diffusion models with differentiable loss functions in a plug-and-play fashion. While previous research has primarily focused on balancing the unconditional diffusion model and the guided loss through a tuned weight hyperparameter, we propose a novel framework that distinctly decouples these two components. Specifically, we introduce two variables  x  and  z , to represent the generated samples governed by the unconditional generation model and the guidance function, respectively. This decoupling reformulates conditional generation into two manageable subproblems, unified by the constraint  x  =  z . Leveraging this setup, we develop a new algorithm based on the Alternating Direction Method of Multipliers (ADMM) to adaptively balance these components. Additionally, we establish the equivalence between the diffusion reverse step and the proximal operator of ADMM and provide a detailed convergence analysis of our algorithm under certain mild assumptions. Our experiments demonstrate that our proposed method \OurMethod   consistently generates high-quality samples while ensuring strong adherence to the conditioning criteria. It outperforms existing methods across a range of conditional generation tasks, including image generation with various guidance and controllable motion synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Decoupling_Training-Free_Guided_Diffusion_by_ADMM_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Decoupling_Training-Free_Guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Youyuan and Liu, Zehua and Li, Zenan and Li, Zhaoyu and Clark, James J. and Si, Xujie},
    title     = {Decoupling Training-Free Guided Diffusion by ADMM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23292-23302}
}

raw_affiliation:
[{"affiliation": "McGill University", "city": "Montreal", "country": "Canada"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "CIFAR AI Chair, MILA", "city": "Montreal", "country": "Canada"}]

affiliations:
["McGill University", "The University of Hong Kong", "Nanjing University", "University of Toronto", "CIFAR AI Chair, MILA"]


title: On the Generalization of Handwritten Text Recognition Models
authors: Carlos Garrido-Munoz, Jorge Calvo-Zaragoza
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15275-15286
affiliations: ["Pattern Recognition and Arti\ufb01cial Intelligence Group, University of Alicante"]
cities: ["Alicante"]
countries: ["Spain"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Garrido-Munoz_On_the_Generalization_of_Handwritten_Text_Recognition_Models_CVPR_2025_paper.html

abstract:
Recent advances in Handwritten Text Recognition (HTR) have led to significant reductions in transcription errors on standard benchmarks under the i.i.d. assumption, thus focusing on minimizing in-distribution (ID) errors.However, this assumption does not hold in real-world applications, which has motivated HTR research to explore Transfer Learning and Domain Adaptation techniques. In this work, we investigate the unaddressed limitations of HTR models in generalizing to out-of-distribution (OOD) data. We adopt the challenging setting of Domain Generalization, where models are expected to generalize to OOD data without any prior access. To this end, we analyze 336 OOD cases from eight state-of-the-art HTR models across seven widely used datasets, spanning five languages. Additionally, we study how HTR models leverage synthetic data to generalize. We reveal that the most significant factor for generalization lies in the textual divergence between domains, followed by visual divergence. We demonstrate that the error of HTR models in OOD scenarios can be reliably estimated, with discrepancies falling below 10 points in 70% of cases. We identify the underlying limitations of HTR models, laying the foundation for future research to address this challenge.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Garrido-Munoz_On_the_Generalization_of_Handwritten_Text_Recognition_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garrido-Munoz_On_the_Generalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Garrido-Munoz_2025_CVPR,
    author    = {Garrido-Munoz, Carlos and Calvo-Zaragoza, Jorge},
    title     = {On the Generalization of Handwritten Text Recognition Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15275-15286}
}

raw_affiliation:
[{"affiliation": "Pattern Recognition and Arti\ufb01cial Intelligence Group, University of Alicante", "city": "Alicante", "country": "Spain"}]


title: SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion
authors: Trong-Tung Nguyen, Quang Nguyen, Khoi Nguyen, Anh Tran, Cuong Pham
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21492-21501
raw_affiliation: [{"affiliation": "Qualcomm AI Research", "city": "San Diego", "country": "USA"}]
affiliations: ["Qualcomm AI Research"]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_SwiftEdit_Lightning_Fast_Text-Guided_Image_Editing_via_One-Step_Diffusion_CVPR_2025_paper.html

abstract:
Recent advances in text-guided image editing enable users to perform image edits through simple text inputs, leveraging the extensive priors of multi-step diffusion-based text-to-image models. However, these methods often fall short of the speed demands required for real-world and on-device applications due to the costly multi-step inversion and sampling process involved. In response to this, we introduce SwiftEdit, a simple yet highly efficient editing tool that achieve instant text-guided image editing in 0.23s. The advancement of SwiftEdit lies in its two novel contributions: a one-step inversion framework that enables one-step image reconstruction via inversion and a mask-guided editing technique with our proposed attention rescaling mechanism to perform localized image editing. Extensive experiments are provided to demonstrate the effectiveness and efficiency of SwiftEdit. In particular, SwiftEdit enables instant text-guided image editing, which is extremely faster than previous multi-step methods (at least 50 times faster) while maintain a competitive performance in editing results. Our project is at https://swift-edit.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_SwiftEdit_Lightning_Fast_Text-Guided_Image_Editing_via_One-Step_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_SwiftEdit_Lightning_Fast_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Trong-Tung and Nguyen, Quang and Nguyen, Khoi and Tran, Anh and Pham, Cuong},
    title     = {SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21492-21501}
}


authors: Keqi Chen, Vinkle Srivastav, Didier Mutter, Nicolas Padoy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24419-24428
cities: ["Strasbourg", "Strasbourg", "Strasbourg"]
countries: ["France", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Learning_from_Synchronization_Self-Supervised_Uncalibrated_Multi-View_Person_Association_in_Challenging_CVPR_2025_paper.html

title:
Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes

abstract:
Multi-view person association is a fundamental step towards multi-view analysis of human activities. Although the person re-identification features have been proven effective, they become unreliable in challenging scenes where persons share similar appearances. Therefore, cross-view geometric constraints are required for a more robust association. However, most existing approaches are either fully-supervised using ground-truth identity labels or require calibrated camera parameters that are hard to obtain. In this work, we investigate the potential of learning from synchronization, and propose a self-supervised uncalibrated multi-view person association approach, Self-MVA, without using any annotations. Specifically, we propose a self-supervised learning framework, consisting of an encoder-decoder model and a self-supervised pretext task, cross-view image synchronization, which aims to distinguish whether two images from different views are captured at the same time. The model encodes each person's unified geometric and appearance features, and we train it by utilizing synchronization labels for supervision after applying Hungarian matching to bridge the gap between instance-wise and image-wise distances.  To further reduce the solution space, we propose two types of self-supervised linear constraints: multi-view re-projection and pairwise edge association. Extensive experiments on three challenging public benchmark datasets (WILDTRACK, MVOR, and SOLDIERS) show that our approach achieves state-of-the-art results, surpassing existing unsupervised and fully-supervised approaches. Code is available at https://github.com/CAMMA-public/Self-MVA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Learning_from_Synchronization_Self-Supervised_Uncalibrated_Multi-View_Person_Association_in_Challenging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Learning_from_Synchronization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Keqi and Srivastav, Vinkle and Mutter, Didier and Padoy, Nicolas},
    title     = {Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24419-24428}
}

raw_affiliation:
[{"affiliation": "University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France", "city": "Strasbourg", "country": "France"}, {"affiliation": "IHU Strasbourg, France", "city": "Strasbourg", "country": "France"}, {"affiliation": "University Hospital of Strasbourg, France", "city": "Strasbourg", "country": "France"}]

affiliations:
["University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France", "IHU Strasbourg, France", "University Hospital of Strasbourg, France"]


title: RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network
authors: Van-Tin Luu, Yon-Lin Cai, Vu-Hoang Tran, Wei-Chen Chiu, Yi-Ting Chen, Ching-Chun Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6700-6709
cities: ["Hsinchu", "Ho Chi Minh City"]
countries: ["Taiwan", "Vietnam"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luu_RC-AutoCalib_An_End-to-End_Radar-Camera_Automatic_Calibration_Network_CVPR_2025_paper.html

abstract:
This paper presents a groundbreaking approach - the first online automatic geometric calibration method for radar and camera systems. Given the significant data sparsity and measurement uncertainty in radar height data, achieving automatic calibration during system operation has long been a challenge. To address the sparsity issue, we propose a Dual-Perspective representation that gathers features from both frontal and bird's-eye views. The frontal view contains rich but sensitive height information, whereas the bird's-eye view provides robust features against height uncertainty. We thereby propose a novel Selective Fusion Mechanism to identify and fuse reliable features from both perspectives, reducing the effect of height uncertainty. Moreover, for each view, we incorporate a Multi-Modal Cross-Attention Mechanism to explicitly find location correspondences through cross-modal matching. During the training phase, we also design a Noise-Resistant Matcher to provide better supervision and enhance the robustness of the matching mechanism against sparsity and height uncertainty. Our experimental results, tested on the nuScenes dataset, demonstrate that our method significantly outperforms previous radar-camera auto-calibration methods, as well as existing state-of-the-art LiDAR-camera calibration techniques, establishing a new benchmark for future research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luu_RC-AutoCalib_An_End-to-End_Radar-Camera_Automatic_Calibration_Network_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luu_RC-AutoCalib_An_End-to-End_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luu_2025_CVPR,
    author    = {Luu, Van-Tin and Cai, Yon-Lin and Tran, Vu-Hoang and Chiu, Wei-Chen and Chen, Yi-Ting and Huang, Ching-Chun},
    title     = {RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6700-6709}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University, Taiwan", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "Ho Chi Minh City University of Technology and Education, Vietnam", "city": "Ho Chi Minh City", "country": "Vietnam"}]

affiliations:
["National Yang Ming Chiao Tung University, Taiwan", "Ho Chi Minh City University of Technology and Education, Vietnam"]


title: Argus: A Compact and Versatile Foundation Model for Vision
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4418-4429
affiliations: ["Sony AI", "Sony Semiconductor Solutions", "University of Texas at Austin"]
cities: ["Unknown", "Unknown", "Austin"]
countries: ["Unknown", "Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_Argus_A_Compact_and_Versatile_Foundation_Model_for_Vision_CVPR_2025_paper.html

authors:
Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu

abstract:
While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at deployment time. To address these challenges, we introduce Argus (The name comes from Argus Panoptes -- a hundred-eyed giant with "all-seeing" capability in Greek mythology), a compact and versatile vision foundation model designed to support a wide range of vision tasks through a unified multitask architecture. Argus employs a two-stage training strategy: (i) multitask pretraining over core vision tasks with a shared backbone that includes a lightweight adapter to inject task-specific inductive biases, and (ii) scalable and efficient adaptation to new tasks by fine-tuning only the task-specific decoders. Extensive evaluations demonstrate that Argus, despite its relatively compact and training-efficient design of merely 100M backbone parameters (only 13.6% of which are trained using 1.6M images), competes with and even surpasses much larger models. Compared to state-of-the-art foundation models, Argus not only covers a broader set of vision tasks but also matches or outperforms the models with similar sizes on 12 tasks. We expect that Argus will accelerate the real-world adoption of vision foundation models in resource-constrained scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_Argus_A_Compact_and_Versatile_Foundation_Model_for_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_Argus_A_Compact_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhuang_2025_CVPR,
    author    = {Zhuang, Weiming and Chen, Chen and Li, Zhizhong and Sajadmanesh, Sina and Li, Jingtao and Huang, Jiabo and Sehwag, Vikash and Sharma, Vivek and Shinozaki, Hirotaka and Garcia, Felan Carlo and Zhan, Yihao and Adachi, Naohiro and Eki, Ryoji and Spranger, Michael and Stone, Peter and Lyu, Lingjuan},
    title     = {Argus: A Compact and Versatile Foundation Model for Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4418-4429}
}

raw_affiliation:
[{"affiliation": "Sony AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Sony Semiconductor Solutions", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}]


title: CLIP-driven Coarse-to-fine Semantic Guidance for Fine-grained Open-set Semi-supervised Learning
authors: Xiaokun Li, Yaping Huang, Qingji Guan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30312-30321
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CLIP-driven_Coarse-to-fine_Semantic_Guidance_for_Fine-grained_Open-set_Semi-supervised_Learning_CVPR_2025_paper.html

abstract:
Fine-grained open-set semi-supervised learning (OSSL) investigates a practical scenario where unlabeled data may contain fine-grained out-of-distribution (OOD) samples. Due to the subtle visual differences among in-distribution (ID) samples, as well as between ID and OOD samples, it is extremely challenging to separate the ID and OOD samples. Due to the subtle visual differences among in-distribution (ID) and OOD samples. Recent Vision-Language Models, such as CLIP, have shown excellent generalization capabilities. However, it tends to focus on general attributes, and thus is insufficient to distinguish the fine-grained details. To tackle the issues, in this paper, we propose a novel CLIP-driven coarse-to-fine semantic-guided framework, named CFSG-CLIP, to progressively focus on the distinctive fine-grained clues.  Specifically, CFSG-CLIP comprises a coarse-guidance branch and a fine-guidance branch derived from the pre-trained CLIP model. In the coarse-guidance branch, we design a semantic filtering module to initially filter and highlight local visual features guided by cross-modality features. Then, in the fine-guidance branch, we further design a visual-semantic injection strategy, which embeds category-related visual cues into the visual encoder to further refine the local visual features. By the designed dual-guidance framework, local subtle cues are progressively discovered to distinct the subtle difference between ID and OOD samples. Extensive experiments demonstrate that CFSG-CLIP achieves competitive performance on multiple fine-grained datasets. The source code is available at https://github.com/LxxxxK/CFSG-CLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CLIP-driven_Coarse-to-fine_Semantic_Guidance_for_Fine-grained_Open-set_Semi-supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CLIP-driven_Coarse-to-fine_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiaokun and Huang, Yaping and Guan, Qingji},
    title     = {CLIP-driven Coarse-to-fine Semantic Guidance for Fine-grained Open-set Semi-supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30312-30321}
}

raw_affiliation:
[{"affiliation": "Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University"]


title: InsTaG: Learning Personalized 3D Talking Head from Few-Second Video
authors: Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Jun Zhou, Lin Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10690-10700
cities: ["Beijing", "Brisbane", "Wako", "Tokyo"]
countries: ["China", "Australia", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_InsTaG_Learning_Personalized_3D_Talking_Head_from_Few-Second_Video_CVPR_2025_paper.html

abstract:
Despite exhibiting impressive performance in synthesizing lifelike personalized 3D talking heads, prevailing methods based on radiance fields suffer from high demands for training data and time for each new identity. This paper introduces InsTaG, a 3D talking head synthesis framework that allows a fast learning of realistic personalized 3D talking head from few training data. Built upon a lightweight 3DGS person-specific synthesizer with universal motion priors, InsTaG achieves high-quality and fast adaptation while preserving high-level personalization and efficiency. As preparation, we first propose an Identity-Free Pre-training strategy that enables the pre-training of the person-specific model and encourages the collection of universal motion priors from long-video data corpus. To fully exploit the universal motion priors to learn an unseen new identity, we then present a Motion-Aligned Adaptation strategy to adaptively align the target head to the pre-trained field, and constrain a robust dynamic head structure under few training data. Experiments demonstrate our outstanding performance and efficiency under various data scenarios to render high-quality personalized talking heads. Project page: https://fictionarry.github.io/InsTaG/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_InsTaG_Learning_Personalized_3D_Talking_Head_from_Few-Second_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_InsTaG_Learning_Personalized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiahe and Zhang, Jiawei and Bai, Xiao and Zheng, Jin and Zhou, Jun and Gu, Lin},
    title     = {InsTaG: Learning Personalized 3D Talking Head from Few-Second Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10690-10700}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, State Key Laboratory of Complex & Critical Software Environment, Jiangxi Research Institute, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Information and Communication Technology, Griffith University", "city": "Brisbane", "country": "Australia"}, {"affiliation": "RIKEN AIP", "city": "Wako", "country": "Japan"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}]

affiliations:
["School of Computer Science and Engineering, State Key Laboratory of Complex & Critical Software Environment, Jiangxi Research Institute, Beihang University", "School of Information and Communication Technology, Griffith University", "RIKEN AIP", "The University of Tokyo"]


title: Sampling Innovation-Based Adaptive Compressive Sensing
authors: Zhifu Tian, Tao Hu, Chaoyang Niu, Di Wu, Shu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2387-2397
raw_affiliation: [{"affiliation": "Information Engineering University", "city": "Zhengzhou", "country": "China"}]
affiliations: ["Information Engineering University"]
cities: ["Zhengzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Sampling_Innovation-Based_Adaptive_Compressive_Sensing_CVPR_2025_paper.html

abstract:
Scene-aware Adaptive Compressive Sensing (ACS) has attracted significant interest due to its promising capability for efficient and high-fidelity acquisition of scene images. ACS typically prescribes adaptive sampling allocation (ASA) based on previous samples in the absence of ground truth. However, when confronting unknown scenes, existing ACS methods often lack accurate judgment and robust feedback mechanisms for ASA, thus limiting the high-fidelity sensing of the scene. In this paper, we introduce a Sampling Innovation-Based ACS (SIB-ACS) method that can effectively identify and allocate sampling to challenging image reconstruction areas, culminating in high-fidelity image reconstruction. An innovation criterion is proposed to judge ASA by predicting the decrease in image reconstruction error attributable to sampling increments, thereby directing more samples towards regions where the reconstruction error diminishes significantly. A sampling innovation-guided multi-stage adaptive sampling (AS) framework is proposed, which iteratively refines the ASA through a multi-stage feedback process. For image reconstruction, we propose a Principal Component Compressed Domain Network (PCCD-Net), which efficiently and faithfully reconstructs images under AS scenarios. Extensive experiments demonstrate that the proposed SIB-ACS method significantly outperforms the state-of-the-art methods in terms of image reconstruction fidelity and visual effects. Codes are available at https://github.com/giant-pandada/SIB-ACS_CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Sampling_Innovation-Based_Adaptive_Compressive_Sensing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Sampling_Innovation-Based_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Zhifu and Hu, Tao and Niu, Chaoyang and Wu, Di and Wang, Shu},
    title     = {Sampling Innovation-Based Adaptive Compressive Sensing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2387-2397}
}


title: A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
authors: Yunlu Yan, Huazhu Fu, Yuexiang Li, Jinheng Xie, Jun Ma, Guang Yang, Lei Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25749-25758
cities: ["Guangzhou", "Singapore", "Nanning", "Singapore", "London", "Hong Kong"]
countries: ["China", "Singapore", "China", "Singapore", "United Kingdom", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.html

abstract:
Federated Learning (FL) facilitates collaborative learning among multiple clients in a distributed manner and ensures the security of privacy. However, its performance inevitably degrades with non-Independent and Identically Distributed (non-IID) data. In this paper, we focus on the feature distribution skewed FL scenario, a common non-IID situation in real-world applications where data from different clients exhibit varying underlying distributions.  This variation leads to feature shift, which is a key issue of this scenario.  While previous works have made notable progress, few pay attention to the data itself, i.e., the root of this issue. The primary goal of this paper is to mitigate feature shift from the perspective of data. To this end, we propose a simple yet remarkably effective input-level data augmentation method, namely FedRDN, which randomly injects the statistical information of the local distribution from the entire federation into the client's data. This is beneficial to improve the generalization of local feature representations, thereby mitigating feature shift. Moreover, our FedRDN is a plug-and-play component, which can be seamlessly integrated into the data augmentation flow with only a few lines of code. Extensive experiments on several datasets show that the performance of various representative FL methods can be further improved by integrating our FedRDN, demonstrating its effectiveness, strong compatibility and generalizability. Code is available at https://github.com/IAMJackYan/FedRDN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_A_Simple_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Yunlu and Fu, Huazhu and Li, Yuexiang and Xie, Jinheng and Ma, Jun and Yang, Guang and Zhu, Lei},
    title     = {A Simple Data Augmentation for Feature Distribution Skewed Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25749-25758}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "IHPC, A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Guangxi Medical University", "city": "Nanning", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Hong Kong University of Science and Technology (Guangzhou)", "IHPC, A*STAR", "Guangxi Medical University", "National University of Singapore", "Imperial College London", "The Hong Kong University of Science and Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8450-8460
affiliations: ["Tsinghua University", "Zhipu AI"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_MotionBench_Benchmarking_and_Improving_Fine-grained_Video_Motion_Understanding_for_Vision_CVPR_2025_paper.html

title:
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models

authors:
Wenyi Hong, Yean Cheng, Zhuoyi Yang, Weihan Wang, Lefan Wang, Xiaotao Gu, Shiyu Huang, Yuxiao Dong, Jie Tang

abstract:
In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs in processing sequential visual data is still insufficiently explored, highlighting the absence of a comprehensive, high-quality assessment of their performance. In this paper, we introduce Video-MME, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis. Our work distinguishes from existing benchmarks through four key features: 1) Diversity in video types, spanning 6 primary visual domains with 30 subfields to ensure broad scenario generalizability; 2) Duration in temporal dimension, encompassing both short-, medium-, and long-term videos, ranging from 11 seconds to 1 hour, for robust contextual dynamics; 3) Breadth in data modalities, integrating multi-modal inputs besides video frames, including sub- titles and audios, to unveil the all-round capabilities of MLLMs; 4) Quality in annotations, utilizing rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment. 900 videos with a total of 254 hours are manually selected and annotated by repeatedly viewing all the video content, resulting in 2,700 question-answer pairs. With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1.5 Pro, as well as open-source image models like InternVL-Chat-V1.5 and video models like LLaVA-NeXT-Video. Our experiments reveal that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models with an average accuracy of 75%, compared to 71.9% for GPT-4o. The results also demonstrate that Video-MME is a universal benchmark, which applies to both image and video MLLMs. Further analysis indicates that subtitle and audio information could significantly enhance video understanding. Besides, a decline in MLLM performance is observed as video duration increases for all models. Our dataset along with these findings underscores the need for further improvements in handling longer sequences and multi-modal data, shedding light on future MLLM development. Project page: https://video-mme.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_MotionBench_Benchmarking_and_Improving_Fine-grained_Video_Motion_Understanding_for_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_MotionBench_Benchmarking_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Wenyi and Cheng, Yean and Yang, Zhuoyi and Wang, Weihan and Wang, Lefan and Gu, Xiaotao and Huang, Shiyu and Dong, Yuxiao and Tang, Jie},
    title     = {MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8450-8460}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhipu AI", "city": "Beijing", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19618-19627
affiliations: ["University of Science and Technology of China", "Ant Group", "Northeastern University, China"]
cities: ["Hefei", "Hangzhou", "Shenyang"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Benchmarking_Large_Vision-Language_Models_via_Directed_Scene_Graph_for_Comprehensive_CVPR_2025_paper.html

title:
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning

authors:
Fan Lu, Wei Wu, Kecheng Zheng, Shuailei Ma, Biao Gong, Jiawei Liu, Wei Zhai, Yang Cao, Yujun Shen, Zheng-Jun Zha

abstract:
Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have developed benchmarks specifically tailored for detailed captions to measure their accuracy and comprehensiveness. In this paper, we introduce a detailed caption benchmark, termed as CompreCap, to evaluate the visual context from a directed scene graph view. Concretely, we first manually segment the image into semantically meaningful regions (i.e., semantic segmentation mask) according to common-object vocabulary, while also distinguishing attributes of objects within all those regions. Then directional relation labels of these objects are annotated to compose a directed scene graph that can well encode rich compositional information of the image. Based on our directed scene graph, we develop a pipeline to assess the generated detailed captions from LVLMs on multiple levels, including the object-level coverage, the accuracy of attribute descriptions, the score of key relationships, etc. Experimental results on the CompreCap dataset confirm that our evaluation method aligns closely with human evaluation scores across LVLMs. We will release the code and the dataset to support the community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Benchmarking_Large_Vision-Language_Models_via_Directed_Scene_Graph_for_Comprehensive_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Fan and Wu, Wei and Zheng, Kecheng and Ma, Shuailei and Gong, Biao and Liu, Jiawei and Zhai, Wei and Cao, Yang and Shen, Yujun and Zha, Zheng-Jun},
    title     = {Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19618-19627}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Northeastern University, China", "city": "Shenyang", "country": "China"}]


title: ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7952-7962
affiliations: ["Microsoft Research Asia", "Tsinghua University", "Peking University", "USTC"]
cities: ["Beijing", "Beijing", "Beijing", "Hefei"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pu_ART_Anonymous_Region_Transformer_for_Variable_Multi-Layer_Transparent_Image_Generation_CVPR_2025_paper.html

authors:
Yifan Pu, Yiming Zhao, Zhicong Tang, Ruihong Yin, Haoxing Ye, Yuhui Yuan, Dong Chen, Jianmin Bao, Sirui Zhang, Yanbin Wang, Lin Liang, Lijuan Wang, Ji Li, Xiu Li, Zhouhui Lian, Gao Huang, Baining Guo

abstract:
Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pu_ART_Anonymous_Region_Transformer_for_Variable_Multi-Layer_Transparent_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pu_ART_Anonymous_Region_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pu_2025_CVPR,
    author    = {Pu, Yifan and Zhao, Yiming and Tang, Zhicong and Yin, Ruihong and Ye, Haoxing and Yuan, Yuhui and Chen, Dong and Bao, Jianmin and Zhang, Sirui and Wang, Yanbin and Liang, Lin and Wang, Lijuan and Li, Ji and Li, Xiu and Lian, Zhouhui and Huang, Gao and Guo, Baining},
    title     = {ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7952-7962}
}

raw_affiliation:
[{"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}]


title: Rotation-Equivariant Self-Supervised Method in Image Denoising
authors: Hanze Liu, Jiahong Fu, Qi Xie, Deyu Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12720-12730
cities: ["Xi\u2019an", "Shenzhen", "Taipa"]
countries: ["China", "China", "Macao"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Rotation-Equivariant_Self-Supervised_Method_in_Image_Denoising_CVPR_2025_paper.html

abstract:
Self-supervised image denoising methods have garnered significant research attention in recent years, for this kind of method reduces the requirement of large training datasets.Compared to supervised methods, self-supervised methods rely more on the prior embedded in deep networks themselves. As a result, most of the self-supervised methods are designed with Convolution Neural Networks(CNNs) architectures, which well capture one of the most important image prior, translation equivariant prior. Inspired by the great success achieved by the introduction of translational equivariance, in this paper, we explore the way to further incorporate another important image prior. Specifically, we first apply high-accuracy rotation equivariant convolution to self-supervised image denoising. Through rigorous theoretical analysis, we have proved that simply replacing all the convolution layers with rotation equivariant convolution layers would modify the network into its rotation equivariant version.To the best of our knowledge, this is the first time that rotation equivariant image prior is introduced to self-supervised image denoising at the network architecture level with a comprehensive theoretical analysis of equivariance errors, whichoffers a new perspective to the field of self-supervised image denoising.Moreover, to further improve the performance, we design a new mask mechanism to fusion the output of rotation equivariant network and vanilla CNN-based network, and construct an adaptive rotation equivariant framework. Through extensive experiments on three typical methods, we have demonstrated the effectiveness of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Rotation-Equivariant_Self-Supervised_Method_in_Image_Denoising_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Rotation-Equivariant_Self-Supervised_Method_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hanze and Fu, Jiahong and Xie, Qi and Meng, Deyu},
    title     = {Rotation-Equivariant Self-Supervised Method in Image Denoising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12720-12730}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi\u2019an", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Macau University of Science and Technology", "city": "Taipa", "country": "Macao"}]

affiliations:
["Xi\u2019an Jiaotong University", "Pengcheng Laboratory", "Macau University of Science and Technology"]


title: ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points
authors: Qirui Huang, Runze Zhang, Kangjun Liu, Minglun Gong, Hao Zhang, Hui Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6563-6572
raw_affiliation: [{"affiliation": "ArcPro", "city": "Unknown", "country": "Unknown"}]
affiliations: ["ArcPro"]
cities: ["Unknown"]
countries: ["Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_ArcPro_Architectural_Programs_for_Structured_3D_Abstraction_of_Sparse_Points_CVPR_2025_paper.html

abstract:
We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically, we design a domain-specific language (DSL) to hierarchically represent building structures as a program, which can be efficiently converted into a mesh. We bridge feedforward and inverse procedural modeling by using a feedforward process for training data synthesis, allowing the network to make reverse predictions. We train an encoder-decoder on the points-program pairs to establish a mapping from unstructured point clouds to architectural programs, where a 3D convolutional encoder extracts point cloud features and a transformer decoder autoregressively predicts the programs in a tokenized form. Inference by our method is highly efficient and produces plausible and faithful 3D abstractions. Comprehensive experiments demonstrate that ArcPro outperforms both traditional architectural proxy reconstruction and learning-based abstraction methods. We further explore its potential when working with multi-view image and natural language inputs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_ArcPro_Architectural_Programs_for_Structured_3D_Abstraction_of_Sparse_Points_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_ArcPro_Architectural_Programs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Qirui and Zhang, Runze and Liu, Kangjun and Gong, Minglun and Zhang, Hao and Huang, Hui},
    title     = {ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6563-6572}
}


title: GLane3D: Detecting Lanes with Graph of 3D Keypoints
authors: Halil İbrahim Öztürk, Muhammet Esat Kalfaoğlu, Ozsel Kilinc
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27508-27518
raw_affiliation: [{"affiliation": "Togg/Trutek AI Team", "city": "Istanbul", "country": "Turkey"}]
affiliations: ["Togg/Trutek AI Team"]
cities: ["Istanbul"]
countries: ["Turkey"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ozturk_GLane3D_Detecting_Lanes_with_Graph_of_3D_Keypoints_CVPR_2025_paper.html

abstract:
Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorithms. Considering the extensive variation in lane structures worldwide, achieving high generalization capacity is particularly challenging, as algorithms must accurately identify a wide variety of lane patterns worldwide. Traditional top-down approaches rely heavily on learning lane characteristics from training datasets, often struggling with lanes exhibiting previously unseen attributes. To address this generalization limitation, we propose a method that detects keypoints of lanes and subsequently predicts sequential connections between them to construct complete 3D lanes. Each key point is essential for maintaining lane continuity, and we predict multiple proposals per keypoint by allowing adjacent grids to predict the same keypoint using an offset mechanism. PointNMS is employed to eliminate overlapping proposal keypoints, reducing redundancy in the estimated BEV graph and minimizing computational overhead from connection estimations. Our model surpasses previous state-of-the-art methods on both the Apollo and OpenLane datasets, demonstrating superior F1 scores and a strong generalization capacity when models trained on OpenLane are evaluated on the Apollo dataset, compared to prior approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ozturk_GLane3D_Detecting_Lanes_with_Graph_of_3D_Keypoints_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ozturk_GLane3D_Detecting_Lanes_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ozturk_2025_CVPR,
    author    = {\"Ozt\"urk, Halil \.Ibrahim and Kalfao\u{g}lu, Muhammet Esat and Kilinc, Ozsel},
    title     = {GLane3D: Detecting Lanes with Graph of 3D Keypoints},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27508-27518}
}


title: Minimal Interaction Seperated Tuning: A New Paradigm for Visual Adaptation
authors: Ningyuan Tang, Minghao Fu, Jianxin Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25208-25217
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Minimal_Interaction_Seperated_Tuning_A_New_Paradigm_for_Visual_Adaptation_CVPR_2025_paper.html

abstract:
The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on devices with low computational resources. We explore a new visual adaptation paradigm called separated tuning, which treats large pretrained models as standalone feature extractors that run on powerful cloud servers. The fine-tuning carries out on devices which possess only low computational resources (slow CPU, no GPU, small memory, etc.) Existing methods that are potentially suitable for our separated tuning paradigm are discussed. But, three major drawbacks hinder their application in separated tuning: low adaptation capability, large adapter network, and in particular, high information transfer overhead. To address these issues, we propose Minimal Interaction Separated Tuning, or MIST, which reveals that the sum of intermediate features from pretrained models not only has minimal information transfer but also has high adaptation capability. With a lightweight attention-based adaptor network, MIST achieves information transfer efficiency, parameter efficiency, computational and memory efficiency, and at the same time demonstrates competitive results on various visual adaptation benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Minimal_Interaction_Seperated_Tuning_A_New_Paradigm_for_Visual_Adaptation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Ningyuan and Fu, Minghao and Wu, Jianxin},
    title     = {Minimal Interaction Seperated Tuning: A New Paradigm for Visual Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25208-25217}
}

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

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


title: Hardware-Rasterized Ray-Based Gaussian Splatting
authors: Samuel Rota Bulò, Nemanja Bartolovic, Lorenzo Porzi, Peter Kontschieder
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 485-494
raw_affiliation: [{"affiliation": "Meta Reality Labs", "city": "Z\u00fcrich", "country": "Switzerland"}]
affiliations: ["Meta Reality Labs"]
cities: ["Z\u00fcrich"]
countries: ["Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bulo_Hardware-Rasterized_Ray-Based_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
We present a novel, hardware-rasterized rendering approach for ray-based 3D Gaussian Splatting (RayGS), obtaining both fast and high-quality results for novel view synthesis. Our work contains a mathematically rigorous and geometrically intuitive derivation about how to efficiently estimate all relevant quantities for rendering RayGS models, structured with respect to standard hardware rasterization shaders. Our solution is the first enabling rendering RayGS models at sufficiently high frame rates to support quality-sensitive applications like Virtual and Mixed Reality. Our second contribution enables alias-free rendering for RayGS, by addressing MIP-related issues arising when rendering diverging scales during training and testing. We demonstrate significant performance gains, across different benchmark scenes, while retaining state-of-the-art appearance quality of RayGS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bulo_Hardware-Rasterized_Ray-Based_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bulo_Hardware-Rasterized_Ray-Based_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bulo_2025_CVPR,
    author    = {Bul\`o, Samuel Rota and Bartolovic, Nemanja and Porzi, Lorenzo and Kontschieder, Peter},
    title     = {Hardware-Rasterized Ray-Based Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {485-494}
}


title: FlashSloth : Lightning Multimodal Large Language Models via Embedded Visual Compression
authors: Bo Tong, Bokai Lai, Yiyi Zhou, Gen Luo, Yunhang Shen, Ke Li, Xiaoshuai Sun, Rongrong Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14570-14581
cities: ["Xiamen", "Unknown", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tong_FlashSloth__Lightning_Multimodal_Large_Language_Models_via_Embedded_Visual_CVPR_2025_paper.html

abstract:
Despite a big leap forward in capability, multimodal large language models (MLLMs) tend to behave like a sloth in practical use, i.e., slow response and large latency. Recent efforts are devoted to building tiny MLLMs for better efficiency, but the plethora of visual tokens still used limit their actual speedup. In this paper, we propose a powerful and fast tiny MLLM called FlashSloth. Different from previous efforts, FlashSloth focuses on improving the descriptive power of visual tokens in the process of compressing their redundant semantics. In particular, FlashSloth introduces embedded visual compression designs to capture both visually salient and instruction-related image information, so as to achieving superior multimodal performance with fewer visual tokens. Extensive experiments are conducted to validate the proposed FlashSloth, and a bunch of tiny but strong MLLMs are also comprehensively compared, e.g., InternVL-2, MiniCPM-V2 and Qwen2-VL. The experimental results show that compared with these advanced tiny MLLMs, our FlashSloth can greatly reduce the number of visual tokens, training memory and computation complexity while retaining high performance on various VL tasks. Our code is released at: https://github.com/codefanw/FlashSloth.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tong_FlashSloth__Lightning_Multimodal_Large_Language_Models_via_Embedded_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tong_FlashSloth__Lightning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tong_2025_CVPR,
    author    = {Tong, Bo and Lai, Bokai and Zhou, Yiyi and Luo, Gen and Shen, Yunhang and Li, Ke and Sun, Xiaoshuai and Ji, Rongrong},
    title     = {FlashSloth : Lightning Multimodal Large Language Models via Embedded Visual Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14570-14581}
}

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

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


title: FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
authors: Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8775-8785
raw_affiliation: [{"affiliation": "Clemson University", "city": "Clemson", "country": "USA"}]
affiliations: ["Clemson University"]
cities: ["Clemson"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.html

abstract:
Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the frequency domain, termed spectral bias, where detectors overly rely on specific frequency bands, restricting their ability to generalize across unseen forgeries. To address this, we propose FreqDebias, a frequency debiasing framework that mitigates spectral bias through two complementary strategies. First, we introduce a novel Forgery Mixup (Fo-Mixup) augmentation, which dynamically diversifies frequency characteristics of training samples. Second, we incorporate a dual consistency regularization (CR), which enforces both local consistency using class activation maps (CAMs) and global consistency through a von Mises-Fisher (vMF) distribution on a hyperspherical embedding space. This dual CR mitigates over-reliance on certain frequency components by promoting consistent representation learning under both local and global supervision. Extensive experiments show that FreqDebias significantly enhances cross-domain generalization and outperforms state-of-the-art methods in both cross-domain and in-domain settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kashiani_FreqDebias_Towards_Generalizable_Deepfake_Detection_via_Consistency-Driven_Frequency_Debiasing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kashiani_FreqDebias_Towards_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kashiani_2025_CVPR,
    author    = {Kashiani, Hossein and Talemi, Niloufar Alipour and Afghah, Fatemeh},
    title     = {FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8775-8785}
}


title: Multi-subject Open-set Personalization in Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6099-6110
affiliations: ["Snap Inc.", "UC Merced", "CMU"]
cities: ["Santa Monica", "Merced", "Pittsburgh"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Multi-subject_Open-set_Personalization_in_Video_Generation_CVPR_2025_paper.html

authors:
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Yuwei Fang, Kwot Sin Lee, Ivan Skorokhodov, Kfir Aberman, Jun-Yan Zhu, Ming-Hsuan Yang, Sergey Tulyakov

abstract:
Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, require time-consuming optimization per subject, or support only a single subject. We present Video Alchemist--a video model with built-in multi-subject, open-set personalization capabilities for both foreground objects and background, eliminating the need for time-consuming test-time optimization. Our model is built on a new Diffusion Transformer module that fuses each conditional reference image and its corresponding subject-level text prompt with cross-attention layers. Developing such a large model presents two main challenges: dataset and evaluation. First, as paired datasets of reference images and videos are extremely hard to collect, we sample selected video frames as reference images and synthesize a clip of the target video. However, while models can easily denoise training videos given reference frames, they fail to generalize to new contexts. To mitigate this issue, we design a new automatic data construction pipeline with extensive image augmentations. Second, evaluating open-set video personalization is a challenge in itself. To address this, we introduce a personalization benchmark that focuses on accurate subject fidelity and supports diverse personalization scenarios. Finally, our extensive experiments show that our method significantly outperforms existing personalization methods in both quantitative and qualitative evaluations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Multi-subject_Open-set_Personalization_in_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Multi-subject_Open-set_Personalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Tsai-Shien and Siarohin, Aliaksandr and Menapace, Willi and Fang, Yuwei and Lee, Kwot Sin and Skorokhodov, Ivan and Aberman, Kfir and Zhu, Jun-Yan and Yang, Ming-Hsuan and Tulyakov, Sergey},
    title     = {Multi-subject Open-set Personalization in Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6099-6110}
}

raw_affiliation:
[{"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "USA"}, {"affiliation": "UC Merced", "city": "Merced", "country": "USA"}, {"affiliation": "CMU", "city": "Pittsburgh", "country": "USA"}]


title: Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
authors: Hao Li, Ju Dai, Xin Zhao, Feng Zhou, Junjun Pan, Lei Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 183-192
cities: ["Beijing", "Shenzhen", "Beijing", "Seattle", "Copenhagen"]
countries: ["China", "China", "China", "USA", "Denmark"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Wav2Sem_Plug-and-Play_Audio_Semantic_Decoupling_for_3D_Speech-Driven_Facial_Animation_CVPR_2025_paper.html

abstract:
In 3D speech-driven facial animation generation, existing methods commonly employ pre-trained self-supervised audio models as encoders. However, due to the prevalence of phonetically similar syllables with distinct lip shapes in language, these near-homophone syllables tend to exhibit significant coupling in self-supervised audio feature spaces, leading to the averaging effect in subsequent lip motion generation. To address this issue, this paper proposes a plug-and-play semantic decorrelation module--Wav2Sem. This module extracts semantic features corresponding to the entire audio sequence, leveraging the added semantic information to decorrelate audio encodings within the feature space, thereby achieving more expressive audio features. Extensive experiments across multiple Speech-driven models indicate that the Wav2Sem module effectively decouples audio features, significantly alleviating the averaging effect of phonetically similar syllables in lip shape generation, thereby enhancing the precision and naturalness of facial animations. Our source code is available at https://github.com/wslh852/Wav2Sem.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Wav2Sem_Plug-and-Play_Audio_Semantic_Decoupling_for_3D_Speech-Driven_Facial_Animation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hao and Dai, Ju and Zhao, Xin and Zhou, Feng and Pan, Junjun and Li, Lei},
    title     = {Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {183-192}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "North China University of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Copenhagen", "city": "Copenhagen", "country": "Denmark"}]

affiliations:
["Beihang University", "Peng Cheng Laboratory", "North China University of Technology", "University of Washington", "University of Copenhagen"]


title: Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning
authors: Li-Jun Zhao, Zhen-Duo Chen, Yongxin Wang, Xin Luo, Xin-Shun Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25657-25666
cities: ["Jinan", "Jinan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Attraction_Diminishing_and_Distributing_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn novel classes with limited samples after pre-training on a set of base classes. To avoid catastrophic forgetting and overfitting, most FSCIL methods first train the model on the base classes and then freeze the feature extractor in the incremental sessions. However, the reliance on nearest neighbor classification makes FSCIL prone to the hubness phenomenon, which negatively impacts performance in this dynamic and open scenario. While recent methods attempt to adapt to the dynamic and open nature of FSCIL, they are often limited to biased optimizations to the feature space. In this paper, we pioneer the theoretical analysis of the inherent hubness in FSCIL. To mitigate the negative effects of hubness, we propose a novel Attraction Diminishing and Distributing (D2A) method from the essential perspectives of distance metric and feature space. Extensive experimental results demonstrate that our method can broadly and significantly improve the performance of existing methods.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Attraction_Diminishing_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Li-Jun and Chen, Zhen-Duo and Wang, Yongxin and Luo, Xin and Xu, Xin-Shun},
    title     = {Attraction Diminishing and Distributing for Few-Shot Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25657-25666}
}

raw_affiliation:
[{"affiliation": "School of Software, Shandong University, China", "city": "Jinan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Shandong Jianzhu University, China", "city": "Jinan", "country": "China"}]

affiliations:
["School of Software, Shandong University, China", "School of Computer Science and Technology, Shandong Jianzhu University, China"]


title: 4DTAM: Non-Rigid Tracking and Mapping via Dynamic Surface Gaussians
authors: Hidenobu Matsuki, Gwangbin Bae, Andrew J. Davison
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26921-26932
affiliations: ["Dyson Robotics Laboratory, Imperial College London"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Matsuki_4DTAM_Non-Rigid_Tracking_and_Mapping_via_Dynamic_Surface_Gaussians_CVPR_2025_paper.html

abstract:
We propose the first 4D tracking and mapping method that jointly performs camera localization and non-rigid surface reconstruction via differentiable rendering. Our approach captures 4D scenes from an online stream of color images with depth measurements or predictions by simultaneously optimizing scene geometry, appearance, dynamics, and camera ego-motion. Although natural environments exhibit complex non-rigid motions, 4D-SLAM remains relatively underexplored due to its inherent challenges; even with 2.5D signals, the problem is ill-posed because of the high dimensionality of the optimization space. To overcome these challenges, we first introduce a SLAM method based on Gaussian surface primitives that leverages depth signals more effectively than 3D Gaussians, thereby achieving accurate surface reconstruction. To further model non-rigid deformations, we employ a warp-field represented by a multi-layer perceptron (MLP) and introduce a novel camera pose estimation technique along with surface regularization terms that facilitate spatio-temporal reconstruction. In addition to these algorithmic challenges, a significant hurdle in 4D-SLAM research is the lack of publicly available datasets with reliable ground truth and evaluation protocols. To address this, we present a novel synthetic dataset of everyday objects with diverse motions, leveraging large-scale object models and animation modeling. In summary, we open up the modern 4D-SLAM research by introducing a novel method and evaluation protocols grounded in modern vision and rendering techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Matsuki_4DTAM_Non-Rigid_Tracking_and_Mapping_via_Dynamic_Surface_Gaussians_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Matsuki_4DTAM_Non-Rigid_Tracking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Matsuki_2025_CVPR,
    author    = {Matsuki, Hidenobu and Bae, Gwangbin and Davison, Andrew J.},
    title     = {4DTAM: Non-Rigid Tracking and Mapping via Dynamic Surface Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26921-26932}
}

raw_affiliation:
[{"affiliation": "Dyson Robotics Laboratory, Imperial College London", "city": "London", "country": "UK"}]


title: T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving
authors: Changsheng Lv, Mengshi Qi, Liang Liu, Huadong Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17197-17206
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lv_T2SG_Traffic_Topology_Scene_Graph_for_Topology_Reasoning_in_Autonomous_CVPR_2025_paper.html

abstract:
Understanding the traffic scenes and then generating high-definition (HD) maps present significant challenges in autonomous driving. In this paper, we defined a novel  \underline T raffic \underline T opology \underline S cene \underline G raph  (\text T ^2\text SG ), a unified scene graph explicitly modeling the lane, controlled and guided by different road signals ( e.g. , right turn), and topology relationships among them, which is always ignored by previous high-definition (HD) mapping methods. For the generation of \text T ^2\text SG , we propose TopoFormer, a novel one-stage  \underline Topo logy Scene Graph Trans\underline Former   with two newly-designed layers. Specifically, TopoFormer incorporates a Lane Aggregation Layer (LAL) that leverages the geometric distance among the centerline of lanes to guide the aggregation of global information. Furthermore, we proposed a Counterfactual Intervention Layer (CIL) to model the reasonable road structure ( e.g. , intersection, straight) among lanes under counterfactual intervention. Then the generated \text T ^2\text SG  can provide a more accurate and explainable description of the topological structure in traffic scenes. Experimental results demonstrate that TopoFormer outperforms existing methods on the \text T ^2\text SG  generation task, and the generated \text T ^2\text SG  significantly enhances traffic topology reasoning in downstream tasks, achieving a state-of-the-art performance of 46.3 OLS on the OpenLane-V2 benchmark. Our source code is available at https://github.com/MICLAB-BUPT/T2SG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lv_T2SG_Traffic_Topology_Scene_Graph_for_Topology_Reasoning_in_Autonomous_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lv_2025_CVPR,
    author    = {Lv, Changsheng and Qi, Mengshi and Liu, Liang and Ma, Huadong},
    title     = {T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17197-17206}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China"]


title: Unseen Visual Anomaly Generation
authors: Han Sun, Yunkang Cao, Hao Dong, Olga Fink
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25508-25517
affiliations: ["EPFL", "HUST", "ETH Zurich", "EPFL"]
cities: ["Lausanne", "Wuhan", "Zurich", "Lausanne"]
countries: ["Switzerland", "China", "Switzerland", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Unseen_Visual_Anomaly_Generation_CVPR_2025_paper.html

abstract:
Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive training data, limiting their applicability in real-world scenarios. In this work, we propose Anomaly Anything (AnomalyAny), a novel framework that leverages Stable Diffusion (SD)'s image generation capabilities to generate diverse and realistic unseen anomalies. By conditioning on a single normal sample during test time, AnomalyAny is able to generate unseen anomalies for arbitrary object types with text descriptions. Within AnomalyAny, we propose attention-guided anomaly optimization to direct SD's attention on generating hard anomaly concepts. Additionally, we introduce prompt-guided anomaly refinement, incorporating detailed descriptions to further improve the generation quality. Extensive experiments on MVTec AD and VisA datasets demonstrate AnomalyAny's ability in generating high-quality unseen anomalies and its effectiveness in enhancing downstream AD performance. Our demo and code are available at https://hansunhayden.github.io/CUT.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Unseen_Visual_Anomaly_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Unseen_Visual_Anomaly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Han and Cao, Yunkang and Dong, Hao and Fink, Olga},
    title     = {Unseen Visual Anomaly Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25508-25517}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "HUST", "city": "Wuhan", "country": "China"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}]


title: T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25336-25345
cities: ["Nottingham", "St Andrews", "Hong Kong", "Singapore", "Harbin"]
countries: ["UK", "UK", "Hong Kong", "Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_T2ICount_Enhancing_Cross-modal_Understanding_for_Zero-Shot_Counting_CVPR_2025_paper.html

authors:
Yifei Qian, Zhongliang Guo, Bowen Deng, Chun Tong Lei, Shuai Zhao, Chun Pong Lau, Xiaopeng Hong, Michael P. Pound

abstract:
Zero-shot object counting aims to count instances of arbitrary object categories specified by text descriptions. Existing methods typically rely on vision-language models like CLIP, but often exhibit limited sensitivity to text prompts. We present T2ICount, a diffusion-based framework that leverages rich prior knowledge and fine-grained visual understanding from pretrained diffusion models. While one-step denoising ensures efficiency, it leads to weakened text sensitivity. To address this challenge, we propose a Hierarchical Semantic Correction Module that progressively refines text-image feature alignment, and a Representational Regional Coherence Loss that provides reliable supervision signals by leveraging the cross-attention maps extracted from the denoising U-Net. Furthermore, we observe that current benchmarks mainly focus on majority objects in images, potentially masking models' text sensitivity. To address this, we contribute a challenging re-annotated subset of FSC147 for better evaluation of text-guided counting ability. Extensive experiments demonstrate that our method achieves superior performance across different benchmarks. Code is available at https://github.com/cha15yq/T2ICount

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_T2ICount_Enhancing_Cross-modal_Understanding_for_Zero-Shot_Counting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qian_T2ICount_Enhancing_Cross-modal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Yifei and Guo, Zhongliang and Deng, Bowen and Lei, Chun Tong and Zhao, Shuai and Lau, Chun Pong and Hong, Xiaopeng and Pound, Michael P.},
    title     = {T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25336-25345}
}

raw_affiliation:
[{"affiliation": "University of Nottingham", "city": "Nottingham", "country": "UK"}, {"affiliation": "University of St Andrews", "city": "St Andrews", "country": "UK"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Nanyang Technology University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}]

affiliations:
["University of Nottingham", "University of St Andrews", "City University of Hong Kong", "Nanyang Technology University", "Harbin Institute of Technology"]


title: RealEdit: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13403-13413
affiliations: ["University of Washington", "Allen Institute for AI"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sushko_RealEdit_Reddit_Edits_As_a_Large-scale_Empirical_Dataset_for_Image_CVPR_2025_paper.html

authors:
Peter Sushko, Ayana Bharadwaj, Zhi Yang Lim, Vasily Ilin, Ben Caffee, Dongping Chen, Mohammadreza Salehi, Cheng-Yu Hsieh, Ranjay Krishna

abstract:
Existing image editing models struggle to meet realworld demands; despite excelling in academic benchmarks, we are yet to see them adopted to solve real user needs. The datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. In response, we introduce REALEDIT, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. REALEDIT contains a test set of 9.3K examples the community can use to evaluate models on real user requests. Our results show that existing models fall short on these tasks, implying a need for realistic training data. So, we introduce 48K training examples, with which we train our REALEDIT model. Our model achieves substantial gains--outperforming competitors by up to 165 Elo points in human judgment and 92% relative improvement on the automated VIEScore metric on our test set. We deploy our model back on Reddit, testing it on new requests, and receive positive feedback. Beyond image editing, we explore REALEDIT 's potential in detecting edited images by partnering with a deepfake detection non-profit. Finetuning their model on REALEDIT data improves its F1-score by 14 percentage points, underscoring the dataset's value for broad, impactful applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sushko_RealEdit_Reddit_Edits_As_a_Large-scale_Empirical_Dataset_for_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sushko_RealEdit_Reddit_Edits_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sushko_2025_CVPR,
    author    = {Sushko, Peter and Bharadwaj, Ayana and Lim, Zhi Yang and Ilin, Vasily and Caffee, Ben and Chen, Dongping and Salehi, Mohammadreza and Hsieh, Cheng-Yu and Krishna, Ranjay},
    title     = {RealEdit: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13403-13413}
}

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


title: VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step
authors: Hanyang Wang, Fangfu Liu, Jiawei Chi, Yueqi Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16475-16485
raw_affiliation: [{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]
affiliations: ["Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VideoScene_Distilling_Video_Diffusion_Model_to_Generate_3D_Scenes_in_CVPR_2025_paper.html

abstract:
Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regularization or feed-forward deterministic model) to mitigate the issue. However, they still suffer from performance degradation by minimal overlap across input views with insufficient visual information. Fortunately, recent video generative models show promise in addressing this challenge as they are capable of generating video clips with plausible 3D structures. Powered by large pretrained video diffusion models, some pioneering research start to explore the potential of video generative prior and create 3D scenes from sparse views. Despite impressive improvements, they are limited by slow inference time and the lack of 3D constraint, leading to inefficiencies and reconstruction artifacts that do not align with real-world geometry structure. In this paper, we propose VideoScene to distill the video diffusion model to generate 3D scenes in one step, aiming to build an efficient and effective tool to bridge the gap from video to 3D. Specifically, we design a 3D-aware leap flow distillation strategy to leap over time-consuming redundant information and train a dynamic denoising policy network to adaptively determine the optimal leap timestep during inference. Extensive experiments demonstrate that our VideoScene achieves faster and superior 3D scene generation results than previous video diffusion models, highlighting its potential as an efficient tool for future video to 3D applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VideoScene_Distilling_Video_Diffusion_Model_to_Generate_3D_Scenes_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VideoScene_Distilling_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hanyang and Liu, Fangfu and Chi, Jiawei and Duan, Yueqi},
    title     = {VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16475-16485}
}


title: 3D-HGS: 3D Half-Gaussian Splatting
authors: Haolin Li, Jinyang Liu, Mario Sznaier, Octavia Camps
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10996-11005
raw_affiliation: [{"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}]
affiliations: ["Northeastern University"]
cities: ["Boston"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_3D-HGS_3D_Half-Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Photo-realistic image rendering from 3D scene reconstruction has advanced significantly with neural rendering techniques. Among these, 3D Gaussian Splatting (3D-GS) outperforms Neural Radiance Fields (NeRFs) in quality and speed but struggles with shape and color discontinuities. We propose 3D Half-Gaussian (3D-HGS) kernels as a plug-and-play solution to address these limitations. Our experiments show that 3D-HGS enhances existing 3D-GS methods, achieving state-of-the-art rendering quality without compromising speed. More demos and code are available at https://lihaolin88.github.io/CVPR-2025-3DHGS

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_3D-HGS_3D_Half-Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_3D-HGS_3D_Half-Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Haolin and Liu, Jinyang and Sznaier, Mario and Camps, Octavia},
    title     = {3D-HGS: 3D Half-Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10996-11005}
}


title: FG^2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching
authors: Zimin Xia, Alexandre Alahi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6362-6372
affiliations: ["\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)"]
cities: ["Lausanne"]
countries: ["Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_FG2_Fine-Grained_Cross-View_Localization_by_Fine-Grained_Feature_Matching_CVPR_2025_paper.html

abstract:
We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the two images. The pose is estimated by aligning a point plane generated from the ground image with a point plane sampled from the aerial image. To generate the ground points, we first map ground image features to a 3D point cloud. Our method then learns to select features along the height dimension to pool the 3D points to a Bird's-Eye-View (BEV) plane. This selection enables us to trace which feature in the ground image contributes to the BEV representation. Next, we sample a set of sparse matches from computed point correspondences between the two point planes and compute their relative pose using Procrustes alignment. Compared to the previous state-of-the-art, our method reduces the mean localization error by 28% on the VIGOR cross-area test set. Qualitative results show that our method learns semantically consistent matches across ground and aerial views through weakly supervised learning from the camera pose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_FG2_Fine-Grained_Cross-View_Localization_by_Fine-Grained_Feature_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_FG2_Fine-Grained_Cross-View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Zimin and Alahi, Alexandre},
    title     = {FG{\textasciicircum}2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6362-6372}
}

raw_affiliation:
[{"affiliation": "\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "city": "Lausanne", "country": "Switzerland"}]


title: ReNeg: Learning Negative Embedding with Reward Guidance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23636-23645
affiliations: ["Advanced Micro Devices Inc.", "Dalian University of Technology", "Tsinghua University"]
cities: ["Santa Clara", "Dalian", "Beijing"]
countries: ["USA", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_ReNeg_Learning_Negative_Embedding_with_Reward_Guidance_CVPR_2025_paper.html

authors:
Xiaomin Li, Yixuan Liu, Takashi Isobe, Xu Jia, Qinpeng Cui, Dong Zhou, Dong Li, You He, Huchuan Lu, Zhongdao Wang, Emad Barsoum

abstract:
In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality. Typically, these negative embeddings are derived from user-defined negative prompts, which, while being functional, are not necessarily optimal. In this paper, we introduce ReNeg, an end-to-end method designed to learn improved Negative embeddings guided by a Reward model. We employ a reward feedback learning framework and integrate classifier-free guidance (CFG) into the training process, which was previously utilized only during inference, thus enabling the effective learning of negative embeddings. We also propose two strategies for learning both global and per-sample negative embeddings. Extensive experiments show that the learned negative embedding significantly outperforms null-text and handcrafted counterparts, achieving substantial improvements in human preference alignment. Additionally, the negative embedding learned within the same text embedding space exhibits strong generalization capabilities. For example, using the same CLIP text encoder, the negative embedding learned on SD1.5 can be seamlessly transferred to text-to-image or even text-to-video models such as ControlNet, ZeroScope, and VideCrafter2, resulting in consistent performance improvements across the board.  Code is available at https://github.com/AMD-AIG-AIMA/ReNeg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_ReNeg_Learning_Negative_Embedding_with_Reward_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_ReNeg_Learning_Negative_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiaomin and Liu, Yixuan and Isobe, Takashi and Jia, Xu and Cui, Qinpeng and Zhou, Dong and Li, Dong and He, You and Lu, Huchuan and Wang, Zhongdao and Barsoum, Emad},
    title     = {ReNeg: Learning Negative Embedding with Reward Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23636-23645}
}

raw_affiliation:
[{"affiliation": "Advanced Micro Devices Inc.", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: Scale Efficient Training for Large Datasets
authors: Qing Zhou, Junyu Gao, Qi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20458-20467
raw_affiliation: [{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]
affiliations: ["Northwestern Polytechnical University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Scale_Efficient_Training_for_Large_Datasets_CVPR_2025_paper.html

abstract:
The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly inefficient due to the presence of low-value samples, including excessive redundant samples, overly challenging samples, and inefficient easy samples that contribute little to model improvement. To address this challenge, we propose Scale Efficient Training (SeTa) for large datasets, a dynamic sample pruning approach that losslessly reduces training time. To remove low-value samples, SeTa first performs random pruning to eliminate redundant samples, then clusters the remaining samples according to their learning difficulty measured by loss. Building upon this clustering, a sliding window strategy is employed to progressively remove both overly challenging and inefficient easy clusters following an easy-to-hard curriculum. We conduct extensive experiments on large-scale synthetic datasets, including ToCa, SS1M, and ST+MJ, each containing over 3 million samples. SeTa reduces training costs by up to 50% while maintaining or improving performance, with minimal degradation even at 70% cost reduction. Furthermore, experiments on various scale real datasets across various backbones (including CNNs, Transformers, and Mambas) and diverse tasks (instruction tuning, multi-view stereo, geo-localization, composed image retrieval, referring image segmentation) demonstrate the powerful effectiveness and universality of our approach. Code is available at https://github.com/mrazhou/SeTa.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Scale_Efficient_Training_for_Large_Datasets_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Qing and Gao, Junyu and Wang, Qi},
    title     = {Scale Efficient Training for Large Datasets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20458-20467}
}


title: Distilled Prompt Learning for Incomplete Multimodal Survival Prediction
authors: Yingxue Xu, Fengtao Zhou, Chenyu Zhao, Yihui Wang, Can Yang, Hao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5102-5111
cities: ["Hong Kong", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Distilled_Prompt_Learning_for_Incomplete_Multimodal_Survival_Prediction_CVPR_2025_paper.html

abstract:
The integration of multimodal data including pathology images and gene profiles is widely applied in precise survival prediction. Despite recent advances in multimodal survival models, collecting complete modalities for multimodal fusion still poses a significant challenge, hindering their application in clinical settings. Current approaches tackling incomplete modalities often fall short, as they typically compensate for only a limited part of the knowledge of missing modalities. To address this issue, we propose a Distilled Prompt Learning framework (DisPro) to utilize the strong robustness of Large Language Models (LLMs) to missing modalities, which employs two-stage prompting for compensation of comprehensive information for missing modalities. In the first stage, Unimodal Prompting (UniPro) distills the knowledge distribution of each modality, preparing for supplementing modality-specific knowledge of the missing modality in the subsequent stage. In the second stage, Multimodal Prompting (MultiPro) leverages available modalities as prompts for LLMs to infer the missing modality, which provides modality-common information. Simultaneously, the unimodal knowledge acquired in the first stage is injected into multimodal inference to compensate for the modality-specific knowledge of the missing modality. Extensive experiments covering various missing scenarios demonstrated the superiority of the proposed method. The code is available at https://github.com/Innse/DisPro.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Distilled_Prompt_Learning_for_Incomplete_Multimodal_Survival_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Distilled_Prompt_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yingxue and Zhou, Fengtao and Zhao, Chenyu and Wang, Yihui and Yang, Can and Chen, Hao},
    title     = {Distilled Prompt Learning for Incomplete Multimodal Survival Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5102-5111}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Department of Mathematics, The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["Department of Computer Science and Engineering, The Hong Kong University of Science and Technology", "Department of Mathematics, The Hong Kong University of Science and Technology"]


authors: Haonan An, Guang Hua, Zhengru Fang, Guowen Xu, Susanto Rahardja, Yuguang Fang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13424-13433
cities: ["Hong Kong", "Singapore", "Chengdu"]
countries: ["Hong Kong", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/An_Decoder_Gradient_Shield_Provable_and_High-Fidelity_Prevention_of_Gradient-Based_Box-Free_CVPR_2025_paper.html

title:
Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal

abstract:
The intellectual property of deep image-to-image models can be protected by the so-called box-free watermarking. It uses an encoder and a decoder, respectively, to embed into and extract from the model's output images invisible copyright marks. Prior works have improved watermark robustness, focusing on the design of better watermark encoders. In this paper, we reveal an overlooked vulnerability of the unprotected watermark decoder which is jointly trained with the encoder and can be exploited to train a watermark removal network. To defend against such an attack, we propose the decoder gradient shield (DGS) as a protection layer in the decoder API to prevent gradient-based watermark removal with a closed-form solution. The fundamental idea is inspired by the classical adversarial attack, but is utilized for the first time as a defensive mechanism in the box-free model watermarking. We then demonstrate that DGS can reorient and rescale the gradient directions of watermarked queries and stop the watermark remover's training loss from converging to the level without DGS, while retaining decoder output image quality. Experimental results verify the effectiveness of the proposed method. Code of paper is available at https://github.com/haonanAN309/CVPR-2025-Official-Implementation-Decoder-Gradient-Shield.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/An_Decoder_Gradient_Shield_Provable_and_High-Fidelity_Prevention_of_Gradient-Based_Box-Free_CVPR_2025_paper.pdf

bibtex:
@InProceedings{An_2025_CVPR,
    author    = {An, Haonan and Hua, Guang and Fang, Zhengru and Xu, Guowen and Rahardja, Susanto and Fang, Yuguang},
    title     = {Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13424-13433}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Singapore Institute of Technology", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}]

affiliations:
["City University of Hong Kong", "Singapore Institute of Technology", "University of Electronic Science and Technology of China"]


title: MotionPro: A Precise Motion Controller for Image-to-Video Generation
authors: Zhongwei Zhang, Fuchen Long, Zhaofan Qiu, Yingwei Pan, Wu Liu, Ting Yao, Tao Mei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27957-27967
affiliations: ["University of Science and Technology of China", "HiDream.ai Inc."]
cities: ["Hefei", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MotionPro_A_Precise_Motion_Controller_for_Image-to-Video_Generation_CVPR_2025_paper.html

abstract:
Animating images with interactive motion control has garnered popularity for image-to-video (I2V) generation. Modern approaches typically rely on large Gaussian kernels to extend motion trajectories as condition without explicitly defining movement region, leading to coarse motion control and failing to disentangle object and camera moving. To alleviate these, we present MotionPro, a precise motion controller that novelly leverages region-wise trajectory and motion mask to regulate fine-grained motion synthesis and identify target motion category (i.e., object or camera moving), respectively. Technically, MotionPro first estimates the flow maps on each training video via a tracking model, and then samples the region-wise trajectories to simulate inference scenario. Instead of extending flow through large Gaussian kernels, our region-wise trajectory approach enables more precise control by directly utilizing trajectories within local regions, thereby effectively characterizing fine-grained movements. A motion mask is simultaneously derived from the predicted flow maps to capture the holistic motion dynamics of the movement regions. To pursue natural motion control, MotionPro further strengthens video denoising by incorporating both region-wise trajectories and motion mask through feature modulation. More remarkably, we meticulously construct a benchmark, i.e., MC-Bench, with 1.1K user-annotated image-trajectory pairs, for the evaluation of both fine-grained and object-level I2V motion control. Extensive experiments conducted on WebVid-10M and MC-Bench demonstrate the effectiveness of MotionPro. Please refer to our project page for more results: https://zhw-zhang.github.io/MotionPro-page/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MotionPro_A_Precise_Motion_Controller_for_Image-to-Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MotionPro_A_Precise_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhongwei and Long, Fuchen and Qiu, Zhaofan and Pan, Yingwei and Liu, Wu and Yao, Ting and Mei, Tao},
    title     = {MotionPro: A Precise Motion Controller for Image-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27957-27967}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "HiDream.ai Inc.", "city": "Beijing", "country": "China"}]


title: Goku: Flow Based Video Generative Foundation Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23516-23527
affiliations: ["The University of Hong Kong", "Bytedance"]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Goku_Flow_Based_Video_Generative_Foundation_Models_CVPR_2025_paper.html

authors:
Shoufa Chen, Chongjian Ge, Yuqi Zhang, Yida Zhang, Fengda Zhu, Hao Yang, Hongxiang Hao, Hui Wu, Zhichao Lai, Yifei Hu, Ting-Che Lin, Shilong Zhang, Fu Li, Chuan Li, Xing Wang, Yanghua Peng, Peize Sun, Ping Luo, Yi Jiang, Zehuan Yuan, Bingyue Peng, Xiaobing Liu

abstract:
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We detail the foundational elements enabling high-quality visual generation, including the data curation pipeline, model architecture design, flow formulation, and advanced infrastructure for efficient and robust large-scale training. The Goku models demonstrate superior performance in both qualitative and quantitative evaluations, setting new benchmarks across major tasks. Specifically, Goku achieves 0.76 on GenEval and 83.65 on DPG-Bench for text-to-image generation, and 84.85 on VBench for text-to-video tasks. We believe that this work provides valuable insights and practical advancements for the research community in developing joint image-and-video generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Goku_Flow_Based_Video_Generative_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Goku_Flow_Based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Shoufa and Ge, Chongjian and Zhang, Yuqi and Zhang, Yida and Zhu, Fengda and Yang, Hao and Hao, Hongxiang and Wu, Hui and Lai, Zhichao and Hu, Yifei and Lin, Ting-Che and Zhang, Shilong and Li, Fu and Li, Chuan and Wang, Xing and Peng, Yanghua and Sun, Peize and Luo, Ping and Jiang, Yi and Yuan, Zehuan and Peng, Bingyue and Liu, Xiaobing},
    title     = {Goku: Flow Based Video Generative Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23516-23527}
}

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


title: Learning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning
authors: Xiaohan Zou, Wenchao Ma, Shu Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4862-4873
raw_affiliation: [{"affiliation": "The Pennsylvania State University", "city": "University Park", "country": "USA"}]
affiliations: ["The Pennsylvania State University"]
cities: ["University Park"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zou_Learning_Conditional_Space-Time_Prompt_Distributions_for_Video_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
Recent advancements in prompt-based learning have significantly advanced image and video class-incremental learning. However, the prompts learned by these methods often fail to capture the diverse and informative characteristics of videos, and struggle to generalize effectively to future tasks and classes. To address these challenges, this paper proposes modeling the distribution of space-time prompts conditioned on the input video using a diffusion model. This generative approach allows the proposed model to naturally handle the diverse characteristics of videos, leading to more robust prompt learning and enhanced generalization capabilities. Additionally, we develop a simple yet effective mechanism to transfer the token relationship modeling capabilities of pre-trained image transformers to spatio-temporal modeling in videos. Our approach has been thoroughly evaluated across four established benchmarks, showing remarkable improvements over existing state-of-the-art methods in video class-incremental learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zou_Learning_Conditional_Space-Time_Prompt_Distributions_for_Video_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zou_Learning_Conditional_Space-Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zou_2025_CVPR,
    author    = {Zou, Xiaohan and Ma, Wenchao and Zhao, Shu},
    title     = {Learning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4862-4873}
}


title: Convex Combination Star Shape Prior for Data-driven Image Semantic Segmentation
authors: Xinyu Zhao, Jun Xie, Shengzhe Chen, Jun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14068-14077
cities: ["Beijing", "Beijing", "Phoenix", "Beijing"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Convex_Combination_Star_Shape_Prior_for_Data-driven_Image_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Multi-center star shape is a prevalent object shape feature, which has proven effective in model-based image segmentation methods. However, the shape field function induced by the multi-center star shape is non-smooth, and directly applying it to the data-driven image segmentation network architecture design may lead to instability in backpropagation. This paper proposes a convex combination star (CCS) shape, possessing multi-center star shape properties, and has the advantage of effectively controlling the shape of the region through a smooth field function. The sufficient condition of the proposed CCS shape can be combined into the image segmentation neural network structure design through the bridge between the variational segmentation model and the activation function of the data-driven method. Taking Segment Anything Model (SAM) and its improved version as backbone networks, we have shown that the segmentation network architecture with CCS shape properties can greatly improve the accuracy of segmentation results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Convex_Combination_Star_Shape_Prior_for_Data-driven_Image_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Convex_Combination_Star_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Xinyu and Xie, Jun and Chen, Shengzhe and Liu, Jun},
    title     = {Convex Combination Star Shape Prior for Data-driven Image Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14068-14077}
}

raw_affiliation:
[{"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "Arizona State University", "city": "Phoenix", "country": "USA"}, {"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Normal University", "Beijing Normal University", "Arizona State University", "Beijing Normal University"]


title: Hyperbolic Safety-Aware Vision-Language Models
authors: Tobia Poppi, Tejaswi Kasarla, Pascal Mettes, Lorenzo Baraldi, Rita Cucchiara
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4222-4232
cities: ["Modena", "Pisa", "Amsterdam", "Italy"]
countries: ["Italy", "Italy", "Netherlands", "Italy"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Poppi_Hyperbolic_Safety-Aware_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current efforts have relied on unlearning techniques that try to erase the model's knowledge of unsafe concepts. While effective in reducing unwanted outputs, unlearning limits the model's capacity to discern between safe and unsafe content. In this work, we introduce a novel approach that shifts from unlearning to an awareness paradigm by leveraging the inherent hierarchical properties of the hyperbolic space. We propose to encode safe and unsafe content as an entailment hierarchy, where both are placed in different regions of hyperbolic space. Our HySAC, Hyperbolic Safety-Aware CLIP, employs entailment loss functions to model the hierarchical and asymmetrical relations between safe and unsafe image-text pairs. This modelling - ineffective in standard vision-language models due to their reliance on Euclidean embeddings - endows the model with awareness of unsafe content, enabling it to serve as both a multimodal unsafe classifier and a flexible content retriever, with the option to dynamically redirect unsafe queries toward safer alternatives or retain the original output. Extensive experiments show that our approach not only enhances safety recognition, but also establishes a more adaptable and interpretable framework for content moderation in vision-language models. Our source code is available at: https://github.com/aimagelab/HySAC

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Poppi_Hyperbolic_Safety-Aware_Vision-Language_Models_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Poppi_2025_CVPR,
    author    = {Poppi, Tobia and Kasarla, Tejaswi and Mettes, Pascal and Baraldi, Lorenzo and Cucchiara, Rita},
    title     = {Hyperbolic Safety-Aware Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4222-4232}
}

raw_affiliation:
[{"affiliation": "University of Modena and Reggio Emilia, Italy", "city": "Modena", "country": "Italy"}, {"affiliation": "University of Pisa, Italy", "city": "Pisa", "country": "Italy"}, {"affiliation": "University of Amsterdam, Netherlands", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "IIT-CNR, Italy", "city": "Italy", "country": "Italy"}]

affiliations:
["University of Modena and Reggio Emilia, Italy", "University of Pisa, Italy", "University of Amsterdam, Netherlands", "IIT-CNR, Italy"]


title: WISH: Weakly Supervised Instance Segmentation using Heterogeneous Labels
authors: Hyeokjun Kweon, Kuk-Jin Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25377-25387
affiliations: ["Chung-Ang University", "KAIST"]
cities: ["Seoul", "Daejeon"]
countries: ["South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kweon_WISH_Weakly_Supervised_Instance_Segmentation_using_Heterogeneous_Labels_CVPR_2025_paper.html

abstract:
Instance segmentation traditionally relies on dense pixel-level annotations, making it costly and labor-intensive. To alleviate this burden, weakly supervised instance segmentation utilizes cost-effective weak labels, such as image-level tags, points, and bounding boxes. However, existing approaches typically focus on a single type of weak label, overlooking the cost-efficiency potential of combining multiple types. In this paper, we introduce WISH, a novel heterogeneous framework for weakly supervised instance segmentation that integrates diverse weak label types within a single model. WISH unifies heterogeneous labels by leveraging SAM's prompt latent space through a multi-stage matching strategy, effectively compensating for the lack of spatial information in class tags. Extensive experiments on Pascal VOC and COCO demonstrate that our framework not only surpasses existing homogeneous weak supervision methods but also achieves superior results in heterogeneous settings with equivalent annotation costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kweon_WISH_Weakly_Supervised_Instance_Segmentation_using_Heterogeneous_Labels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kweon_WISH_Weakly_Supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kweon_2025_CVPR,
    author    = {Kweon, Hyeokjun and Yoon, Kuk-Jin},
    title     = {WISH: Weakly Supervised Instance Segmentation using Heterogeneous Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25377-25387}
}

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


title: SinGS: Animatable Single-Image Human Gaussian Splats with Kinematic Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5571-5580
cities: ["Shanghai", "Unknown", "Unknown", "Shanghai"]
countries: ["China", "Unknown", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_SinGS_Animatable_Single-Image_Human_Gaussian_Splats_with_Kinematic_Priors_CVPR_2025_paper.html

authors:
Yufan Wu, Xuanhong Chen, Wen Li, Shunran Jia, Hualiang Wei, Kairui Feng, Jialiang Chen, Yuhan Li, Ang He, Weimin Zhang, Bingbing Ni, Wenjun Zhang

abstract:
Despite significant advances in accurately estimating geometry in contemporary single-image 3D human reconstruction, creating a high-quality, efficient, and animatable 3D avatar remains an open challenge.  Two key obstacles persist: incomplete observation and inconsistent 3D priors. To address these challenges, we propose SinGS, aiming to achieve high-quality and efficient animatable 3D avatar reconstruction. At the heart of SinGS are two key components: Kinematic Human Diffusion and Geometry-Preserving 3D Gaussain Splatting. The former is a foundational human model that samples within pose space to generate a highly 3D-consistent and high-quality sequence of human images, inferring unseen viewpoints and providing kinematic priors.  The latter is a system that reconstructs a compact, high-quality 3D avatar even under imperfect priors, achieved through a novel semantic Laplacian regularization and a geometry-preserving density control strategy that enable precise and compact assembly of 3D primitives. Extensive experiments demonstrate that SinGS enables lifelike, animatable human reconstructions, maintaining both high quality and inference efficiency (up to 70FPS).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_SinGS_Animatable_Single-Image_Human_Gaussian_Splats_with_Kinematic_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_SinGS_Animatable_Single-Image_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yufan and Chen, Xuanhong and Li, Wen and Jia, Shunran and Wei, Hualiang and Feng, Kairui and Chen, Jialiang and Li, Yuhan and He, Ang and Zhang, Weimin and Ni, Bingbing and Zhang, Wenjun},
    title     = {SinGS: Animatable Single-Image Human Gaussian Splats with Kinematic Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5571-5580}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "DreamX Inc", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Akool Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "The National Key Laboratory of Autonomous Intelligent Unmanned Systems", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Jiao Tong University", "DreamX Inc", "Akool Research", "The National Key Laboratory of Autonomous Intelligent Unmanned Systems"]


title: Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation
authors: Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, Wei Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15009-15020
affiliations: ["Shanghai Jiao Tong University", "Meituan"]
cities: ["Shanghai", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Parameter-efficient_Fine-tuning_in_Hyperspherical_Space_for_Open-vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary semantic segmentation seeks to label each pixel in an image with arbitrary text descriptions. Vision-language foundation models, especially CLIP, have recently emerged as powerful tools for acquiring open-vocabulary capabilities. However, fine-tuning CLIP to equip it with pixel-level prediction ability often suffers three issues: 1) high computational cost, 2) misalignment between the two inherent modalities of CLIP, and 3) degraded generalization ability on unseen categories. To address these issues, we propose \alg, a symmetrical parameter-efficient fine-tuning (PEFT) strategy conducted in hyperspherical space for both of the two CLIP modalities. Specifically, the PEFT strategy is achieved by a series of efficient block-diagonal learnable transformation matrices and a dual cross-relation communication module among all learnable matrices. Since the PEFT strategy is conducted symmetrically to the two CLIP modalities, the misalignment between them is mitigated. Furthermore, we apply an additional constraint to PEFT on the CLIP text encoder according to the hyperspherical energy principle, i.e., minimizing hyperspherical energy during fine-tuning preserves the intrinsic structure of the original parameter space, to prevent the destruction of the generalization ability offered by the CLIP text encoder. Extensive evaluations across various benchmarks show that H-CLIP achieves new SOTA open-vocabulary semantic segmentation results while only requiring updating approximately 4% of the total parameters of CLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Parameter-efficient_Fine-tuning_in_Hyperspherical_Space_for_Open-vocabulary_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_Parameter-efficient_Fine-tuning_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Zelin and Xu, Zhengqin and Zeng, Zhilin and Huang, Yu and Wang, Yaoming and Shen, Wei},
    title     = {Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15009-15020}
}

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


title: Relative Pose Estimation through Affine Corrections of Monocular Depth Priors
authors: Yifan Yu, Shaohui Liu, Rémi Pautrat, Marc Pollefeys, Viktor Larsson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16706-16716
affiliations: ["ETH Zurich", "PICO", "Microsoft Spatial AI Lab", "Lund University"]
cities: ["Zurich", "Unknown", "Unknown", "Lund"]
countries: ["Switzerland", "Unknown", "Unknown", "Sweden"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Relative_Pose_Estimation_through_Affine_Corrections_of_Monocular_Depth_Priors_CVPR_2025_paper.html

abstract:
Monocular depth estimation (MDE) models have undergone significant advancements over recent years. Many MDE models aim to predict affine-invariant relative depth from monocular images, while recent developments in large-scale training and vision foundation models enable reasonable estimation of metric (absolute) depth. However, effectively leveraging these predictions for geometric vision tasks, in particular relative pose estimation, remains relatively under explored. While depths provide rich constraints for cross-view image alignment, the intrinsic noise and ambiguity from the monocular depth priors present practical challenges to improving upon classic keypoint-based solutions. In this paper, we develop three solvers for relative pose estimation that explicitly account for independent affine (scale and shift) ambiguities, covering both calibrated and uncalibrated conditions. We further propose a hybrid estimation pipeline that combines our proposed solvers with classic point-based solvers and epipolar constraints. We find that the affine correction modeling is beneficial to not only the relative depth priors but also, surprisingly, the "metric" ones. Results across multiple datasets demonstrate large improvements of our approach over classic keypoint-based baselines and PnP-based solutions, under both calibrated and uncalibrated setups. We also show that our method improves consistently with different feature matchers and MDE models, and can further benefit from very recent advances on both modules.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Relative_Pose_Estimation_through_Affine_Corrections_of_Monocular_Depth_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Relative_Pose_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Yifan and Liu, Shaohui and Pautrat, R\'emi and Pollefeys, Marc and Larsson, Viktor},
    title     = {Relative Pose Estimation through Affine Corrections of Monocular Depth Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16706-16716}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "PICO", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Microsoft Spatial AI Lab", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Lund University", "city": "Lund", "country": "Sweden"}]


title: Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video Diffusion
authors: Zhenglin Zhou, Fan Ma, Hehe Fan, Tat-Seng Chua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15941-15952
cities: ["Hangzhou", "Hangzhou", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Zero-1-to-A_Zero-Shot_One_Image_to_Animatable_Head_Avatars_Using_Video_CVPR_2025_paper.html

abstract:
Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avatar generation methods, such as pre-trained diffusion models with score distillation sampling (SDS), which align avatars with pseudo ground-truth outputs from the diffusion model. However, directly distilling 4D avatars from video diffusion often leads to over-smooth results due to spatial and temporal inconsistencies in the generated video. To address this issue, we propose Zero-1-to-A, a robust method that synthesizes a spatial and temporal consistency dataset for 4D avatar reconstruction using the video diffusion model. Specifically, Zero-1-to-A iteratively constructs video datasets and optimizes animatable avatars in a progressive manner, ensuring that avatar quality increases smoothly and consistently throughout the learning process. This progressive learning involves two stages: (1) Spatial Consistency Learning fixes expressions and learns from front-to-side views, and (2) Temporal Consistency Learning fixes views and learns from relaxed to exaggerated expressions, generating 4D avatars in a simple-to-complex manner. Extensive experiments demonstrate that Zero-1-to-A improves fidelity, animation quality, and rendering speed compared to existing diffusion-based methods, providing a solution for lifelike avatar creation. Code is publicly available at: https://github.com/ZhenglinZhou/Zero-1-to-A.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Zero-1-to-A_Zero-Shot_One_Image_to_Animatable_Head_Avatars_Using_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Zero-1-to-A_Zero-Shot_One_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Zhenglin and Ma, Fan and Fan, Hehe and Chua, Tat-Seng},
    title     = {Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15941-15952}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Brain-machine Intelligence, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["State Key Laboratory of Brain-machine Intelligence, Zhejiang University", "ReLER, CCAI, Zhejiang University", "National University of Singapore"]


title: Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition
authors: Khanh Nguyen, Ghulam Mubashar Hassan, Ajmal Mian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16965-16975
raw_affiliation: [{"affiliation": "The University of W estern Australia", "city": "Perth", "country": "Australia"}]
affiliations: ["The University of W estern Australia"]
cities: ["Perth"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_Occlusion-aware_Text-Image-Point_Cloud_Pretraining_for_Open-World_3D_Object_Recognition_CVPR_2025_paper.html

abstract:
Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still falls short due to unrealistic pretraining settings. Additionally, these methods incur high inference costs because they rely on Transformer's attention modules. In this paper, we make two contributions to address these limitations. First, we propose occlusion-aware text-image-point cloud pretraining to reduce the training-testing domain gap. From 52K synthetic 3D objects, our framework generates nearly 630K partial point clouds for pretraining, consistently improving real-world recognition performances of existing popular 3D networks. Second, to reduce computational requirements, we introduce DuoMamba, a two-stream linear state space model tailored for point clouds. By integrating two space-filling curves with 1D convolutions, DuoMamba effectively models spatial dependencies between point tokens, offering a powerful alternative to Transformer. When pretrained with our framework, DuoMamba surpasses current state-of-the-art methods while reducing latency and FLOPs, highlighting the potential of our approach for real-world applications. Our code and data are available at ndkhanh360.github.io/project_occtip.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_Occlusion-aware_Text-Image-Point_Cloud_Pretraining_for_Open-World_3D_Object_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_Occlusion-aware_Text-Image-Point_Cloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Khanh and Hassan, Ghulam Mubashar and Mian, Ajmal},
    title     = {Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16965-16975}
}


title: Conical Visual Concentration for Efficient Large Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14593-14603
cities: ["Hefei", "Shanghai", "Hong Kong", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_Conical_Visual_Concentration_for_Efficient_Large_Vision-Language_Models_CVPR_2025_paper.html

authors:
Long Xing, Qidong Huang, Xiaoyi Dong, Jiajie Lu, Pan Zhang, Yuhang Zang, Yuhang Cao, Conghui He, Jiaqi Wang, Feng Wu, Dahua Lin

abstract:
In large vision-language models (LVLMs), images serve as inputs that carry a wealth of information. As the idiom "A picture is worth a thousand words" implies, representing a single image in current LVLMs can require hundreds or even thousands of tokens. This results in significant computational costs, which grow quadratically as input image resolution increases, thereby severely impacting the efficiency. Previous approaches have attempted to reduce the number of image tokens either before or within the early layers of LVLMs. However, these strategies inevitably result in the loss of crucial image information. To address this challenge, we conduct an empirical study revealing that all visual tokens are necessary for LVLMs in the shallow layers, and token redundancy progressively increases in the deeper layers.To this end, we propose ViCo, a conical-style visual concentration strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss. Specifically, we partition the LVLM into several stages and drop part of the image tokens at the end of each stage with a pre-defined ratio. The dropping is based on a lightweight similarity calculation with a negligible time overhead. Extensive experiments demonstrate that ViCo can achieve over 40% training time reduction and 55% inference FLOPs acceleration on leading LVLMs like LLaVA-NeXT, maintaining comparable multi-modal performance. Besides, ViCo can also serve as a plug-and-play strategy to accelerate inference in a free way, with better performance and lower inference cost than counterparts.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_Conical_Visual_Concentration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Long and Huang, Qidong and Dong, Xiaoyi and Lu, Jiajie and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and He, Conghui and Wang, Jiaqi and Wu, Feng and Lin, Dahua},
    title     = {Conical Visual Concentration for Efficient Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14593-14603}
}

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

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


title: Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
authors: Jona Ballé, Luca Versari, Emilien Dupont, Hyunjik Kim, Matthias Bauer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23259-23268
affiliations: ["New York University", "Google Paradigms Of Intelligence", "Google DeepMind"]
cities: ["New York", "Mountain View", "Mountain View"]
countries: ["USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Balle_Good_Cheap_and_Fast_Overfitted_Image_Compression_with_Wasserstein_Distortion_CVPR_2025_paper.html

abstract:
Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution, leading to excellent image quality. This, however, comes at the expense of a computational complexity that is several orders of magnitude higher than today's commercial codecs, and thus prohibitive for most practical applications. With this paper, we demonstrate that by focusing on modeling visual perception rather than the data distribution, we can achieve a very good trade-off between visual quality and bit rate similar to "generative" compression models such as HiFiC, while requiring less than 1% of the multiply-accumulate operations (MACs) for decompression. We do this by optimizing C3, an overfitted image codec, for Wasserstein Distortion (WD), and evaluating the image reconstructions with a human rater study, showing that WD clearly outperforms LPIPS as an optimization objective. The study also reveals that WD outperforms other perceptual metrics such as LPIPS, DISTS, and MS-SSIM as a predictor of human ratings, remarkably achieving over 94% Pearson correlation with Elo scores.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Balle_Good_Cheap_and_Fast_Overfitted_Image_Compression_with_Wasserstein_Distortion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Balle_Good_Cheap_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Balle_2025_CVPR,
    author    = {Ball\'e, Jona and Versari, Luca and Dupont, Emilien and Kim, Hyunjik and Bauer, Matthias},
    title     = {Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23259-23268}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Google Paradigms Of Intelligence", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


title: Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model
authors: Yuting Zhang, Hao Lu, Qingyong Hu, Yin Wang, Kaishen Yuan, Xin Liu, Kaishun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29237-29247
cities: ["Guangzhou", "Hong Kong", "Hangzhou", "Lahti"]
countries: ["China", "China", "China", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Period-LLM_Extending_the_Periodic_Capability_of_Multimodal_Large_Language_Model_CVPR_2025_paper.html

abstract:
Periodic or quasi-periodic phenomena reveal intrinsic characteristics in various natural processes, such as weather patterns, movement behaviors, traffic flows, and biological signals. Given that these phenomena span multiple modalities, the capabilities of Multimodal Large Language Models (MLLMs) offer promising potential to effectively capture and understand their complex nature. However, current MLLMs struggle with periodic tasks due to limitations in: 1) lack of temporal modelling and 2) conflict between short and long periods. This paper introduces Period-LLM, a multimodal large language model designed to enhance the performance of periodic tasks across various modalities, and constructs a benchmark of various difficulty for evaluating the cross-modal periodic capabilities of large models. Specially, We adopt an "Easy to Hard Generalization" paradigm, starting with relatively simple text-based tasks and progressing to more complex visual and multimodal tasks, ensuring that the model gradually builds robust periodic reasoning capabilities. Additionally, we propose a "Resisting Logical Oblivion" optimization strategy to maintain periodic reasoning abilities during semantic alignment. Extensive experiments demonstrate the superiority of the proposed Period-LLM over existing MLLMs in periodic tasks. The code is available at https://github.com/keke-nice/Period-LLM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Period-LLM_Extending_the_Periodic_Capability_of_Multimodal_Large_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Period-LLM_Extending_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yuting and Lu, Hao and Hu, Qingyong and Wang, Yin and Yuan, Kaishen and Liu, Xin and Wu, Kaishun},
    title     = {Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29237-29247}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science & Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Hong Kong University of Science & Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Lappeenranta-Lahti University of Technology", "city": "Lahti", "country": "Finland"}]

affiliations:
["The Hong Kong University of Science & Technology (Guangzhou)", "The Hong Kong University of Science & Technology", "Zhejiang University", "Lappeenranta-Lahti University of Technology"]


title: V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection
authors: Xun Huang, Jinlong Wang, Qiming Xia, Siheng Chen, Bisheng Yang, Xin Li, Cheng Wang, Chenglu Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27390-27400
affiliations: ["Xiamen University", "Shanghai Jiao Tong University", "Wuhan University", "Texas A&M University"]
cities: ["Xiamen", "Shanghai", "Wuhan", "College Station"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_V2X-R_Cooperative_LiDAR-4D_Radar_Fusion_with_Denoising_Diffusion_for_3D_CVPR_2025_paper.html

abstract:
Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, they face performance degradation in adverse weather. Weather-robust 4D radar, with Doppler velocity and additional geometric information, offers a promising solution to this challenge. To this end, we present V2X-R, the first simulated V2X dataset incorporating LiDAR, camera, and 4D radar modalities. V2X-R contains 12,079 scenarios with 37,727 frames of LiDAR and 4D radar point clouds, 150,908 images, and 170,859 annotated 3D vehicle bounding boxes. Subsequently, we propose a novel cooperative LiDAR-4D radar fusion pipeline for 3D object detection and implement it with multiple fusion strategies. To achieve weather-robust detection, we additionally propose a Multi-modal Denoising Diffusion (MDD) module in our fusion pipeline. MDD utilizes weather-robust 4D radar feature as a condition to guide the diffusion model in denoising noisy LiDAR features.Experiments show that our LiDAR-4D radar fusion pipeline demonstrates superior performance in the V2X-R dataset. Over and above this, our MDD module further improved the foggy/snowy performance of the basic fusion model by up to  5.73%/6.70% and barely disrupting normal performance. The dataset and code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_V2X-R_Cooperative_LiDAR-4D_Radar_Fusion_with_Denoising_Diffusion_for_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_V2X-R_Cooperative_LiDAR-4D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Xun and Wang, Jinlong and Xia, Qiming and Chen, Siheng and Yang, Bisheng and Li, Xin and Wang, Cheng and Wen, Chenglu},
    title     = {V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27390-27400}
}

raw_affiliation:
[{"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]


title: Multi-Modal Synergistic Implicit Image Enhancement for Efficient Optical Flow Estimation
authors: Weichen Dai, Hexing Wu, Xiaoyang Weng, Yuxin Zheng, Yuhang Ming, Wanzeng Kong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2173-2182
affiliations: ["Hangzhou Dianzi University", "Tsinghua University"]
cities: ["Hangzhou", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Multi-Modal_Synergistic_Implicit_Image_Enhancement_for_Efficient_Optical_Flow_Estimation_CVPR_2025_paper.html

abstract:
As a fundamental visual task, optical flow estimation has widespread applications in computer vision. However, it faces significant challenges under adverse lighting conditions, where low texture and noise make accurate optical flow estimation particularly difficult.In this paper, we propose an optical flow method that employs implicit image enhancement through multi-modal synergistic training. To supplement the scene information missing in the original low-quality image, we utilize a high-low frequency feature enhancement network. The enhancement network is implicitly guided by multi-modal data and the specific subsequent tasks, enabling the model to learn multi-modal knowledge that enhances feature information suitable for optical flow estimation during inference. By using RGBD multi-modal data, the proposed method avoids the reliance on the images captured from the same view, a common limitation in traditional image enhancement methods.During training, the encoded features extracted from the enhanced images are synergistically supervised by features from the RGBD fusion as well as by the optical flow task.Experiments conducted on both synthetic and real datasets demonstrate that the proposed method significantly improves performance on public datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Multi-Modal_Synergistic_Implicit_Image_Enhancement_for_Efficient_Optical_Flow_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Weichen and Wu, Hexing and Weng, Xiaoyang and Zheng, Yuxin and Ming, Yuhang and Kong, Wanzeng},
    title     = {Multi-Modal Synergistic Implicit Image Enhancement for Efficient Optical Flow Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2173-2182}
}

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


title: TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
authors: Dongyoon Yang, Jihu Lee, Yongdai Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25780-25789
affiliations: ["AI Advanced Technology, SK Hynix", "Department of Statistics, Seoul National University"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_TAROT_Towards_Essentially_Domain-Invariant_Robustness_with_Theoretical_Justification_CVPR_2025_paper.html

abstract:
Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challenging domains. In this paper, we derive a new generalization bound for robust risk on the target domain using a novel divergence measure specifically designed for robust domain adaptation. Building upon this, we propose a new algorithm named TAROT, which is designed to enhance both domain adaptability and robustness. Through extensive experiments, TAROT not only surpasses state-of-the-art methods in accuracy and robustness but also significantly enhances domain generalization and scalability by effectively learning domain-invariant features. In particular, TAROT achieves superior performance on the challenging DomainNet dataset, demonstrating its ability to learn domain-invariant representations that generalize well across different domains, including unseen ones. These results highlight the broader applicability of our approach in real-world domain adaptation scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_TAROT_Towards_Essentially_Domain-Invariant_Robustness_with_Theoretical_Justification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_TAROT_Towards_Essentially_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Dongyoon and Lee, Jihu and Kim, Yongdai},
    title     = {TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25780-25789}
}

raw_affiliation:
[{"affiliation": "AI Advanced Technology, SK Hynix", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Department of Statistics, Seoul National University", "city": "Seoul", "country": "South Korea"}]


title: Foundations of the Theory of Performance-Based Ranking
authors: Sébastien Piérard, Anaïs Halin, Anthony Cioppa, Adrien Deliege, Marc Van Droogenbroeck
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14293-14302
affiliations: ["Montefiore Institute, University of Li\u00e8ge"]
cities: ["Li\u00e8ge"]
countries: ["Belgium"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pierard_Foundations_of_the_Theory_of_Performance-Based_Ranking_CVPR_2025_paper.html

abstract:
Ranking entities such as algorithms, devices, methods, or models based on their performances, while accounting for application-specific preferences, is a challenge. To address this challenge, we establish the foundations of a universal theory for performance-based ranking. First, we introduce a rigorous framework built on top of both the probability and order theories. Our new framework encompasses the elements necessary to (1) manipulate performances as mathematical objects, (2) express which performances are worse than or equivalent to others, (3) model tasks through a variable called satisfaction, (4) consider properties of the evaluation, (5) define scores, and (6) specify application-specific preferences through a variable called importance. On top of this framework, we propose the first axiomatic definition of performance orderings and performance-based rankings. Then, we introduce a universal parametric family of scores, called ranking scores, that can be used to establish rankings satisfying our axioms, while considering application-specific preferences. Finally, we show, in the case of two-class classification, that the family of ranking scores encompasses well-known performance scores, including the accuracy, the true positive rate (recall, sensitivity), the true negative rate (specificity), the positive predictive value (precision), and F1. However, we also show that some other scores commonly used to compare classifiers are unsuitable to derive performance orderings satisfying the axioms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pierard_Foundations_of_the_Theory_of_Performance-Based_Ranking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pierard_Foundations_of_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pierard_2025_CVPR,
    author    = {Pi\'erard, S\'ebastien and Halin, Ana{\"\i}s and Cioppa, Anthony and Deliege, Adrien and Van Droogenbroeck, Marc},
    title     = {Foundations of the Theory of Performance-Based Ranking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14293-14302}
}

raw_affiliation:
[{"affiliation": "Montefiore Institute, University of Li\u00e8ge", "city": "Li\u00e8ge", "country": "Belgium"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24570-24581
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Unveiling_the_Mist_over_3D_Vision-Language_Understanding_Object-centric_Evaluation_with_CVPR_2025_paper.html

title:
Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis

authors:
Jiangyong Huang, Baoxiong Jia, Yan Wang, Ziyu Zhu, Xiongkun Linghu, Qing Li, Song-Chun Zhu, Siyuan Huang

abstract:
Existing 3D vision-language (3D-VL) benchmarks fall short in evaluating 3D-VL models, creating a "mist" that obscures rigorous insights into model capabilities and 3D-VL tasks. This mist persists due to three key limitations. First, flawed test data, like ambiguous referential text in the grounding task, can yield incorrect and unreliable test results. Second, oversimplified metrics such as simply averaging accuracy per question answering (QA) pair, cannot reveal true model capability due to their vulnerability to language variations. Third, existing benchmarks isolate the grounding and QA tasks, disregarding the underlying coherence that QA should be based on solid grounding capabilities. To unveil the "mist", we propose Beacon3D, a benchmark for 3D-VL grounding and QA tasks, delivering a perspective shift in the evaluation of 3D-VL understanding. Beacon3D features (i) high-quality test data with precise and natural language, (ii) object-centric evaluation with multiple tests per object to ensure robustness, and (iii) a novel chain-of-analysis paradigm to address language robustness and model performance coherence across grounding and QA. Our evaluation of state-of-the-art 3D-VL models on Beacon3D reveals that (i) object-centric evaluation elicits true model performance and particularly weak generalization in QA; (ii) grounding-QA coherence remains fragile in current 3D-VL models, and (iii) incorporating large language models (LLMs) to 3D-VL models, though as a prevalent practice, hinders grounding capabilities and has yet to elevate QA capabilities. We hope Beacon3D and our comprehensive analysis could benefit the 3D-VL community towards faithful developments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Unveiling_the_Mist_over_3D_Vision-Language_Understanding_Object-centric_Evaluation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Unveiling_the_Mist_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Jiangyong and Jia, Baoxiong and Wang, Yan and Zhu, Ziyu and Linghu, Xiongkun and Li, Qing and Zhu, Song-Chun and Huang, Siyuan},
    title     = {Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24570-24581}
}

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

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


title: Generating Multimodal Driving Scenes via Next-Scene Prediction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6844-6853
cities: ["Xi'an", "Beijing", "Lausanne", "Beijing"]
countries: ["China", "China", "Switzerland", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Generating_Multimodal_Driving_Scenes_via_Next-Scene_Prediction_CVPR_2025_paper.html

authors:
Yanhao Wu, Haoyang Zhang, Tianwei Lin, Lichao Huang, Shujie Luo, Rui Wu, Congpei Qiu, Wei Ke, Tong Zhang

abstract:
Generative models in Autonomous Driving (AD) enable diverse scenario creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capability of generating controllable scenes for comprehensive evaluation of AD systems. In this paper, we introduce a multimodal generation framework that incorporates four major data modalities, including a novel addition of the map modality. With tokenized modalities, our scene sequence generation framework autoregressively predicts each scene while managing computational demands through a two-stage approach. The Temporal AutoRegressive (TAR) component captures inter-frame dynamics for each modality, while the Ordered AutoRegressive (OAR) component aligns modalities within each scene by sequentially predicting tokens in a fixed order. To maintain coherence between map and ego-action modalities, we introduce the Action-aware Map Alignment (AMA) module, which applies a transformation based on the ego-action to maintain coherence between these two modalities.  Our framework effectively generates complex, realistic driving scenes over extended sequences, ensuring multimodal consistency and offering fine-grained control over scene elements. Project page: https://yanhaowu.github.io/UMGen/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Generating_Multimodal_Driving_Scenes_via_Next-Scene_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Generating_Multimodal_Driving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yanhao and Zhang, Haoyang and Lin, Tianwei and Huang, Lichao and Luo, Shujie and Wu, Rui and Qiu, Congpei and Ke, Wei and Zhang, Tong},
    title     = {Generating Multimodal Driving Scenes via Next-Scene Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6844-6853}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, XJTU", "city": "Xi'an", "country": "China"}, {"affiliation": "Horizon Robotics", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer and Communication Sciences, EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Software Engineering, XJTU", "Horizon Robotics", "School of Computer and Communication Sciences, EPFL", "University of Chinese Academy of Sciences"]


authors: Jeongwan On, Kyeonghwan Gwak, Gunyoung Kang, Junuk Cha, Soohyun Hwang, Hyein Hwang, Seungryul Baek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17437-17447
raw_affiliation: [{"affiliation": "UNIST", "city": "Ulsan", "country": "South Korea"}]
affiliations: ["UNIST"]
cities: ["Ulsan"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/On_BIGS_Bimanual_Category-agnostic_Interaction_Reconstruction_from_Monocular_Videos_via_3D_CVPR_2025_paper.html

title:
BIGS: Bimanual Category-agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting

abstract:
Reconstructing 3Ds of hand-object interaction (HOI) is a fundamental problem that can find numerous applications. Despite recent advances, there is no comprehensive pipeline yet for bimanual class-agnostic interaction reconstruction from a monocular RGB video, where two hands and an unknown object are interacting with each other. Previous works tackled the limited hand-object interaction case, where object templates are pre-known or only one hand is involved in the interaction. The bimanual interaction reconstruction exhibits severe occlusions introduced by complex interactions between two hands and an object. To solve this, we  first introduce BIGS (Bimanual Interaction 3D Gaussian Splatting), a method that reconstructs 3D Gaussians of hands and an unknown object from a monocular video. To robustly obtain object Gaussians avoiding severe occlusions, we leverage prior knowledge of pre-trained diffusion model with score distillation sampling (SDS) loss, to reconstruct unseen object parts. For hand Gaussians, we exploit the 3D priors of hand model (i.e., MANO) and share a single Gaussian for two hands to effectively accumulate hand 3D information, given limited views. To further consider the 3D alignment between hands and objects, we include the interacting-subjects optimization step during Gaussian optimization. Our method achieves the state-of-the-art accuracy on two challenging datasets, in terms of 3D hand pose estimation (MPJPE), 3D object reconstruction (CDh, CDo, F10), and rendering quality (PSNR, SSIM, LPIPS), respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/On_BIGS_Bimanual_Category-agnostic_Interaction_Reconstruction_from_Monocular_Videos_via_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/On_BIGS_Bimanual_Category-agnostic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{On_2025_CVPR,
    author    = {On, Jeongwan and Gwak, Kyeonghwan and Kang, Gunyoung and Cha, Junuk and Hwang, Soohyun and Hwang, Hyein and Baek, Seungryul},
    title     = {BIGS: Bimanual Category-agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17437-17447}
}


title: APT: Adaptive Personalized Training for Diffusion Models with Limited Data
authors: JungWoo Chae, Jiyoon Kim, JaeWoong Choi, Kyungyul Kim, Sangheum Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28619-28628
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chae_APT_Adaptive_Personalized_Training_for_Diffusion_Models_with_Limited_Data_CVPR_2025_paper.html

abstract:
Personalizing diffusion models using limited data presents significant challenges, including overfitting, loss of prior knowledge, and degradation of text alignment. Overfitting leads to shifts in the noise prediction distribution, disrupting the denoising trajectory and causing the model to lose semantic coherence. In this paper, we propose Adaptive Personalized Training, a novel framework that mitigates overfitting by employing adaptive training strategies and regularizing the model's internal representations during fine-tuning. APT consists of three key components: (1) Adaptive Training Adjustment, which introduces an overfitting indicator to detect the degree of overfitting at each time step bin and applies adaptive data augmentation and adaptive loss weighting based on this indicator; (2)Representation Stabilization, which regularizes the mean and variance of intermediate feature maps to prevent excessive shifts in noise prediction; and (3) Attention Alignment for Prior Knowledge Preservation, which aligns the cross-attention maps of the fine-tuned model with those of the pretrained model to maintain prior knowledge and semantic coherence. Through extensive experiments, we demonstrate that APT effectively mitigates overfitting, preserves prior knowledge, and outperforms existing methods in generating high-quality, diverse images with limited reference data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chae_APT_Adaptive_Personalized_Training_for_Diffusion_Models_with_Limited_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chae_APT_Adaptive_Personalized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chae_2025_CVPR,
    author    = {Chae, JungWoo and Kim, Jiyoon and Choi, JaeWoong and Kim, Kyungyul and Hwang, Sangheum},
    title     = {APT: Adaptive Personalized Training for Diffusion Models with Limited Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28619-28628}
}

raw_affiliation:
[{"affiliation": "LGCNS AI Research", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Department of Data Science, Seoul National University of Science and Technology", "city": "Seoul", "country": "South Korea"}]

affiliations:
["LGCNS AI Research", "Department of Data Science, Seoul National University of Science and Technology"]


title: Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D Generation
authors: Xiang Li, Zixuan Huang, Anh Thai, James M. Rehg
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 743-752
affiliations: ["University of Illinois at Urbana-Champaign", "Georgia Institute of Technology"]
cities: ["Urbana-Champaign", "Atlanta"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Symmetry_Strikes_Back_From_Single-Image_Symmetry_Detection_to_3D_Generation_CVPR_2025_paper.html

abstract:
Symmetry is a ubiquitous and fundamental property in the visual world, serving as a critical cue for perception and structure interpretation. This paper investigates the detection of 3D reflection symmetry from a single RGB image, and reveals its significant benefit on single-image 3D generation. We introduce Reflect3D, a scalable, zero-shot symmetry detector capable of robust generalization to diverse and real-world scenarios. Inspired by the success of foundation models, our method scales up symmetry detection with a transformer-based architecture. We also leverage generative priors from multi-view diffusion models to address the inherent ambiguity in single-view symmetry detection. Extensive evaluations on various data sources demonstrate that Reflect3D establishes a new state-of-the-art in single-image symmetry detection. Furthermore, we show the practical benefit of incorporating detected symmetry into single-image 3D generation pipelines through a symmetry-aware optimization process. The integration of symmetry significantly enhances the structural accuracy, cohesiveness, and visual fidelity of the reconstructed 3D geometry and textures, advancing the capabilities of 3D content creation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Symmetry_Strikes_Back_From_Single-Image_Symmetry_Detection_to_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Symmetry_Strikes_Back_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiang and Huang, Zixuan and Thai, Anh and Rehg, James M.},
    title     = {Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {743-752}
}

raw_affiliation:
[{"affiliation": "University of Illinois at Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


title: Frequency-Biased Synergistic Design for Image Compression and Compensation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12820-12829
cities: ["Shanghai", "Hangzhou", "Southfield"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Frequency-Biased_Synergistic_Design_for_Image_Compression_and_Compensation_CVPR_2025_paper.html

authors:
Jiaming Liu, Qi Zheng, Zihao Liu, Yilian Zhong, Peiye Liu, Tao Liu, Shusong Xu, Yanheng Lu, Sicheng Li, Dimin Niu, Yibo Fan

abstract:
Compression artifacts removal (CAR), an effective post-processing method to reduce compression distortion in edge-side codecs, demonstrates remarkable results by utilizing convolutional neural networks (CNNs) on high computational power cloud side. Traditional image compression reduces redundancy in the frequency domain, and we observed that CNNs also exhibit a bias in frequency domain when handling compression distortions. However, no prior research leverages this frequency bias to design compression methods tailored to CAR CNNs, or vice versa. In this paper, we present a synergistic design that bridges the gap between image compression and learnable compensation for CAR. Our investigation reveals that different compensation networks have varying effects on low and high-frequencies. Building upon these insights, we propose a pioneering redesign of the quantization process, a fundamental component in lossy image compression, to more effectively compress low-frequency information. Additionally, we devise a novel compensation framework that applies different neural networks for reconstructing different frequencies, incorporating a basis attention block to prioritize intentionally dropped low-frequency information, thereby enhancing the overall compensation. We instantiate two compensation networks based on this synergistic design and conduct extensive experiments on three image compression standards, demonstrating that our approach significantly reduces bitrate consumption while delivering high perceptual quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Frequency-Biased_Synergistic_Design_for_Image_Compression_and_Compensation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Frequency-Biased_Synergistic_Design_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiaming and Zheng, Qi and Liu, Zihao and Zhong, Yilian and Liu, Peiye and Liu, Tao and Xu, Shusong and Lu, Yanheng and Li, Sicheng and Niu, Dimin and Fan, Yibo},
    title     = {Frequency-Biased Synergistic Design for Image Compression and Compensation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12820-12829}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Integrated Chips and Systems, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Damo Academy & Hupan Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Lawrence Technological University", "city": "Southfield", "country": "USA"}]

affiliations:
["State Key Laboratory of Integrated Chips and Systems, Fudan University", "Damo Academy & Hupan Lab, Alibaba Group", "Lawrence Technological University"]


title: PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering
authors: Yifan Gao, Zihang Lin, Chuanbin Liu, Min Zhou, Tiezheng Ge, Bo Zheng, Hongtao Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8083-8093
affiliations: ["University of Science and Technology of China", "Taobao & Tmall Group of Alibaba"]
cities: ["Hefei", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_PosterMaker_Towards_High-Quality_Product_Poster_Generation_with_Accurate_Text_Rendering_CVPR_2025_paper.html

abstract:
Product posters, which integrate subject, scene, and text, are crucial promotional tools for attracting customers. Creating such posters using modern image generation methods is valuable, while the main challenge lies in accurately rendering text, especially for complex writing systems like Chinese, which contains over 10,000 individual characters. In this work, we identify the key to precise text rendering as constructing a character-discriminative visual feature as a control signal. Based on this insight, we propose a robust character-wise representation as control and we develop TextRenderNet, which achieves a high text rendering accuracy of over 90%. Another challenge in poster generation is maintaining the fidelity of user-specific products. We address this by introducing SceneGenNet, an inpainting-based model, and propose subject fidelity feedback learning to further enhance fidelity. Based on TextRenderNet and SceneGenNet, we present PosterMaker, an end-to-end generation framework. To optimize PosterMaker efficiently, we implement a two-stage training strategy that decouples text rendering and background generation learning. Experimental results show that PosterMaker outperforms existing baselines by a remarkable margin, which demonstrates its effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_PosterMaker_Towards_High-Quality_Product_Poster_Generation_with_Accurate_Text_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_PosterMaker_Towards_High-Quality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Yifan and Lin, Zihang and Liu, Chuanbin and Zhou, Min and Ge, Tiezheng and Zheng, Bo and Xie, Hongtao},
    title     = {PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8083-8093}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Taobao & Tmall Group of Alibaba", "city": "Hangzhou", "country": "China"}]


title: Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering
authors: Cheng Sun, Jaesung Choe, Charles Loop, Wei-Chiu Ma, Yu-Chiang Frank Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16187-16196
affiliations: ["NVIDIA", "Cornell University", "National Taiwan University"]
cities: ["Santa Clara", "Ithaca", "Taipei"]
countries: ["USA", "USA", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Sparse_Voxels_Rasterization_Real-time_High-fidelity_Radiance_Field_Rendering_CVPR_2025_paper.html

abstract:
We propose an efficient radiance field rendering algorithm that incorporates a rasterization process on adaptive sparse voxels without neural networks or 3D Gaussians. There are two key contributions coupled with the proposed system. The first is to adaptively and explicitly allocate sparse voxels to different levels of detail within scenes, faithfully reproducing scene details with 65536^3 grid resolution while achieving high rendering frame rates. Second, we customize a rasterizer for efficient adaptive sparse voxels rendering. We render voxels in the correct depth order by using ray direction-dependent Morton ordering, which avoids the well-known popping artifact found in Gaussian splatting. Our method improves the previous neural-free voxel model by over 4db PSNR and more than 10x FPS speedup, achieving state-of-the-art comparable novel-view synthesis results. Additionally, our voxel representation is seamlessly compatible with grid-based 3D processing techniques such as Volume Fusion, Voxel Pooling, and Marching Cubes, enabling a wide range of future extensions and applications. Code at https://github.com/NVlabs/svraster

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Sparse_Voxels_Rasterization_Real-time_High-fidelity_Radiance_Field_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Sparse_Voxels_Rasterization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Cheng and Choe, Jaesung and Loop, Charles and Ma, Wei-Chiu and Wang, Yu-Chiang Frank},
    title     = {Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16187-16196}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}]


authors: Haobin Zhong, Shuai He, Anlong Ming, Huadong Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2935-2944
affiliations: ["Beijing University of Posts and Telecommunications"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Rethinking_Personalized_Aesthetics_Assessment_Employing_Physique_Aesthetics_Assessment_as_An_CVPR_2025_paper.html

title:
Rethinking Personalized Aesthetics Assessment: Employing Physique Aesthetics Assessment as An Exemplification

abstract:
The Personalized Aesthetics Assessment (PAA) aims to accurately predict an individual's unique perception of aesthetics. With the surging demand for customization, PAA enables applications to generate personalized outcomes by aligning with individual aesthetic preferences. The prevailing PAA paradigm involves two stages: pre-training and fine-tuning, but it faces three inherent challenges: 1) The model is pre-trained using datasets of the Generic Aesthetics Assessment (GAA), but the collective preferences of GAA lead to conflicts in individualized aesthetic predictions. 2) The scope and stage of personalized surveys are related to both the user and the assessed object; however, the prevailing personalized surveys fail to adequately address assessed objects' characteristics. 3) During application usage, the cumulative multimodal feedback from an individual holds great value that should be considered for improving the PAA model but unfortunately attracts insufficient attention. To address the aforementioned challenges, we introduce a new PAA paradigm called PAA+, which is structured into three distinct stages: pre-training, fine-tuning, and continual learning. Furthermore, to better reflect individual differences, we employ a familiar and intuitive application, physique aesthetics assessment (PhysiqueAA), to validate the PAA+ paradigm.  We propose a dataset called PhysiqueAA50K, consisting of over 50,000 annotated physique images. Furthermore, we develop a PhysiqueAA framework (PhysiqueFrame) and conduct a large-scale benchmark, achieving state-of-the-art (SOTA) performance. Our research is expected to provide an innovative roadmap and application for the PAA community. The code and dataset are available in here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Rethinking_Personalized_Aesthetics_Assessment_Employing_Physique_Aesthetics_Assessment_as_An_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Rethinking_Personalized_Aesthetics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Haobin and He, Shuai and Ming, Anlong and Ma, Huadong},
    title     = {Rethinking Personalized Aesthetics Assessment: Employing Physique Aesthetics Assessment as An Exemplification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2935-2944}
}

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


title: You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
authors: Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo, Tiejun Huang, Lulu Tang, Xinlong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2016-2029
affiliations: ["Beijing Academy of Artificial Intelligence", "Westlake University"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_You_See_it_You_Got_it_Learning_3D_Creation_on_CVPR_2025_paper.html

abstract:
Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or  2D diffusion priors for 3D content creation.  However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion model trained on large-scale Internet videos for open-world 3D creation. The model aims to Get 3D knowledge by solely Seeing the visual contents from the vast and rapidly growing video data --- You See it, You Got it. To achieve this, we first scale up the training data using a proposed data curation pipeline that automatically filters out multi-view inconsistencies and insufficient observations from source videos. This results in a high-quality, richly diverse, large-scale dataset of multi-view images, termed WebVi3D, containing 320M frames from 16M video clips. Nevertheless, learning generic 3D priors from videos without explicit 3D geometry or camera pose annotations is nontrivial, and annotating poses for web-scale videos is prohibitively expensive. To eliminate the need for pose conditions, we introduce  an innovative visual-condition - a purely 2D-inductive visual signal generated  by adding time-dependent noise to the masked video data. Finally, we introduce a novel visual-conditional 3D generation framework by integrating See3D into a warping-based pipeline for high-fidelity 3D generation. Our numerical and visual comparisons on single and sparse reconstruction benchmarks show that See3D, trained on cost-effective and scalable video data, achieves notable zero-shot and open-world generation capabilities, markedly outperforming models trained on costly and constrained 3D datasets. Additionally, our model naturally supports other image-conditioned 3D creation tasks, such as 3D editing, without further fine-tuning. Please refer to our project page at: https://vision.baai.ac.cn/see3d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_You_See_it_You_Got_it_Learning_3D_Creation_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_You_See_it_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Baorui and Gao, Huachen and Deng, Haoge and Luo, Zhengxiong and Huang, Tiejun and Tang, Lulu and Wang, Xinlong},
    title     = {You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2016-2029}
}

raw_affiliation:
[{"affiliation": "Beijing Academy of Artificial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}]


title: MambaIC: State Space Models for High-Performance Learned Image Compression
authors: Fanhu Zeng, Hao Tang, Yihua Shao, Siyu Chen, Ling Shao, Yan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18041-18050
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_MambaIC_State_Space_Models_for_High-Performance_Learned_Image_Compression_CVPR_2025_paper.html

abstract:
A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational inefficiency and poor redundancy modeling still pose significant bottlenecks, limiting practical applications. Inspired by the effectiveness of state space models (SSMs) in capturing long-range dependencies, we leverage SSMs to address computational inefficiency in existing methods and improve image compression from multiple perspectives. In this paper, we systematically analyze the advantages of SSMs for better integration and propose an enhanced image compression approach through refined context modeling, which we term MambaIC. Specifically, we explore context modeling to adaptively refine the representation of hidden states. Additionally, we introduce window-based local attention into channel-spatial entropy modeling to reduce potential spatial redundancy during compression, thereby increasing efficiency. Comprehensive qualitative and quantitative results validate the effectiveness and efficiency of our approach, particularly for high-resolution image compression. Code is released at https://github.com/AuroraZengfh/MambaIC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_MambaIC_State_Space_Models_for_High-Performance_Learned_Image_Compression_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_MambaIC_State_Space_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Fanhu and Tang, Hao and Shao, Yihua and Chen, Siyu and Shao, Ling and Wang, Yan},
    title     = {MambaIC: State Space Models for High-Performance Learned Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18041-18050}
}

raw_affiliation:
[{"affiliation": "Institute for AI Industry Research (AIR), Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "UCAS-Terminus AI Lab, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Institute for AI Industry Research (AIR), Tsinghua University", "School of Computer Science, Peking University", "University of Science and Technology Beijing", "UCAS-Terminus AI Lab, University of Chinese Academy of Sciences"]


title: SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting
authors: Chenyu Zhang, Kunlun Xu, Zichen Liu, Yuxin Peng, Jiahuan Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30032-30041
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_SCAP_Transductive_Test-Time_Adaptation_via_Supportive_Clique-based_Attribute_Prompting_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) encounter considerable challenges when adapting to domain shifts stemming from changes in data distribution. Test-time adaptation (TTA) has emerged as a promising approach to enhance VLM performance under such conditions. In practice, test data often arrives in batches, leading to increasing interest in the transductive TTA setting. However, existing TTA methods primarily focus on individual test samples, overlooking crucial cross-sample correlations within a batch. While recent ViT-based TTA methods have introduced batch-level adaptation, they remain suboptimal for VLMs due to inadequate integration of the text modality. To address these limitations, we propose a novel transductive TTA framework, Supportive Clique-based Attribute Prompting (SCAP), which effectively combines visual and textual information to enhance adaptation by generating fine-grained attribute prompts across test batches. SCAP first forms supportive cliques of test samples in an unsupervised manner based on visual similarity and learns an attribute prompt for each clique, capturing shared attributes critical for adaptation. For each test sample, SCAP aggregates attribute prompts from its associated cliques, providing enriched contextual information. To ensure adaptability over time, we incorporate a retention module that dynamically updates attribute prompts and their associated attributes as new data arrives.  Comprehensive experiments across multiple benchmarks demonstrate that SCAP outperforms existing state-of-the-art methods, significantly advancing VLM generalization under domain shifts. Our code is available at https://github.com/zhoujiahuan1991/CVPR2025-SCAP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_SCAP_Transductive_Test-Time_Adaptation_via_Supportive_Clique-based_Attribute_Prompting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_SCAP_Transductive_Test-Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chenyu and Xu, Kunlun and Liu, Zichen and Peng, Yuxin and Zhou, Jiahuan},
    title     = {SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30032-30041}
}

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


authors: Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16520-16531
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html

title:
Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting

abstract:
Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians. This generalized Network generates the motion of Gaussians for each target frame in the time required for a single inference. Second, we propose a Key-frame-guided Streaming Strategy that refines each key frame, enabling accurate reconstruction of temporally complex scenes while mitigating error accumulation. We conducted extensive in-domain and cross-domain evaluations, demonstrating that our approach can achieve streaming with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Instant_Gaussian_Stream_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Jinbo and Peng, Rui and Wang, Zhiyan and Tang, Luyang and Yang, Jiayu and Liang, Jie and Wu, Jiahao and Wang, Ronggang},
    title     = {Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16520-16531}
}

raw_affiliation:
[{"affiliation": "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "Pengcheng Laboratory"]


title: Locality-Aware Zero-Shot Human-Object Interaction Detection
authors: Sanghyun Kim, Deunsol Jung, Minsu Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20190-20200
affiliations: ["Pohang University of Science and Technology (POSTECH)"]
cities: ["Pohang"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Locality-Aware_Zero-Shot_Human-Object_Interaction_Detection_CVPR_2025_paper.html

abstract:
Recent methods for zero-shot Human-Object Interaction (HOI) detection typically leverage the generalization ability of large Vision-Language Model (VLM), i.e., CLIP, on unseen categories, showing impressive results on various zero-shot settings. However, existing methods struggle to adapt CLIP representations for human-object pairs, as CLIP tends to overlook fine-grained information necessary for distinguishing interactions. To address this issue, we devise, LAIN, a novel zero-shot HOI detection framework designed to enhance the locality and interaction awareness of CLIP representations. The locality awareness, which involves capturing fine-grained details and the spatial structure of individual objects, is achieved by aggregating the information and spatial priors of adjacent neighborhood patches. The interaction awareness, which involves identifying whether and how a human is interacting with an object, is achieved by capturing the interaction pattern between the human and the object. By infusing locality and interaction awareness into CLIP representation, LAIN captures detailed information about the human-object pairs. Our extensive experiments on existing benchmarks show that LAIN outperforms previous methods in various zero-shot settings, demonstrating the importance of locality and interaction awareness for effective zero-shot HOI detection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Locality-Aware_Zero-Shot_Human-Object_Interaction_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Locality-Aware_Zero-Shot_Human-Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sanghyun and Jung, Deunsol and Cho, Minsu},
    title     = {Locality-Aware Zero-Shot Human-Object Interaction Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20190-20200}
}

raw_affiliation:
[{"affiliation": "Pohang University of Science and Technology (POSTECH)", "city": "Pohang", "country": "South Korea"}]


title: PEACE: Empowering Geologic Map Holistic Understanding with MLLMs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3899-3908
affiliations: ["Microsoft Research", "Chinese Academy of Geological Sciences", "Wuhan University"]
cities: ["Redmond", "Beijing", "Wuhan"]
countries: ["USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_PEACE_Empowering_Geologic_Map_Holistic_Understanding_with_MLLMs_CVPR_2025_paper.html

authors:
Yangyu Huang, Tianyi Gao, Haoran Xu, Qihao Zhao, Yang Song, Zhipeng Gui, Tengchao Lv, Hao Chen, Lei Cui, Scarlett Li, Furu Wei

abstract:
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispensable in various fields, including disaster assessment, resource exploration, and civil engineering. Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding. This gap is primarily due to the challenging nature of cartographic generalization, which involves handling high-resolution map, managing multiple associated components, and requiring domain-specific knowledge. To quantify this gap, we construct **GeoMap-Bench**, the first-ever benchmark for evaluating MLLMs in geologic map understanding, which assesses the full-scale abilities in extracting, referring, grounding, reasoning, and analyzing. To bridge this gap, we introduce **GeoMap-Agent**, the inaugural agent designed for geologic map understanding, which features three modules: Hierarchical Information Extraction (HIE), Domain Knowledge Injection (DKI), and Prompt-enhanced Question Answering (PEQA). Inspired by the interdisciplinary collaboration among human scientists, an AI expert group acts as consultants, utilizing a diverse tool pool to comprehensively analyze questions. Through comprehensive experiments, GeoMap-Agent achieves an overall score of 0.811 on GeoMap-Bench, significantly outperforming 0.369 of GPT-4o. Our work, em**P**owering g**E**ologic m**A**p holisti**C** und**E**rstanding (**PEACE**) with MLLMs, paves the way for advanced AI applications in geology, enhancing the efficiency and accuracy of geological investigations. The code and data are available at https://github.com/microsoft/PEACE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_PEACE_Empowering_Geologic_Map_Holistic_Understanding_with_MLLMs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_PEACE_Empowering_Geologic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yangyu and Gao, Tianyi and Xu, Haoran and Zhao, Qihao and Song, Yang and Gui, Zhipeng and Lv, Tengchao and Chen, Hao and Cui, Lei and Li, Scarlett and Wei, Furu},
    title     = {PEACE: Empowering Geologic Map Holistic Understanding with MLLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3899-3908}
}

raw_affiliation:
[{"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}, {"affiliation": "Chinese Academy of Geological Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]


title: Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better
authors: Zihang Lai, Andrea Vedaldi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22809-22819
affiliations: ["Visual Geometry Group (VGG), University of Oxford"]
cities: ["Oxford"]
countries: ["UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Tracktention_Leveraging_Point_Tracking_to_Attend_Videos_Faster_and_Better_CVPR_2025_paper.html

abstract:
Temporal consistency is critical in video prediction. Traditional methods, such as temporal attention mechanisms and 3D convolutions, often struggle with significant object movements and fail to capture long-range temporal dependencies in dynamic scenes. To address these limitations, we propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks -- sequences of corresponding points across frames. By incorporating these motion cues, the Tracktention Layer enhances temporal alignment and effectively handles complex object motions, maintaining consistent feature representations over time. Our approach is computationally efficient and can be seamlessly integrated into existing models, such as Vision Transformers, with minimal modification. Empirical evaluations on standard video estimation benchmarks demonstrate that models augmented with the Tracktention Layer exhibit significantly improved temporal consistency compared to baseline models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Tracktention_Leveraging_Point_Tracking_to_Attend_Videos_Faster_and_Better_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_Tracktention_Leveraging_Point_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Zihang and Vedaldi, Andrea},
    title     = {Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22809-22819}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group (VGG), University of Oxford", "city": "Oxford", "country": "UK"}]


authors: Zirun Guo, Tao Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2945-2954
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_ConceptGuard_Continual_Personalized_Text-to-Image_Generation_with_Forgetting_and_Confusion_Mitigation_CVPR_2025_paper.html

title:
ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation

abstract:
Diffusion customization methods have achieved impressive results with only a minimal number of user-provided images. However, existing approaches customize concepts collectively, whereas real-world applications often require sequential concept integration. This sequential nature can lead to catastrophic forgetting, where previously learned concepts are lost. In this paper, we investigate concept forgetting and concept confusion in the continual customization. To tackle these challenges, we present ConceptGuard, a comprehensive approach that combines shift embedding, concept-binding prompts and memory preservation regularization, supplemented by a priority queue which can adaptively update the importance and occurrence order of different concepts. These strategies can dynamically update, unbind and learn the relationship of the previous concepts, thus alleviating concept forgetting and confusion. Through comprehensive experiments, we show that our approach outperforms all the baseline methods consistently and significantly in both quantitative and qualitative analyses.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_ConceptGuard_Continual_Personalized_Text-to-Image_Generation_with_Forgetting_and_Confusion_Mitigation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Zirun and Jin, Tao},
    title     = {ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2945-2954}
}


title: Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation
authors: Yu Qi, Yuanchen Ju, Tianming Wei, Chi Chu, Lawson L.S. Wong, Huazhe Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17383-17393
cities: ["Shanghai", "Shenyang", "Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Two_by_Two_Learning_Multi-Task_Pairwise_Objects_Assembly_for_Generalizable_CVPR_2025_paper.html

abstract:
3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchmarks and datasets predominantly focus on assembling geometric fragments or factory parts, which fall short in addressing the complexities of everyday object interactions and assemblies. To bridge this gap, we present 2BY2, a large-scale annotated dataset for daily pairwise objects assembly, covering 18 fine-grained tasks that reflect real-life scenarios, such as plugging into sockets, arranging flowers in vases, and inserting bread into toasters. 2BY2 dataset includes 1,034 instances and 517 pairwise objects with pose and symmetry annotations, requiring approaches that align geometric shapes while accounting for functional and spatial relationships between objects. Leveraging the 2BY2 dataset, we propose a two-step SE(3) pose estimation method with equivariant features for assembly constraints. Compared to previous shape assembly methods, our approach achieves state-of-the-art performance across all 18 tasks in the 2BY2 dataset.  Additionally, robot experiments further validate the reliability and generalization ability of our method for complex 3D assembly tasks. More details and demonstrations can be found at https://tea-lab.github.io/TwoByTwo/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_Two_by_Two_Learning_Multi-Task_Pairwise_Objects_Assembly_for_Generalizable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_Two_by_Two_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Yu and Ju, Yuanchen and Wei, Tianming and Chu, Chi and Wong, Lawson L.S. and Xu, Huazhe},
    title     = {Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17383-17393}
}

raw_affiliation:
[{"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}, {"affiliation": "IIIS, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Qi Zhi Institute", "Northeastern University", "IIIS, Tsinghua University", "Shanghai Jiao Tong University", "Shanghai AI Laboratory"]


title: SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion
authors: Xiyue Guo, Jiarui Hu, Junjie Hu, Hujun Bao, Guofeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11929-11938
affiliations: ["State Key Lab of CAD&CG, Zhejiang University", "Chinese University of Hong Kong, Shenzhen"]
cities: ["Hangzhou", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_SGFormer_Satellite-Ground_Fusion_for_3D_Semantic_Scene_Completion_CVPR_2025_paper.html

abstract:
Recently, camera-based solutions have been extensively explored for scene semantic completion (SSC). Despite their success in visible areas, existing methods struggle to capture complete scene semantics due to frequent visual occlusions. To address this limitation, this paper presents the first satellite-ground cooperative SSC framework, i.e., SGFormer, exploring the potential of satellite-ground image pairs in the SSC task. Specifically, we propose a dual-branch architecture that encodes orthogonal satellite and ground views in parallel, unifying them into a common domain. Additionally, we design a ground-view guidance strategy that pre-corrects satellite image biases during feature encoding, addressing misalignment between satellite and ground views. Moreover, we develop an adaptive weighting strategy that balances contributions from satellite and ground views. Experiments demonstrate that SGFormer outperforms the state of the art on SemanticKITTI and SSCBench-KITTI-360 datasets. Our code is available on https://github.com/gxytcrc/SGFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_SGFormer_Satellite-Ground_Fusion_for_3D_Semantic_Scene_Completion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_SGFormer_Satellite-Ground_Fusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Xiyue and Hu, Jiarui and Hu, Junjie and Bao, Hujun and Zhang, Guofeng},
    title     = {SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11929-11938}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]


title: MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8105-8116
affiliations: ["DFKI", "RPTU Kaiserslautern-Landau", "MindGarage", "BITS Pilani"]
cities: ["Kaiserslautern", "Kaiserslautern", "Unknown", "Hyderabad"]
countries: ["Germany", "Germany", "Unknown", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sinha_MARVEL-40M_Multi-Level_Visual_Elaboration_for_High-Fidelity_Text-to-3D_Content_Creation_CVPR_2025_paper.html

authors:
Sankalp Sinha, Mohammad Sadil Khan, Muhammad Usama, Shino Sam, Didier Stricker, Sk Aziz Ali, Muhammad Zeshan Afzal

abstract:
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 million 3D assets aggregated from seven major 3D datasets. Our contribution is a novel multi-stage annotation pipeline that integrates open-source pretrained multi-view VLMs and LLMs to automatically produce multi-level descriptions, ranging from detailed (150-200 words) to concise semantic tags (10-20 words). This structure supports both fine-grained 3D reconstruction and rapid prototyping. Furthermore, we incorporate human metadata from source datasets into our annotation pipeline to add domain-specific information in our annotation and reduce VLM hallucinations. Additionally, we develop MARVEL-FX3D, a two-stage text-to-3D pipeline. We fine-tune Stable Diffusion with our annotations and use a pretrained image-to-3D network to generate 3D textured meshes within 15s.  Extensive evaluations show that MARVEL-40M+ significantly outperforms existing datasets in annotation quality and linguistic diversity, achieving win rates of 72.41% by GPT-4 and 73.40% by human evaluators. Project page is available at https://sankalpsinha-cmos.github.io/MARVEL/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sinha_MARVEL-40M_Multi-Level_Visual_Elaboration_for_High-Fidelity_Text-to-3D_Content_Creation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sinha_MARVEL-40M_Multi-Level_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sinha_2025_CVPR,
    author    = {Sinha, Sankalp and Khan, Mohammad Sadil and Usama, Muhammad and Sam, Shino and Stricker, Didier and Ali, Sk Aziz and Afzal, Muhammad Zeshan},
    title     = {MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8105-8116}
}

raw_affiliation:
[{"affiliation": "DFKI", "city": "Kaiserslautern", "country": "Germany"}, {"affiliation": "RPTU Kaiserslautern-Landau", "city": "Kaiserslautern", "country": "Germany"}, {"affiliation": "MindGarage", "city": "Unknown", "country": "Unknown"}, {"affiliation": "BITS Pilani", "city": "Hyderabad", "country": "India"}]


title: Random Conditioning for Diffusion Model Compression with Distillation
authors: Dohyun Kim, Sehwan Park, Geonhee Han, Seung Wook Kim, Paul Hongsuck Seo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18607-18618
affiliations: ["Dept. of CSE, Korea University", "NVIDIA"]
cities: ["Seoul", "Santa Clara"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Random_Conditioning_for_Diffusion_Model_Compression_with_Distillation_CVPR_2025_paper.html

abstract:
Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation--transferring knowledge from a complex teacher to a simpler student model--has been widely studied in recognition tasks, particularly for transferring concepts unseen during student training. However, its application to diffusion models remains underexplored, especially in enabling student models to generate concepts not covered by the training images. In this work, we propose Random Conditioning, a novel approach that pairs noised images with randomly selected text conditions to enable efficient, image-free knowledge distillation.  By leveraging this technique, we show that the student can generate concepts unseen in the training images. When applied to conditional diffusion model distillation, our method allows the student to explore the condition space without generating condition-specific images, resulting in notable improvements in both generation quality and efficiency. This promotes resource-efficient deployment of generative diffusion models, broadening their accessibility for both research and real-world applications. Code, models, and datasets are available at: https://dohyun-as.github.io/Random-Conditioning

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Random_Conditioning_for_Diffusion_Model_Compression_with_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Random_Conditioning_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Dohyun and Park, Sehwan and Han, Geonhee and Kim, Seung Wook and Seo, Paul Hongsuck},
    title     = {Random Conditioning for Diffusion Model Compression with Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18607-18618}
}

raw_affiliation:
[{"affiliation": "Dept. of CSE, Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Hierarchical Gaussian Mixture Model Splatting for Efficient and Part Controllable 3D Generation
authors: Qitong Yang, Mingtao Feng, Zijie Wu, Weisheng Dong, Fangfang Wu, Yaonan Wang, Ajmal Mian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11104-11114
cities: ["Xi'an", "Changsha", "Perth", "Nanchang"]
countries: ["China", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Hierarchical_Gaussian_Mixture_Model_Splatting_for_Efficient_and_Part_Controllable_CVPR_2025_paper.html

abstract:
3D content creation has achieved significant progress in terms of both quality and speed. Although current Gaussian Splatting-based methods can produce 3D objects within seconds, they are still limited by complex preprocessing or low controllability. In this paper, we introduce a novel framework designed to efficiently and controllably generate high-resolution 3D models from text promptsor image. Our key insights are three-fold: 1) Hierarchical Gaussian Mixture Model Splatting: We propose an hybrid hierarchical representation to extract fixed number of fine-grained Gaussians with multiscale details from textured object, also establish part-level representation of Gaussians primitives. 2) Mamba with adaptive tree topology: We present a diffusion mamba with tree-topology to adaptively generate Gaussians with disordered spatial structures, without the need for complex preprocessing and maintain linear complexity generation. 3) Controllable Generation: Building on the HGMM tree, we introduce a cascaded diffusion framework combining controllable implicit latent generation, which progressively generates condition-driven latents, and explicit splatting generation, which transforms latents into high-quality Gaussian primitives. Extensive experiments demonstrate the high fidelity and efficiency of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Hierarchical_Gaussian_Mixture_Model_Splatting_for_Efficient_and_Part_Controllable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Hierarchical_Gaussian_Mixture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Qitong and Feng, Mingtao and Wu, Zijie and Dong, Weisheng and Wu, Fangfang and Wang, Yaonan and Mian, Ajmal},
    title     = {Hierarchical Gaussian Mixture Model Splatting for Efficient and Part Controllable 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11104-11114}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "The University of Western Australia", "city": "Perth", "country": "Australia"}, {"affiliation": "Jiangxi Communication Terminal Industrial Technology Research Institute", "city": "Nanchang", "country": "China"}]

affiliations:
["Xidian University", "Hunan University", "The University of Western Australia", "Jiangxi Communication Terminal Industrial Technology Research Institute"]


title: ERUPT: Efficient Rendering with Unposed Patch Transformer
authors: Maxim V. Shugaev, Vincent Chen, Maxim Karrenbach, Kyle Ashley, Bridget Kennedy, Naresh P. Cuntoor
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6057-6067
affiliations: ["BlueHalo", "Carnegie Mellon University"]
cities: ["Albuquerque", "Pittsburgh"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shugaev_ERUPT_Efficient_Rendering_with_Unposed_Patch_Transformer_CVPR_2025_paper.html

abstract:
This work addresses the problem of novel view synthesis in diverse scenes from small collections of RGB images. We propose ERUPT (Efficient Rendering with Unposed Patch Transformer) a state-of-the-art scene reconstruction model capable of efficient scene rendering using unposed imagery. We introduce patch-based querying, in contrast to existing pixel-based queries, to reduce the compute required to render a target view. This makes our model highly efficient both during training and at inference, capable of rendering at 600 fps on commercial hardware. Notably, our model is designed to use a learned latent camera pose which allows for training using unposed targets in datasets with sparse or inaccurate ground truth camera pose. We show that our approach can generalize on large real-world data and introduce a new benchmark dataset (MSVS-1M) for latent view synthesis using street-view imagery collected from Mapillary. In contrast to NeRF and Gaussian Splatting which require dense imagery and precise metadata, ERUPT can render novel views of arbitrary scenes with as few as five unposed input images. ERUPT achieves better rendered image quality than current state-of-the-art methods for unposed image synthesis tasks, reduces labeled data requirements by  95% and reduces computational requirements by an order of magnitude, providing efficient novel view synthesis for diverse real-world scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shugaev_ERUPT_Efficient_Rendering_with_Unposed_Patch_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shugaev_ERUPT_Efficient_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shugaev_2025_CVPR,
    author    = {Shugaev, Maxim V. and Chen, Vincent and Karrenbach, Maxim and Ashley, Kyle and Kennedy, Bridget and Cuntoor, Naresh P.},
    title     = {ERUPT: Efficient Rendering with Unposed Patch Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6057-6067}
}

raw_affiliation:
[{"affiliation": "BlueHalo", "city": "Albuquerque", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting
authors: Runsong Zhu, Shi Qiu, Zhengzhe Liu, Ka-Hei Hui, Qianyi Wu, Pheng-Ann Heng, Chi-Wing Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3656-3665
cities: ["Hong Kong", "Hong Kong", "Pittsburgh", "Melbourne"]
countries: ["China", "China", "USA", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Rethinking_End-to-End_2D_to_3D_Scene_Segmentation_in_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Lifting multi-view 2D instance segmentation to a radiance field has proven effective to enhance 3D understanding. Existing works rely on direct matching for end-to-end lifting, yielding inferior results, or employ a two-stage solution constrained by complex pre- or post-processing. In this work, we design Unified-Lift, a new end-to-end object-aware lifting approach that aims for high-quality 3D segmentation based on our object-aware 3D Gaussian representation. To start, we augment each Gaussian point with a Gaussian-level feature learned using a contrastive loss to encode instance information. Importantly, we introduce a learnable object-level codebook to account for individual objects in the scene for an explicit object-level understanding and associate the encoded object-level features with the Gaussian-level point features for segmentation predictions. While promising, achieving effective codebook learning is nontrivial and a naive solution leads to degraded performance. Hence, we formulate the association learning module and the noisy label filtering module for effective and robust codebook learning. We conduct experiments on three benchmarks LERF-Masked, Replica, and Messy Rooms. Both qualitative and quantitative results manifest that our Unified-Lift clearly outperforms existing methods in terms of segmentation quality and time efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Rethinking_End-to-End_2D_to_3D_Scene_Segmentation_in_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Rethinking_End-to-End_2D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Runsong and Qiu, Shi and Liu, Zhengzhe and Hui, Ka-Hei and Wu, Qianyi and Heng, Pheng-Ann and Fu, Chi-Wing},
    title     = {Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3656-3665}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Lingnan University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["The Chinese University of Hong Kong", "Lingnan University", "Carnegie Mellon University", "Monash University"]


title: Quad-Pixel Image Defocus Deblurring: A New Benchmark and Model
authors: Hang Chen, Yin Xie, Xiaoxiu Peng, 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. 5709-5719
affiliations: ["OMNIVISION", "OMNIVISION"]
cities: ["Wuhan", "Santa Clara"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Quad-Pixel_Image_Defocus_Deblurring_A_New_Benchmark_and_Model_CVPR_2025_paper.html

abstract:
Defocus deblurring is a challenging task due to the spatially varying blur. Recent works have shown impressive results in data-driven approaches using dual-pixel (DP) sensors. Quad-pixel (QP) sensors represent an advanced evolution of DP sensors, providing four distinct sub-aperture views in contrast to only two views offered by DP sensors. However, research on QP-based defocus deblurring is scarce. In this paper, we propose a novel end-to-end learning-based approach for defocus deblurring that leverages QP data. To achieve this, we design a QP defocus and all-in-focus image pair acquisition method and provide a QP Defocus Deblurring (QPDD) dataset containing 4,935 image pairs. We then introduce a Local-gate assisted Mamba Network (LMNet), which includes a two-branch encoder and a Simple Fusion Module (SFM) to fully utilize features of sub-aperture views. In particular, our LMNet incorporates a Local-gate assisted Mamba Block (LAMB) that mitigates local pixel forgetting and channel redundancy within Mamba, and effectively captures global and local dependencies. By extending the defocus deblurring task from a DP-based to a QP-based approach, we demonstrate significant improvements in restoring sharp images. Comprehensive experimental evaluations further indicate that our approach outperforms state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Quad-Pixel_Image_Defocus_Deblurring_A_New_Benchmark_and_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Quad-Pixel_Image_Defocus_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Hang and Xie, Yin and Peng, Xiaoxiu and Sun, Lihu and Su, Wenkai and Yang, Xiaodong and Liu, Chengming},
    title     = {Quad-Pixel Image Defocus Deblurring: A New Benchmark and Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5709-5719}
}

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


title: DocVLM: Make Your VLM an Efficient Reader
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29005-29015
raw_affiliation: [{"affiliation": "AWS AI Labs", "city": "Seattle", "country": "USA"}]
affiliations: ["AWS AI Labs"]
cities: ["Seattle"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nacson_DocVLM_Make_Your_VLM_an_Efficient_Reader_CVPR_2025_paper.html

authors:
Mor Shpigel Nacson, Aviad Aberdam, Roy Ganz, Elad Ben Avraham, Alona Golts, Yair Kittenplon, Shai Mazor, Ron Litman

abstract:
Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing. While typical visual tasks perform well with low-resolution inputs, reading-intensive applications demand high-resolution, resulting in significant computational overhead. Using OCR-extracted text in VLM prompts partially addresses this issue but underperforms compared to full-resolution counterpart, as it lacks the complete visual context needed for optimal performance.We introduce DocVLM, a method that integrates an OCR-based modality into VLMs to enhance document processing while preserving original weights. Our approach employs an OCR encoder to capture textual content and layout, compressing these into a compact set of learned queries incorporated into the VLM. Comprehensive evaluations across leading VLMs show that DocVLM significantly reduces reliance on high-resolution images for document understanding.In limited-token regimes (448x448), DocVLM with 64 learned queries improves DocVQA results from 56.0% to 86.6% when integrated with InternVL2 and from 84.4% to 91.2% with Qwen2-VL. In LLaVA-OneVision, DocVLM achieves improved results while using 80% less image tokens. The reduced token usage allows processing multiple pages effectively, showing impressive zero-shot results on DUDE and state-of-the-art performance on MP-DocVQA, highlighting DocVLM's potential for applications requiring high-performance and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nacson_DocVLM_Make_Your_VLM_an_Efficient_Reader_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nacson_DocVLM_Make_Your_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nacson_2025_CVPR,
    author    = {Nacson, Mor Shpigel and Aberdam, Aviad and Ganz, Roy and Ben Avraham, Elad and Golts, Alona and Kittenplon, Yair and Mazor, Shai and Litman, Ron},
    title     = {DocVLM: Make Your VLM an Efficient Reader},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29005-29015}
}


authors: Ronghang Zhu, Mengxuan Hu, Weiming Zhuang, Lingjuan Lyu, Xiang Yu, Sheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25688-25697
affiliations: ["University of Georgia", "University of Virginia", "Sony AI", "Amazon"]
cities: ["Athens", "Charlottesville", "Tokyo", "Seattle"]
countries: ["USA", "USA", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Revisiting_Source-Free_Domain_Adaptation_Insights_into_Representativeness_Generalization_and_Variety_CVPR_2025_paper.html

title:
Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety

abstract:
Domain adaptation addresses the challenge where the distribution of target inference data differs from that of the source training data. Recently, data privacy has become a significant constraint, limiting access to the source domain. To mitigate this issue, Source-Free Domain Adaptation (SFDA) methods bypass source domain data by generating source-like data or pseudo-labeling the unlabeled target domain. However, these approaches often lack theoretical grounding. In this work, we provide a theoretical analysis of the SFDA problem, focusing on the general empirical risk of the unlabeled target domain. Our analysis offers a comprehensive understanding of how representativeness, generalization, and variety contribute to controlling the upper bound of target domain empirical risk in SFDA settings. We further explore how to balance this trade-off from three perspectives: sample selection, semantic domain alignment, and a progressive learning framework. These insights inform the design of novel algorithms. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on three benchmark datasets--Office-Home, DomainNet, and VisDA-C--yielding relative improvements of 3.2%, 9.1%, and 7.5%, respectively, over the representative SFDA method, SHOT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Revisiting_Source-Free_Domain_Adaptation_Insights_into_Representativeness_Generalization_and_Variety_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Revisiting_Source-Free_Domain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Ronghang and Hu, Mengxuan and Zhuang, Weiming and Lyu, Lingjuan and Yu, Xiang and Li, Sheng},
    title     = {Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25688-25697}
}

raw_affiliation:
[{"affiliation": "University of Georgia", "city": "Athens", "country": "USA"}, {"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]


title: Adaptive Unimodal Regulation for Balanced Multimodal Information Acquisition
authors: Chengxiang Huang, Yake Wei, Zequn Yang, Di Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25854-25863
affiliations: ["Beijing University of Posts and Telecommunications", "Renmin University of China"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Adaptive_Unimodal_Regulation_for_Balanced_Multimodal_Information_Acquisition_CVPR_2025_paper.html

abstract:
Sensory training during the early ages is vital for human development. Inspired by this cognitive phenomenon, we observe that the early training stage is also important for the multimodal learning process, where dataset information is rapidly acquired. We refer to this stage as the prime learning window. However, based on our observation, this prime learning window in multimodal learning is often dominated by information-sufficient modalities, which in turn suppresses the information acquisition of information-insufficient modalities. To address this issue, we propose Information Acquisition Regulation (InfoReg), a method designed to balance information acquisition among modalities. Specifically, InfoReg slows down the information acquisition process of information-sufficient modalities during the prime learning window, which could promote information acquisition of information-insufficient modalities. This regulation enables a more balanced learning process and improves the overall performance of the multimodal network. Experiments show that InfoReg outperforms related multimodal imbalanced methods across various datasets, achieving superior model performance. The code is available at https://github.com/GeWu-Lab/InfoReg_CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Adaptive_Unimodal_Regulation_for_Balanced_Multimodal_Information_Acquisition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Adaptive_Unimodal_Regulation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Chengxiang and Wei, Yake and Yang, Zequn and Hu, Di},
    title     = {Adaptive Unimodal Regulation for Balanced Multimodal Information Acquisition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25854-25863}
}

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


title: Heterogeneous Skeleton-Based Action Representation Learning
authors: Hongsong Wang, Xiaoyan Ma, Jidong Kuang, Jie Gui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19154-19164
cities: ["Nanjing", "Nanjing", "Nanjing", "Nanjing", "Nanjing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Heterogeneous_Skeleton-Based_Action_Representation_Learning_CVPR_2025_paper.html

abstract:
Skeleton-based human action recognition has received widespread attention in recent years due to its diverse range of application scenarios. Due to the different sources of human skeletons, skeleton data naturally exhibit heterogeneity. The previous works, however, overlook the heterogeneity of human skeletons and solely construct models tailored for homogeneous skeletons. This work addresses the challenge of heterogeneous skeleton-based action representation learning, specifically focusing on processing skeleton data that varies in joint dimensions and topological structures. The proposed framework comprises two primary components: heterogeneous skeleton processing and unified representation learning. The former first converts two-dimensional skeleton data into three-dimensional skeleton via an auxiliary network, and then constructs a prompted unified skeleton using skeleton-specific prompts. We also design an additional modality named semantic motion encoding to harness the semantic information within skeletons. The latter module learns a unified action representation using a shared backbone network that processes different heterogeneous skeletons. Extensive experiments on the NTU-60, NTU-120, and PKU-MMD II datasets demonstrate the effectiveness of our method in various tasks of action understanding. Our approach can be applied to action recognition in robots with different humanoid structures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Heterogeneous_Skeleton-Based_Action_Representation_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hongsong and Ma, Xiaoyan and Kuang, Jidong and Gui, Jie},
    title     = {Heterogeneous Skeleton-Based Action Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19154-19164}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Southeast University, Nanjing 210096, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Purple Mountain Laboratories, Nanjing 210000, China", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Southeast University, Nanjing 210096, China", "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China", "School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China", "Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education, China", "Purple Mountain Laboratories, Nanjing 210000, China"]


title: FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21936-21947
affiliations: ["Zhejiang University", "Ant Group", "University of Oxford", "Stanford University"]
cities: ["Hangzhou", "Unknown", "Oxford", "Stanford"]
countries: ["China", "China", "United Kingdom", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_FLARE_Feed-forward_Geometry_Appearance_and_Camera_Estimation_from_Uncalibrated_Sparse_CVPR_2025_paper.html

authors:
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yujun Shen, Gordon Wetzstein

abstract:
We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a challenging yet practical setting in real-world applications. Our solution features a cascaded learning paradigm with camera pose serving as the critical bridge, recognizing its essential role in mapping 3D structures onto 2D image planes. Concretely, FLARE starts with camera pose estimation, whose results condition the subsequent learning of geometric structure and appearance, optimized through the objectives of geometry reconstruction and novel-view synthesis. Utilizing large-scale public datasets for training, our method delivers state-of-the-art performance in the tasks of pose estimation, geometry reconstruction, and novel view synthesis, while maintaining the inference efficiency (i.e., less than 0.5 seconds).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_FLARE_Feed-forward_Geometry_Appearance_and_Camera_Estimation_from_Uncalibrated_Sparse_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_FLARE_Feed-forward_Geometry_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shangzhan and Wang, Jianyuan and Xu, Yinghao and Xue, Nan and Rupprecht, Christian and Zhou, Xiaowei and Shen, Yujun and Wetzstein, Gordon},
    title     = {FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21936-21947}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Ant Group", "city": "Unknown", "country": "China"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: Improving Gaussian Splatting with Localized Points Management
authors: Haosen Yang, Chenhao Zhang, Wenqing Wang, Marco Volino, Adrian Hilton, Li Zhang, Xiatian Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21696-21705
affiliations: ["University of Surrey", "Fudan University"]
cities: ["Guildford", "Shanghai"]
countries: ["United Kingdom", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Improving_Gaussian_Splatting_with_Localized_Points_Management_CVPR_2025_paper.html

abstract:
Point management is critical for optimizing 3D Gaussian Splatting models, as point initiation (e.g., via structure from motion) is often distributionally inappropriate. Typically, Adaptive Density Control (ADC) algorithm is adopted, leveraging view-averaged gradient magnitude thresholding for point densification, opacity thresholding for pruning, and regular all-points opacity reset. We reveal that this strategy is limited in tackling intricate/special image regions (e.g., transparent) due to inability of identifying all 3D zones requiring point densification, and lacking an appropriate mechanism to handle ill-conditioned points with negative impacts (e.g., occlusion due to false high opacity).To address these limitations, we propose a Localized Point Management(LPM) strategy, capable of identifying those error-contributing zones in greatest need for both point addition and geometry calibration. Zone identification is achieved by leveraging the underlying multiview geometry constraints, subject to image rendering errors. We apply point densification in the identified zones and then reset the opacity of the points in front of these regions, creating a new opportunity to correct poorly conditioned points. Serving as a versatile plugin, LPM can be seamlessly integrated into existing static 3D and dynamic 4D Gaussian Splatting models with minimal additional cost.Experimental evaluations validate the efficacy of our LPM in boosting a variety of existing 3D/4D models both quantitatively and qualitatively.  Notably, LPM improves both static 3DGS and dynamic SpaceTimeGS to achieve state-of-the-art rendering quality while retaining real-time speeds, excelling on challenging datasets such as Tanks & Temples and the Neural 3D Video dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Improving_Gaussian_Splatting_with_Localized_Points_Management_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Improving_Gaussian_Splatting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Haosen and Zhang, Chenhao and Wang, Wenqing and Volino, Marco and Hilton, Adrian and Zhang, Li and Zhu, Xiatian},
    title     = {Improving Gaussian Splatting with Localized Points Management},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21696-21705}
}

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


title: GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency
authors: Dongyue Lu, Lingdong Kong, Tianxin Huang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1680-1690
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_GEAL_Generalizable_3D_Affordance_Learning_with_Cross-Modal_Consistency_CVPR_2025_paper.html

abstract:
Identifying affordance regions on 3D objects from semantic cues is essential for robotics and human-machine interaction. However, existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data and a reliance on 3D backbones focused on geometric encoding, which often lack resilience to real-world noise and data corruption. We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models. We employ a dual-branch architecture with Gaussian splatting to establish consistent mappings between 3D point clouds and 2D representations, enabling realistic 2D renderings from sparse point clouds. A granularity-adaptive fusion module and a 2D-3D consistency alignment module further strengthen cross-modal alignment and knowledge transfer, allowing the 3D branch to benefit from the rich semantics and generalization capacity of 2D models. To holistically assess the robustness, we introduce two new corruption-based benchmarks: PIAD-C and LASO-C. Extensive experiments on public datasets and our benchmarks show that GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data, demonstrating robust and adaptable affordance predictions. The code and datasets are publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_GEAL_Generalizable_3D_Affordance_Learning_with_Cross-Modal_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_GEAL_Generalizable_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Dongyue and Kong, Lingdong and Huang, Tianxin and Lee, Gim Hee},
    title     = {GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1680-1690}
}


title: Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics
authors: Chen Liu, Liying Yang, Peike Li, Dadong Wang, Lincheng Li, Xin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3131-3141
cities: ["Brisbane", "Hangzhou", "Unknown", "Sydney", "Macau"]
countries: ["Australia", "China", "Unknown", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Dynamic_Derivation_and_Elimination_Audio_Visual_Segmentation_with_Enhanced_Audio_CVPR_2025_paper.html

abstract:
Sound-guided object segmentation has drawn considerable attention for its potential to enhance multimodal perception. Previous methods primarily focus on developing advanced architectures to facilitate effective audio-visual interactions, without fully addressing the inherent challenges posed by audio natures,  i.e., (1) feature confusion due to the overlapping nature of audio signals, and (2) audio-visual matching difficulty from the varied sounds produced by the same object. To address these challenges, we propose Dynamic Derivation and Elimination (DDESeg): a novel audio-visual segmentation framework. Specifically, to mitigate feature confusion, DDESeg reconstructs the semantic content of the mixed audio signal by enriching the distinct semantic information of each individual source, deriving representations that preserve the unique characteristics of each sound. To reduce the matching difficulty, we introduce a discriminative feature learning module, which enhances the semantic distinctiveness of generated audio representations. Considering that not all derived audio representations directly correspond to visual features (e.g., off-screen sounds), we propose a dynamic elimination module to filter out non-matching elements. This module facilitates targeted interaction between sounding regions and relevant audio semantics. By scoring the interacted features, we identify and filter out irrelevant audio information, ensuring accurate audio-visual alignment. Comprehensive experiments demonstrate that our framework achieves superior performance in AVS datasets. Our code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Dynamic_Derivation_and_Elimination_Audio_Visual_Segmentation_with_Enhanced_Audio_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Dynamic_Derivation_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Chen and Yang, Liying and Li, Peike and Wang, Dadong and Li, Lincheng and Yu, Xin},
    title     = {Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3131-3141}
}

raw_affiliation:
[{"affiliation": "The University of Queensland", "city": "Brisbane", "country": "Australia"}, {"affiliation": "NetEase Fuxi AI Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "Matrix Verse AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "CSIRO Data61", "city": "Sydney", "country": "Australia"}, {"affiliation": "Macau University of Science and Technology", "city": "Macau", "country": "China"}]

affiliations:
["The University of Queensland", "NetEase Fuxi AI Lab", "Matrix Verse AI", "CSIRO Data61", "Macau University of Science and Technology"]


authors: Andrea Porfiri Dal Cin, Georgi Dikov, Jihong Ju, Mohsen Ghafoorian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16674-16684
raw_affiliation: [{"affiliation": "XR Labs, Qualcomm Technologies, Inc.", "city": "San Diego", "country": "USA"}]
affiliations: ["XR Labs, Qualcomm Technologies, Inc."]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dal_Cin_AnyMap_Learning_a_General_Camera_Model_for_Structure-from-Motion_with_Unknown_CVPR_2025_paper.html

title:
AnyMap: Learning a General Camera Model for Structure-from-Motion with Unknown Distortion in Dynamic Scenes

abstract:
Current learning-based Structure-from-Motion (SfM) methods struggle with videos of dynamic scenes from wide-angle cameras. We present AnyMap, a differentiable SfM framework that jointly addresses image distortion and motion estimation. By learning a general implicit camera model without predefined parameters, AnyMap effectively handles lens distortion, estimating multi-view consistent 3D geometry, camera poses, and (un)projection functions. To resolve the ambiguity where motion estimation can compensate for undistortion errors and vice versa, we introduce a low-dimensional motion representation consisting of a set of learnable basis trajectories, interpolated to produce regularized motion estimates. Experimental results show that our method produces accurate camera poses, excels in camera calibration and image rectification, and enables high-quality novel view synthesis. Our low-dimensional motion representation effectively disentangles undistortion with motion estimation, outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dal_Cin_AnyMap_Learning_a_General_Camera_Model_for_Structure-from-Motion_with_Unknown_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dal_Cin_AnyMap_Learning_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cin_2025_CVPR,
    author    = {Cin, Andrea Porfiri Dal and Dikov, Georgi and Ju, Jihong and Ghafoorian, Mohsen},
    title     = {AnyMap: Learning a General Camera Model for Structure-from-Motion with Unknown Distortion in Dynamic Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16674-16684}
}


title: ESC: Erasing Space Concept for Knowledge Deletion
authors: Tae-Young Lee, Sundong Park, Minwoo Jeon, Hyoseok Hwang, Gyeong-Moon Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5010-5019
affiliations: ["Korea University", "Kyung Hee University"]
cities: ["Seoul", "Yongin"]
countries: ["Republic of Korea", "Republic of Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_ESC_Erasing_Space_Concept_for_Knowledge_Deletion_CVPR_2025_paper.html

abstract:
As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their personal knowledge in trained models. Despite several research efforts to address this, they often fail to consider the real-world demand from users for complete knowledge erasure. Furthermore, our investigation reveals that existing methods have a risk of leaking personal knowledge through embedding features. To address these issues, we introduce a novel concept of Knowledge Deletion (KD), an advanced task that considers both concerns, and provides an appropriate metric, named Knowledge Retention score (KR), for assessing knowledge retention in feature space. To achieve this, we propose a novel training-free erasing approach named Erasing Space Concept (ESC), which restricts the important subspace for the forgetting knowledge by eliminating the relevant activations in the feature. In addition, we suggest ESC with Training (ESC-T), which uses a learnable mask to better balance the trade-off between forgetting and preserving knowledge in KD. Our extensive experiments on various datasets and models demonstrate that our proposed methods achieve the fastest and state-of-the-art performance. Notably, our methods are applicable to diverse forgetting scenarios, such as facial domain setting, demonstrating the generalizability of our methods. The code is available at https://github.com/KU-VGI/ESC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_ESC_Erasing_Space_Concept_for_Knowledge_Deletion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_ESC_Erasing_Space_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Tae-Young and Park, Sundong and Jeon, Minwoo and Hwang, Hyoseok and Park, Gyeong-Moon},
    title     = {ESC: Erasing Space Concept for Knowledge Deletion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5010-5019}
}

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


title: Language Guided Concept Bottleneck Models for Interpretable Continual Learning
authors: Lu Yu, Haoyu Han, Zhe Tao, Hantao Yao, Changsheng Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14976-14986
cities: ["Tianjin", "Hefei", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.html

abstract:
Continual learning (CL) aims to enable learning systems to acquire new knowledge constantly without forgetting previously learned information. CL faces the challenge of mitigating catastrophic forgetting while maintaining interpretability across tasks.Most existing CL methods focus primarily on preserving learned knowledge to improve model performance. However, as new information is introduced, the interpretability of the learning process becomes crucial for understanding the evolving decision-making process, yet it is rarely explored. In this paper, we introduce a novel framework that integrates language-guided Concept Bottleneck Models (CBMs) to address both challenges. Our approach leverages the Concept Bottleneck Layer, aligning semantic consistency with CLIP models to learn human-understandable concepts that can generalize across tasks. By focusing on interpretable concepts, our method not only enhances the model's ability to retain knowledge over time but also provides transparent decision-making insights. We demonstrate the effectiveness of our approach by achieving superior performance on several datasets, outperforming state-of-the-art methods with an improvement of up to 3.06% in final average accuracy on ImageNet-subset. Additionally, we offer concept visualizations for model predictions, further advancing the understanding of interpretable continual learning. Code is available at https://github.com/FisherCats/CLG-CBM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Language_Guided_Concept_Bottleneck_Models_for_Interpretable_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Language_Guided_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Lu and Han, Haoyu and Tao, Zhe and Yao, Hantao and Xu, Changsheng},
    title     = {Language Guided Concept Bottleneck Models for Interpretable Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14976-14986}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Tianjin University of Technology", "city": "Tianjin", "country": "China"}, {"affiliation": "School of Information Science and Technology, University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "State Key Laboratory of Multimodal Arti\ufb01cial Intelligence Systems, Institute of Automation, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Tianjin University of Technology", "School of Information Science and Technology, University of Science and Technology of China", "State Key Laboratory of Multimodal Arti\ufb01cial Intelligence Systems, Institute of Automation, University of Chinese Academy of Sciences"]


title: One-Way Ticket: Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23563-23574
cities: ["Tianjin", "Barcelona", "Guiyang", "Abu Dhabi", "Linkoping", "Tokyo", "Shenzhen"]
countries: ["China", "Spain", "China", "UAE", "Sweden", "Japan", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_One-Way_Ticket_Time-Independent_Unified_Encoder_for_Distilling_Text-to-Image_Diffusion_Models_CVPR_2025_paper.html

authors:
Senmao Li, Lei Wang, Kai Wang, Tao Liu, Jiehang Xie, Joost van de Weijer, Fahad Shahbaz Khan, Shiqi Yang, Yaxing Wang, Jian Yang

abstract:
Text-to-Image (T2I) diffusion models have made remarkable advancements in generative modeling; however, they face a trade-off between inference speed and image quality, posing challenges for efficient deployment. Existing distilled T2I models can generate high-fidelity images with fewer sampling steps, but often struggle with diversity and quality, especially in one-step models. From our analysis, we observe redundant computations in the UNet encoders. Our findings suggest that, for T2I diffusion models, decoders are more adept at capturing richer and more explicit semantic information, while encoders can be effectively shared across decoders from diverse time steps.Based on these observations, we introduce the first Time-independent Unified Encoder (TiUE) for the student model UNet architecture, which is a loop-free image generation approach for distilling T2I diffusion models. Using a one-pass scheme, TiUE shares encoder features across multiple decoder time steps, enabling parallel sampling and significantly reducing inference time complexity. In addition, we incorporate a KL divergence term to regularize noise prediction, which enhances the perceptual realism and diversity of the generated images. Experimental results demonstrate that TiUE outperforms state-of-the-art methods, including LCM, SD-Turbo, and SwiftBrushv2, producing more diverse and realistic results while maintaining the computational efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_One-Way_Ticket_Time-Independent_Unified_Encoder_for_Distilling_Text-to-Image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_One-Way_Ticket_Time-Independent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Senmao and Wang, Lei and Wang, Kai and Liu, Tao and Xie, Jiehang and van de Weijer, Joost and Khan, Fahad Shahbaz and Yang, Shiqi and Wang, Yaxing and Yang, Jian},
    title     = {One-Way Ticket: Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23563-23574}
}

raw_affiliation:
[{"affiliation": "VCIP, CS, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Computer Vision Center, Universitat Aut`onoma de Barcelona", "city": "Barcelona", "country": "Spain"}, {"affiliation": "School of Big Data and Computer Science, Guizhou Normal University", "city": "Guiyang", "country": "China"}, {"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Linkoping University", "city": "Linkoping", "country": "Sweden"}, {"affiliation": "SB Intuitions, SoftBank", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University", "city": "Shenzhen", "country": "China"}]

affiliations:
["VCIP, CS, Nankai University", "Computer Vision Center, Universitat Aut`onoma de Barcelona", "School of Big Data and Computer Science, Guizhou Normal University", "Mohamed bin Zayed University of AI", "Linkoping University", "SB Intuitions, SoftBank", "Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University"]


title: Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data
authors: Wenxin Su, Song Tang, Xiaofeng Liu, Xiaojing Yi, Mao Ye, Chunxiao Zu, Jiahao Li, Xiatian Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28337-28346
cities: ["Shanghai", "Hamburg", "Unknown", "New Haven", "Chengdu", "Chengdu", "Beijing", "Guildford"]
countries: ["China", "Germany", "Unknown", "USA", "China", "China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_Domain_Adaptive_Diabetic_Retinopathy_Grading_with_Model_Absence_and_Flowing_CVPR_2025_paper.html

abstract:
Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering certain clinical requirements, like source data privacy, conventional transfer methods are predominantly model-centered and often struggle to prevent model-targeted attacks. In this paper, we address a challenging Online Model-aGnostic Domain Adaptation (OMG-DA) setting, driven by the demands of clinical environments. This setting is characterized by the absence of the model and the flow of target data. To tackle the new challenge, we propose a novel approach, Generative Unadversarial ExampleS (GUES), which enables adaptation from a data-centric perspective. Specifically, we first theoretically reformulate conventional perturbation optimization in a generative way-- learning a perturbation generation function with a latent input variable. During model instantiation, we leverage a Variational AutoEncoder to express this function. The encoder with the reparameterization trick predicts the latent input, whilst the decoder is responsible for the generation. Furthermore, the saliency map is selected as pseudo perturbation labels. Because it not only captures potential lesions but also theoretically provides an upper bound on the function input, enabling the identification of the latent variable. Extensive experiments on DR benchmarks with both frozen pre-trained models and trainable models demonstrate the superiority of GUES, showing robustness even with small batch size. The source code and data are available at https://github.com/tntek/GUES.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_Domain_Adaptive_Diabetic_Retinopathy_Grading_with_Model_Absence_and_Flowing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_Domain_Adaptive_Diabetic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Wenxin and Tang, Song and Liu, Xiaofeng and Yi, Xiaojing and Ye, Mao and Zu, Chunxiao and Li, Jiahao and Zhu, Xiatian},
    title     = {Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28337-28346}
}

raw_affiliation:
[{"affiliation": "University of Shanghai for Science and Technology", "city": "Shanghai", "country": "China"}, {"affiliation": "Universit\u00a8at Hamburg", "city": "Hamburg", "country": "Germany"}, {"affiliation": "ComOriginMat Inc.", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Yale University", "city": "New Haven", "country": "USA"}, {"affiliation": "Sichuan Eye Hospital", "city": "Chengdu", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Chinese Academy of Medical Sciences & Peking Union Medical College", "city": "Beijing", "country": "China"}, {"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}]

affiliations:
["University of Shanghai for Science and Technology", "Universit\u00a8at Hamburg", "ComOriginMat Inc.", "Yale University", "Sichuan Eye Hospital", "University of Electronic Science and Technology of China", "Chinese Academy of Medical Sciences & Peking Union Medical College", "University of Surrey"]


authors: Kairong Yu, Chengting Yu, Tianqing Zhang, Xiaochen Zhao, Shu Yang, Hongwei Wang, Qiang Zhang, Qi Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8806-8816
affiliations: ["Zhejiang University", "Dalian University of Technology"]
cities: ["Hangzhou", "Dalian"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Temporal_Separation_with_Entropy_Regularization_for_Knowledge_Distillation_in_Spiking_CVPR_2025_paper.html

title:
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks

abstract:
Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a performance gap persists between SNNs and Artificial Neural Networks (ANNs), primarily due to current training methods and inherent model limitations. While recent research has aimed to enhance SNN learning by employing knowledge distillation (KD) from ANN teacher networks, traditional distillation techniques often overlook the distinctive spatiotemporal properties of SNNs, thus failing to fully leverage their advantages. To overcome these challenge, we propose a novel logit distillation method characterized by temporal separation and entropy regularization. This approach improves existing SNN distillation techniques by performing distillation learning on logits across different time steps, rather than merely on aggregated output features. Furthermore, the integration of entropy regularization stabilizes model optimization and further boosts the performance. Extensive experimental results indicate that our method surpasses prior SNN distillation strategies, whether based on logit distillation, feature distillation, or a combination of both. Our project is available at https://github.com/yukairong/TSER.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Temporal_Separation_with_Entropy_Regularization_for_Knowledge_Distillation_in_Spiking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Kairong and Yu, Chengting and Zhang, Tianqing and Zhao, Xiaochen and Yang, Shu and Wang, Hongwei and Zhang, Qiang and Xu, Qi},
    title     = {Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8806-8816}
}

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


authors: Jian Liang, Wenke Huang, Guancheng Wan, Qu Yang, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26170-26180
cities: ["Wuhan"]
countries: ["China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_LoRASculpt_Sculpting_LoRA_for_Harmonizing_General_and_Specialized_Knowledge_in_CVPR_2025_paper.html

title:
LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models

abstract:
While Multimodal Large Language Models (MLLMs) excel at generalizing across modalities and tasks, effectively adapting them to specific downstream tasks while simultaneously retaining both general and specialized knowledge remains challenging. Although Low-Rank Adaptation (LoRA) is widely used to efficiently acquire specialized knowledge in MLLMs, it introduces substantial harmful redundancy during visual instruction tuning, which exacerbates the forgetting of general knowledge and degrades downstream task performance.To address this issue, we propose LoRASculpt to eliminate harmful redundant parameters, thereby harmonizing general and specialized knowledge.Specifically, under theoretical guarantees, we introduce sparse updates into LoRA to discard redundant parameters effectively. Furthermore, we propose a Conflict Mitigation Regularizer to refine the update trajectory of LoRA, mitigating knowledge conflicts with the pretrained weights.Extensive experimental results demonstrate that even at very high degree of sparsity (\le 5%), our method simultaneously enhances generalization and downstream task performance. This confirms that our approach effectively mitigates the catastrophic forgetting issue and further promotes knowledge harmonization in MLLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_LoRASculpt_Sculpting_LoRA_for_Harmonizing_General_and_Specialized_Knowledge_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_LoRASculpt_Sculpting_LoRA_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Jian and Huang, Wenke and Wan, Guancheng and Yang, Qu and Ye, Mang},
    title     = {LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26170-26180}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University.", "city": "Wuhan", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University."]


title: SEAL: Semantic Attention Learning for Long Video Representation
authors: Lan Wang, Yujia Chen, Du Tran, Vishnu Naresh Boddeti, Wen-Sheng Chu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26192-26201
affiliations: ["Michigan State University", "Google"]
cities: ["East Lansing", "Mountain View"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SEAL_Semantic_Attention_Learning_for_Long_Video_Representation_CVPR_2025_paper.html

abstract:
Long video understanding presents challenges due to the inherent high computational complexity and redundant temporal information. An effective representation for long videos must efficiently process such redundancy while preserving essential contents for downstream tasks.  This paper introduces **SE**mantic **A**ttention **L**earning (SEAL), a novel unified representation for long videos. To reduce computational complexity, long videos are decomposed into three distinct types of semantic entities: scenes, objects, and actions, allowing models to operate on a compact set of entities rather than a large number of frames or pixels.  To further address redundancy, we propose an attention learning module that balances token relevance with diversity, formulated as a subset selection optimization problem.  Our representation is versatile, enabling applications across various long video understanding tasks. Our representation is versatile and applicable across various long video understanding tasks. Extensive experiments demonstrate that SEAL significantly outperforms state-of-the-art methods in video question answering and temporal grounding tasks across diverse benchmarks, including LVBench, MovieChat-1K, and Ego4D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SEAL_Semantic_Attention_Learning_for_Long_Video_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SEAL_Semantic_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lan and Chen, Yujia and Tran, Du and Boddeti, Vishnu Naresh and Chu, Wen-Sheng},
    title     = {SEAL: Semantic Attention Learning for Long Video Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26192-26201}
}

raw_affiliation:
[{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Re-HOLD: Video Hand Object Interaction Reenactment via adaptive Layout-instructed Diffusion Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17550-17560
cities: ["Wuhan", "Hefei", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Re-HOLD_Video_Hand_Object_Interaction_Reenactment_via_adaptive_Layout-instructed_Diffusion_CVPR_2025_paper.html

authors:
Yingying Fan, Quanwei Yang, Kaisiyuan Wang, Hang Zhou, Yingying Li, Haocheng Feng, Errui Ding, Yu Wu, Jingdong Wang

abstract:
Current digital human studies focusing on lip-syncing and body movement are no longer sufficient to meet the growing industrial demand, while human video generation techniques that support interacting with real-world environments (e.g., objects) have not been well investigated. Despite human hand synthesis already being an intricate problem, generating objects in contact with hands and their interactions presents an even more challenging task, especially when the objects exhibit obvious variations in size and shape. To tackle these issues, we present a novel video Reenactment framework focusing on Human-Object Interaction (HOI) via an adaptive Layout-instructed Diffusion model (Re-HOLD). Our key insight is to employ specialized layout representation for hands and objects, respectively. Such representations enable effective disentanglement of hand modeling and object adaptation to diverse motion sequences. To further improve the generation quality of HOI, we design an interactive textural enhancement module for both hands and objects by introducing two independent memory banks. We also propose a layout adjustment strategy for the cross-object reenactment scenario to adaptively adjust unreasonable layouts caused by diverse object sizes during inference. Comprehensive qualitative and quantitative evaluations demonstrate that our proposed framework significantly outperforms existing methods. Project page: https://fyycs.github.io/Re-HOLD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Re-HOLD_Video_Hand_Object_Interaction_Reenactment_via_adaptive_Layout-instructed_Diffusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Yingying and Yang, Quanwei and Wang, Kaisiyuan and Zhou, Hang and Li, Yingying and Feng, Haocheng and Ding, Errui and Wu, Yu and Wang, Jingdong},
    title     = {Re-HOLD: Video Hand Object Interaction Reenactment via adaptive Layout-instructed Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17550-17560}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "University of Science and Technology of China", "Baidu Inc."]


authors: Song Xia, Yi Yu, Wenhan Yang, Meiwen Ding, Zhuo Chen, Ling-Yu Duan, Alex C. Kot, Xudong Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8753-8763
affiliations: ["ROSE Lab, Nanyang Technological University", "Pengcheng Laboratory", "Peking University"]
cities: ["Singapore", "Shenzhen", "Beijing"]
countries: ["Singapore", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Theoretical_Insights_in_Model_Inversion_Robustness_and_Conditional_Entropy_Maximization_CVPR_2025_paper.html

title:
Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems

abstract:
By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers. However, recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked and raw data can be reconstructed via model inversion attacks (MIAs). Obfuscation-based methods, such as noise corruption, adversarial representation learning, and information filters, enhance the inversion robustness by obfuscating the task-irrelevant redundancy empirically. However, methods for quantifying such redundancy remain elusive, and the explicit mathematical relation between this redundancy and worst-case robustness against inversion has not yet been established. To address that, this work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA. Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy maximization (CEM) algorithm to enhance the inversion robustness. Experimental results on four datasets demonstrate the effectiveness and adaptability of our proposed CEM; without compromising the feature utility and computing efficiency, integrating the proposed CEM into obfuscation-based defense mechanisms consistently boosts their inversion robustness, achieving average gains ranging from 12.9% to 48.2%. Code is available at \href https://github.com/xiasong0501/CEM  https://github.com/xiasong0501/CEM .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Theoretical_Insights_in_Model_Inversion_Robustness_and_Conditional_Entropy_Maximization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_Theoretical_Insights_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Song and Yu, Yi and Yang, Wenhan and Ding, Meiwen and Chen, Zhuo and Duan, Ling-Yu and Kot, Alex C. and Jiang, Xudong},
    title     = {Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8753-8763}
}

raw_affiliation:
[{"affiliation": "ROSE Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: Odd-One-Out: Anomaly Detection by Comparing with Neighbors
authors: Ankan Bhunia, Changjian Li, Hakan Bilen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20395-20404
raw_affiliation: [{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "United Kingdom"}]
affiliations: ["University of Edinburgh"]
cities: ["Edinburgh"]
countries: ["United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bhunia_Odd-One-Out_Anomaly_Detection_by_Comparing_with_Neighbors_CVPR_2025_paper.html

abstract:
This paper introduces a novel anomaly detection (AD) problem aimed at identifying `odd-looking' objects within a scene by comparing them to other objects present. Unlike traditional AD benchmarks with fixed anomaly criteria, our task detects anomalies specific to each scene by inferring a reference group of regular objects. To address occlusions, we use multiple views of each scene as input, construct 3D object-centric models for each instance from 2D views, enhancing these models with geometrically consistent part-aware representations. Anomalous objects are then detected through cross-instance comparison. We also introduce two new benchmarks, ToysAD-8K and PartsAD-15K as testbeds for future research in this task. We provide a comprehensive analysis of our method quantitatively and qualitatively on these benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bhunia_Odd-One-Out_Anomaly_Detection_by_Comparing_with_Neighbors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bhunia_Odd-One-Out_Anomaly_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bhunia_2025_CVPR,
    author    = {Bhunia, Ankan and Li, Changjian and Bilen, Hakan},
    title     = {Odd-One-Out: Anomaly Detection by Comparing with Neighbors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20395-20404}
}


title: SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
authors: Qingyuan Wang, Rui Song, Jiaojiao Li, Kerui Cheng, David Ferstl, Yinlin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22045-22054
affiliations: ["State Key Laboratory of ISN, Xidian University", "Taiyuan University of Technology", "MagicLeap"]
cities: ["Xi'an", "Taiyuan", "Plantation"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SCFlow2_Plug-and-Play_Object_Pose_Refiner_with_Shape-Constraint_Scene_Flow_CVPR_2025_paper.html

abstract:
We introduce SCFlow2, a plug-and-play refinement framework for 6D object pose estimation. Most recent 6D object pose methods rely on refinement to get accurate results. However, most existing refinements either suffer from noises in establishing correspondences, or rely on retraining for novel objects. SCFlow2 is based on the SCFlow model designed for iterative RGB refinement with shape constraint, but formulates the additional depth as a regularization in the iteration via 3D scene flow for RGBD frames. The key design of SCFlow2 is an introduction of geometry constraints into the training of recurrent match network, by combining the rigid-motion embeddings in 3D scene flow and 3D shape prior of the target. We train the refinement network on a combination of dataset Objaverse, GSO and ShapeNet, and demonstrate on BOP datasets with novel objects that, after using our method, the result of most state-of-the-art methods improves significantly, without any retraining or fine-tuning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SCFlow2_Plug-and-Play_Object_Pose_Refiner_with_Shape-Constraint_Scene_Flow_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qingyuan and Song, Rui and Li, Jiaojiao and Cheng, Kerui and Ferstl, David and Hu, Yinlin},
    title     = {SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22045-22054}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of ISN, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Taiyuan University of Technology", "city": "Taiyuan", "country": "China"}, {"affiliation": "MagicLeap", "city": "Plantation", "country": "USA"}]


title: D^3CTTA: Domain-Dependent Decorrelation for Continual Test-Time Adaption of 3D LiDAR Segmentation
authors: Jichun Zhao, Haiyong Jiang, Haoxuan Song, Jun Xiao, Dong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11864-11874
affiliations: ["University of Chinese Academy of Sciences", "University of New South Wales"]
cities: ["Beijing", "Sydney"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_D3CTTA_Domain-Dependent_Decorrelation_for_Continual_Test-Time_Adaption_of_3D_LiDAR_CVPR_2025_paper.html

abstract:
Adapting pre-trained LiDAR segmentation models to dynamic domain shifts during testing is of paramount importance for the safety of autonomous driving.  Most existing methods neglect the influence of domain changes and point density in continual test-time adaption (CTTA), relying on backpropagation and large batch sizes for stability. We approach this problem with three insights: 1) Point clouds at different distances usually have different densities resulting in distribution disparities; 2) The feature distribution of different domains varies, and domain-aware parameters can alleviate domain gaps; 3) Features are highly correlated and make segmentation of different labels confusing.  To this end, this work presents D^3CTTA, an online backpropagation-free framework for 3D continual test-time adaption for LiDAR segmentation. D^3CTTA consists of a distance-aware prototype learning module to integrate LiDAR-based geometry prior and a domain-dependent decorrelation module to reduce feature correlations among different domains and different categories. Extensive experiments on three benchmarks showcase that our method achieves a state-of-the-art performance compared to both backpropagation-based methods and backpropagation-free methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_D3CTTA_Domain-Dependent_Decorrelation_for_Continual_Test-Time_Adaption_of_3D_LiDAR_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_D3CTTA_Domain-Dependent_Decorrelation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Jichun and Jiang, Haiyong and Song, Haoxuan and Xiao, Jun and Gong, Dong},
    title     = {D{\textasciicircum}3CTTA: Domain-Dependent Decorrelation for Continual Test-Time Adaption of 3D LiDAR Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11864-11874}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}]


title: Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution
authors: Qihao Liu, Xi Yin, Alan Yuille, Andrew Brown, Mannat Singh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2755-2765
affiliations: ["GenAI, Meta", "Johns Hopkins University"]
cities: ["Menlo Park", "Baltimore"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Flowing_from_Words_to_Pixels_A_Noise-Free_Framework_for_Cross-Modality_CVPR_2025_paper.html

abstract:
Diffusion models, and their generalization, flow matching, have had a remarkable impact on the field of media generation. Here, the conventional approach is to learn the complex mapping from a simple source distribution of Gaussian noise to the target media distribution. For cross-modal tasks such as text-to-image generation, this same mapping from noise to image is learnt whilst including a conditioning mechanism in the model. One key and thus far relatively unexplored feature of flow matching is that, unlike Diffusion models, they are not constrained for the source distribution to be noise. Hence, in this paper, we propose a paradigm shift, and ask the question of whether we can instead train flow matching models to learn a direct mapping from the distribution of one modality to the distribution of another, thus obviating the need for both the noise distribution and conditioning mechanism. We present a general and simple framework, CrossFlow, for cross-modal flow matching. We show the importance of applying Variational Encoders to the input data, and introduce a method to enable Classifier-free guidance. Surprisingly, for text-to-image, CrossFlow with a vanilla transformer without cross attention slightly outperforms standard flow matching, and we show that it scales better with training steps and model size, while also allowing for interesting latent arithmetic which results in semantically meaningful edits in the output space. To demonstrate the generalizability of our approach, we also show that CrossFlow is on par with or outperforms the state-of-the-art for various cross-modal / intra-modal mapping tasks, viz. image captioning, depth estimation, and image super-resolution. We hope this paper contributes to accelerating progress in cross-modal media generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Flowing_from_Words_to_Pixels_A_Noise-Free_Framework_for_Cross-Modality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Flowing_from_Words_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Qihao and Yin, Xi and Yuille, Alan and Brown, Andrew and Singh, Mannat},
    title     = {Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2755-2765}
}

raw_affiliation:
[{"affiliation": "GenAI, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


title: FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations
authors: Hmrishav Bandyopadhyay, Yi-Zhe Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28394-28404
affiliations: ["SketchX, CVSSP, University of Surrey"]
cities: ["Guildford"]
countries: ["United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bandyopadhyay_FlipSketch_Flipping_Static_Drawings_to_Text-Guided_Sketch_Animations_CVPR_2025_paper.html

abstract:
Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames, existing automation attempts still demand significant artistic effort through precise motion paths or keyframe specification. We present FlipSketch, a system that brings back the magic of flip-book animation -- just draw your idea and describe how you want it to move! Our approach harnesses motion priors from text-to-video diffusion models, adapting them to generate sketch animations through three key innovations: (i) fine-tuning for sketch-style frame generation, (ii) a reference frame mechanism that preserves visual integrity of input sketch through noise refinement, and (iii) a dual-attention composition that enables fluid motion without losing visual consistency. Unlike constrained vector animations, our raster frames support dynamic sketch transformations, capturing the expressive freedom of traditional animation. The result is an intuitive system that makes sketch animation as simple as doodling and describing, while maintaining the artistic essence of hand-drawn animation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bandyopadhyay_FlipSketch_Flipping_Static_Drawings_to_Text-Guided_Sketch_Animations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bandyopadhyay_FlipSketch_Flipping_Static_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bandyopadhyay_2025_CVPR,
    author    = {Bandyopadhyay, Hmrishav and Song, Yi-Zhe},
    title     = {FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28394-28404}
}

raw_affiliation:
[{"affiliation": "SketchX, CVSSP, University of Surrey", "city": "Guildford", "country": "United Kingdom"}]


title: Interpretable Generative Models through Post-hoc Concept Bottlenecks
authors: Akshay Kulkarni, Ge Yan, Chung-En Sun, Tuomas Oikarinen, Tsui-Wei Weng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8162-8171
raw_affiliation: [{"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}]
affiliations: ["University of California San Diego"]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kulkarni_Interpretable_Generative_Models_through_Post-hoc_Concept_Bottlenecks_CVPR_2025_paper.html

abstract:
Concept bottleneck models (CBM) aim to produce inherently interpretable models that rely on human-understandable concepts for their predictions. However, existing approaches to design interpretable generative models based on CBMs are not yet efficient and scalable, as they require expensive generative model training from scratch as well as real images with labor-intensive concept supervision. To address these challenges, we present two novel and low-cost methods to build interpretable generative models through post-hoc techniques and we name our approaches: concept-bottleneck autoencoder (CB-AE) and concept controller (CC). Our proposed approaches enable efficient and scalable training without the need of real data and require only minimal to no concept supervision. Additionally, our methods generalize across modern generative model families including generative adversarial networks and diffusion models. We demonstrate the superior interpretability and steerability of our methods on numerous standard datasets like CelebA, CelebA-HQ, and CUB with large improvements (average  25%) over the prior work, while being 4-15x faster to train. Finally, a large-scale user study is performed to validate the interpretability and steerability of our methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kulkarni_Interpretable_Generative_Models_through_Post-hoc_Concept_Bottlenecks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kulkarni_Interpretable_Generative_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kulkarni_2025_CVPR,
    author    = {Kulkarni, Akshay and Yan, Ge and Sun, Chung-En and Oikarinen, Tuomas and Weng, Tsui-Wei},
    title     = {Interpretable Generative Models through Post-hoc Concept Bottlenecks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8162-8171}
}


title: SketchAgent: Language-Driven Sequential Sketch Generation
authors: Yael Vinker, Tamar Rott Shaham, Kristine Zheng, Alex Zhao, Judith E Fan, Antonio Torralba
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23355-23368
affiliations: ["MIT", "Stanford University"]
cities: ["Cambridge", "Stanford"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vinker_SketchAgent_Language-Driven_Sequential_Sketch_Generation_CVPR_2025_paper.html

abstract:
Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driven substantial advances in content creation and human-computer interaction, capturing the dynamic and abstract nature of human sketching remains challenging. In this work, we introduce SketchAgent, a language-driven, sequential sketch generation method that enables users to create, modify, and refine sketches through dynamic, conversational interactions. Our approach requires no training or fine-tuning. Instead, we leverage the sequential nature and rich prior knowledge of off-the-shelf multimodal large language models (LLMs). We present an intuitive sketching language, introduced to the model through in-context examples, enabling it to "draw" using string-based actions. These are processed into vector graphics and then rendered to create a sketch on a pixel canvas, which can be accessed again for further tasks. By drawing stroke by stroke, our agent captures the evolving, dynamic qualities intrinsic to sketching. We demonstrate that SketchAgent can generate sketches from diverse prompts, engage in dialogue-driven drawing, and collaborate meaningfully with human users.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vinker_SketchAgent_Language-Driven_Sequential_Sketch_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vinker_SketchAgent_Language-Driven_Sequential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vinker_2025_CVPR,
    author    = {Vinker, Yael and Shaham, Tamar Rott and Zheng, Kristine and Zhao, Alex and E Fan, Judith and Torralba, Antonio},
    title     = {SketchAgent: Language-Driven Sequential Sketch Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23355-23368}
}

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


title: DRAWER: Digital Reconstruction and Articulation With Environment Realism
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21771-21782
cities: ["Urbana", "Seattle", "Seattle", "Ithaca"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_DRAWER_Digital_Reconstruction_and_Articulation_With_Environment_Realism_CVPR_2025_paper.html

authors:
Hongchi Xia, Entong Su, Marius Memmel, Arhan Jain, Raymond Yu, Numfor Mbiziwo-Tiapo, Ali Farhadi, Abhishek Gupta, Shenlong Wang, Wei-Chiu Ma

abstract:
Creating virtual digital replicas from real-world data unlocks significant potential across domains like gaming and robotics. In this paper, we present DRAWER, a novel framework that converts a video of a static indoor scene into a photorealistic and interactive digital environment. Our approach centers on two main contributions: (i) a reconstruction module based on a dual scene representation that reconstructs the scene with fine-grained geometric details, and (ii) an articulation module that identifies articulation types and hinge positions, reconstructs simulatable shapes and appearances and integrates them into the scene. The resulting virtual environment is photorealistic, interactive, and runs in real time, with compatibility for game engines and robotic simulation platforms. We demonstrate the potential of DRAWER by using it to automatically create an interactive game in Unreal Engine and to enable real-to-sim-to-real transfer for robotics applications. Project page: https://xiahongchi.github.io/DRAWER/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_DRAWER_Digital_Reconstruction_and_Articulation_With_Environment_Realism_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_DRAWER_Digital_Reconstruction_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Hongchi and Su, Entong and Memmel, Marius and Jain, Arhan and Yu, Raymond and Mbiziwo-Tiapo, Numfor and Farhadi, Ali and Gupta, Abhishek and Wang, Shenlong and Ma, Wei-Chiu},
    title     = {DRAWER: Digital Reconstruction and Articulation With Environment Realism},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21771-21782}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]

affiliations:
["University of Illinois Urbana-Champaign", "University of Washington", "Allen Institute for AI", "Cornell University"]


title: GoLF-NRT: Integrating Global Context and Local Geometry for Few-Shot View Synthesis
authors: You Wang, Li Fang, Hao Zhu, Fei Hu, Long Ye, Zhan Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21349-21359
cities: ["Beijing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_GoLF-NRT_Integrating_Global_Context_and_Local_Geometry_for_Few-Shot_View_CVPR_2025_paper.html

abstract:
Neural Radiance Fields (NeRF) have transformed novel view synthesis by modeling scene-specific volumetric representations directly from images. While generalizable NeRF models can generate novel views across unknown scenes by learning latent ray representations, their performance heavily depends on a large number of multi-view observations. However, with limited input views, these methods experience significant degradation in rendering quality. To address this limitation, we propose GoLF-NRT: a Global and Local feature Fusion-based Neural Rendering Transformer. GoLF-NRT enhances generalizable neural rendering from few input views by leveraging a 3D transformer with efficient sparse attention to capture global scene context. In parallel, it integrates local geometric features extracted along the epipolar line, enabling high-quality scene reconstruction from as few as 1 to 3 input views. Furthermore, we introduce an adaptive sampling strategy based on attention weights and kernel regression, improving the accuracy of transformer-based neural rendering. Extensive experiments on public datasets show that GoLF-NRT achieves state-of-the-art performance across varying numbers of input views, highlighting the effectiveness and superiority of our approach. Code is available at https://github.com/KLMAV-CUC/GoLF-NRT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_GoLF-NRT_Integrating_Global_Context_and_Local_Geometry_for_Few-Shot_View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_GoLF-NRT_Integrating_Global_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, You and Fang, Li and Zhu, Hao and Hu, Fei and Ye, Long and Ma, Zhan},
    title     = {GoLF-NRT: Integrating Global Context and Local Geometry for Few-Shot View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21349-21359}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education, Beijing 100024, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China", "city": "Nanjing", "country": "China"}]

affiliations:
["Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education, Beijing 100024, China", "School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"]


authors: Yante Li, Hanwen Qi, Haoyu Chen, Xinlian Liang, Guoying Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27346-27356
affiliations: ["University of Oulu", "Wuhan University"]
cities: ["Oulu", "Wuhan"]
countries: ["Finland", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Deep_Change_Monitoring_A_Hyperbolic_Representative_Learning_Framework_and_a_CVPR_2025_paper.html

title:
Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change Detection

abstract:
In environmental protection, tree monitoring plays an essential role in maintaining and improving ecosystem health. However, precise monitoring is challenging because existing datasets fail to capture continuous fine-grained changes in trees due to low-resolution images and high acquisition costs. In this paper, we introduce UAVTC, a large-scale, long-term, high-resolution dataset collected using UAVs equipped with cameras, specifically designed to detect individual Tree Changes (TCs). UAVTC includes rich annotations and statistics based on biological knowledge, offering a fine-grained view for tree monitoring. To address environmental influences and effectively model the hierarchical diversity of physiological TCs, we propose a novel Hyperbolic Siamese Network (HSN) for TC detection, enabling compact and hierarchical representations of dynamic tree changes. Extensive experiments show that HSN can effectively capture complex hierarchical changes and provide a robust solution for fine-grained TC detection. In addition, HSN generalizes well to cross-domain face anti-spoofing task, highlighting its broader significance in AI.  We believe our work, combining ecological insights and interdisciplinary expertise, will benefit the community by offering a new benchmark and innovative  AI technologies.   Source code is available on https://github.com/liyantett/Tree-Changes-Detection-with-Siamese-Hyperbolic-network.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Deep_Change_Monitoring_A_Hyperbolic_Representative_Learning_Framework_and_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Deep_Change_Monitoring_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yante and Qi, Hanwen and Chen, Haoyu and Liang, Xinlian and Zhao, Guoying},
    title     = {Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27346-27356}
}

raw_affiliation:
[{"affiliation": "University of Oulu", "city": "Oulu", "country": "Finland"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]


title: A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12934-12944
cities: ["Singapore", "Shenzhen", "Unknown", "Chicago", "Singapore"]
countries: ["Singapore", "China", "Unknown", "USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_A_Closer_Look_at_Time_Steps_is_Worthy_of_Triple_CVPR_2025_paper.html

authors:
Kai Wang, Mingjia Shi, Yukun Zhou, Zekai Li, Zhihang Yuan, Yuzhang Shang, Xiaojiang Peng, Hanwang Zhang, Yang You

abstract:
Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empirically divided into acceleration, deceleration, and convergence areas based on the process increment. ii) These time steps are imbalanced, with many concentrated in the convergence area. iii) The concentrated steps provide limited benefits for diffusion training. To address this, we design an asymmetric sampling strategy that reduces the frequency of steps from the convergence area while increasing the sampling probability for steps from other areas. Additionally, we propose a weighting strategy to emphasize the importance of time steps with rapid-change process increments. As a plug-and-play and architecture-agnostic approach, SpeeD consistently achieves 3-times acceleration across various diffusion architectures, datasets, and tasks. Notably, due to its simple design, our approach significantly reduces the cost of diffusion model training with minimal overhead. Our research enables more researchers to train diffusion models at a lower cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_A_Closer_Look_at_Time_Steps_is_Worthy_of_Triple_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_A_Closer_Look_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Kai and Shi, Mingjia and Zhou, Yukun and Li, Zekai and Yuan, Zhihang and Shang, Yuzhang and Peng, Xiaojiang and Zhang, Hanwang and You, Yang},
    title     = {A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12934-12944}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shenzhen Technology University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Infinigence-AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Illinois Institute of Technology", "city": "Chicago", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["National University of Singapore", "Shenzhen Technology University", "Infinigence-AI", "Illinois Institute of Technology", "Nanyang Technological University"]


title: Empowering LLMs to Understand and Generate Complex Vector Graphics
authors: Ximing Xing, Juncheng Hu, Guotao Liang, Jing Zhang, Dong Xu, Qian Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19487-19497
affiliations: ["Beihang University", "The University of Hong Kong"]
cities: ["Beijing", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_Empowering_LLMs_to_Understand_and_Generate_Complex_Vector_Graphics_CVPR_2025_paper.html

abstract:
The unprecedented advancements in Large Language Models (LLMs) have profoundly impacted natural language processing but have yet to fully embrace the realm of scalable vector graphics (SVG) generation. While LLMs encode partial knowledge of SVG data from web pages during training, recent findings suggest that semantically ambiguous and tokenized representations within LLMs may result in hallucinations in vector primitive predictions. Additionally, LLM training typically lacks modeling and understanding of the rendering sequence of vector paths, which can lead to occlusion between output vector primitives. In this paper, we present LLM4SVG, an initial yet substantial step toward bridging this gap by enabling LLMs to better understand and generate vector graphics. LLM4SVG facilitates a deeper understanding of SVG components through learnable semantic tokens, which precisely encode these tokens and their corresponding properties to generate semantically aligned SVG outputs. Using a series of learnable semantic tokens, a structured dataset for instruction following is developed to support comprehension and generation across two primary tasks. Our method introduces a modular architecture to existing large language models, integrating semantic tags, vector instruction encoders, fine-tuned commands, and powerful LLMs to tightly combine geometric, appearance, and language information. To overcome the scarcity of SVG-text instruction data, we developed an automated data generation pipeline that collected our SVGX-SFT Dataset, consisting of high-quality human-designed SVGs and 580k SVG instruction following data specifically crafted for LLM training, which facilitated the adoption of the supervised fine-tuning strategy popular in LLM development. By exploring various training strategies, we developed LLM4SVG, which significantly moves beyond optimized rendering-based approaches and language-model-based baselines to achieve remarkable results in human evaluation tasks. Code, model, and data will be released at: https://ximinng.github.io/LLM4SVGProject/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_Empowering_LLMs_to_Understand_and_Generate_Complex_Vector_Graphics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_Empowering_LLMs_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Ximing and Hu, Juncheng and Liang, Guotao and Zhang, Jing and Xu, Dong and Yu, Qian},
    title     = {Empowering LLMs to Understand and Generate Complex Vector Graphics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19487-19497}
}

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


title: PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding
authors: Hongjia Zhai, Hai Li, Zhenzhe Li, Xiaokun Pan, Yijia He, Guofeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14114-14124
affiliations: ["State Key Lab of CAD & CG, Zhejiang University", "RayNeo"]
cities: ["Hangzhou", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhai_PanoGS_Gaussian-based_Panoptic_Segmentation_for_3D_Open_Vocabulary_Scene_Understanding_CVPR_2025_paper.html

abstract:
Recently, 3D Gaussian Splatting (3DGS) has shown encouraging performance for open vocabulary scene understanding tasks. However, previous methods can not distinguish 3D instance-level information, which usually predicts a heatmap between the scene feature and text query. In this paper, we propose PanoGS, a novel and efficient 3D panoptic open vocabulary scene understanding approach. Technical-wise, to learn accurate 3D language features that can scale to large indoor scenarios, we adopt the pyramid tri-planes to model the latent continuous parametric feature space and use a 3D feature decoder to regress the multi-view fused 2D feature cloud. Besides, we propose language-guided graph cuts that synergistically leverage reconstructed geometry and learned language cues to group 3D Gaussian primitives into a set of super-primitives. To obtain 3D consistent instance, we perform graph clustering based segmentation with SAM-guided edge affinity computation between different super-primitives. Extensive experiments on widely used datasets show better or more competitive performance on 3D panoptic open vocabulary scene understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhai_PanoGS_Gaussian-based_Panoptic_Segmentation_for_3D_Open_Vocabulary_Scene_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhai_PanoGS_Gaussian-based_Panoptic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhai_2025_CVPR,
    author    = {Zhai, Hongjia and Li, Hai and Li, Zhenzhe and Pan, Xiaokun and He, Yijia and Zhang, Guofeng},
    title     = {PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14114-14124}
}

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


title: Watermarking One for All: A Robust Watermarking Scheme Against Partial Image Theft
authors: Gaozhi Liu, Silu Cao, Zhenxing Qian, Xinpeng Zhang, Sheng Li, Wanli Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8225-8234
affiliations: ["Fudan University", "China Agricultural University"]
cities: ["Shanghai", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Watermarking_One_for_All_A_Robust_Watermarking_Scheme_Against_Partial_CVPR_2025_paper.html

abstract:
The proliferation of digital images on the Internet has provided unprecedented convenience, but also poses significant risks of malicious theft and misuse. Digital watermarking has long been researched as an effective tool for copyright protection. However, it often falls short when addressing partial image theft, a common yet little-researched issue in practical applications. Most existing schemes typically require the entire image as input to extract watermarks. However, in practice, malicious users often steal only a portion of the image to create new content. The stolen portion can have arbitrary shape or content, being fused with a new background and may have undergone geometric transformations, making it challenging for current methods to extract correctly. To address the issues above, we propose WOFA (Watermarking One for All), a robust watermarking scheme against partial image theft. First of all, we define the entire process of partial image theft and construct a dataset accordingly. To gain robustness against partial image theft, we then design a comprehensive distortion layer that incorporates the process of partial image theft and several common distortions in channel. For easier network convergence, we employ a multi-level network structure on the basis of the commonly used embedder-distortion layer-extractor architecture and adopt a progressive training strategy. Abundant experiments demonstrate that our superior performance in the scenario of partial image theft, offering a more reliable solution for protecting digital images against unauthorized use in practical use.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Watermarking_One_for_All_A_Robust_Watermarking_Scheme_Against_Partial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Watermarking_One_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Gaozhi and Cao, Silu and Qian, Zhenxing and Zhang, Xinpeng and Li, Sheng and Peng, Wanli},
    title     = {Watermarking One for All: A Robust Watermarking Scheme Against Partial Image Theft},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8225-8234}
}

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


authors: Ji Woo Hong, Tri Ton, Trung X. Pham, Gwanhyeong Koo, Sunjae Yoon, Chang D. Yoo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28284-28294
affiliations: ["Korea Advanced Institute of Science and Technology (KAIST)"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_ITA-MDT_Image-Timestep-Adaptive_Masked_Diffusion_Transformer_Framework_for_Image-Based_Virtual_Try-On_CVPR_2025_paper.html

title:
ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On

abstract:
This paper introduces ITA-MDT, the Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On (IVTON), designed to overcome the limitations of previous approaches by leveraging the Masked Diffusion Transformer (MDT) for improved handling of both global garment context and fine-grained details. The IVTON task involves seamlessly superimposing a garment from one image onto a person in another, creating a realistic depiction of the person wearing the specified garment. Unlike conventional diffusion-based virtual try-on models that depend on large pre-trained U-Net architectures, ITA-MDT leverages a lightweight, scalable transformer-based denoising diffusion model with a mask latent modeling scheme, achieving competitive results while reducing computational overhead. A key component of ITA-MDT is the Image-Timestep Adaptive Feature Aggregator (ITAFA), a dynamic feature aggregator that combines all of the features from the image encoder into a unified feature of the same size, guided by diffusion timestep and garment image complexity. This enables adaptive weighting of features, allowing the model to emphasize either global information or fine-grained details based on the requirements of the denoising stage. Additionally, the Salient Region Extractor (SRE) module is presented to identify complex region of the garment to provide high-resolution local information to the denoising model as an additional condition alongside the global information of the full garment image. This targeted conditioning strategy enhances detail preservation of fine details in highly salient garment regions, optimizing computational resources by avoiding unnecessarily processing entire garment image. Comparative evaluations confirms that ITA-MDT improves efficiency while maintaining strong performance, reaching state-of-the-art results in several metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_ITA-MDT_Image-Timestep-Adaptive_Masked_Diffusion_Transformer_Framework_for_Image-Based_Virtual_Try-On_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_ITA-MDT_Image-Timestep-Adaptive_Masked_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Ji Woo and Ton, Tri and Pham, Trung X. and Koo, Gwanhyeong and Yoon, Sunjae and Yoo, Chang D.},
    title     = {ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28284-28294}
}

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


title: MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24149-24158
affiliations: ["Human Language Technology Center of Excellence", "Johns Hopkins University"]
cities: ["Unknown", "Baltimore"]
countries: ["Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kriz_MultiVENT_2.0_A_Massive_Multilingual_Benchmark_for_Event-Centric_Video_Retrieval_CVPR_2025_paper.html

authors:
Reno Kriz, Kate Sanders, David Etter, Kenton Murray, Cameron Carpenter, Hannah Recknor, Jimena Guallar-Blasco, Alexander Martin, Eugene Yang, Benjamin Van Durme

abstract:
Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce MultiVENT 2.0, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and over 3,900 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kriz_MultiVENT_2.0_A_Massive_Multilingual_Benchmark_for_Event-Centric_Video_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kriz_MultiVENT_2.0_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kriz_2025_CVPR,
    author    = {Kriz, Reno and Sanders, Kate and Etter, David and Murray, Kenton and Carpenter, Cameron and Recknor, Hannah and Guallar-Blasco, Jimena and Martin, Alexander and Yang, Eugene and Van Durme, Benjamin},
    title     = {MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24149-24158}
}

raw_affiliation:
[{"affiliation": "Human Language Technology Center of Excellence", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


authors: Dabing Yu, Zheng Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28091-28101
affiliations: ["OVOPARK", "Hohai University"]
cities: ["Suzhou", "Changzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_VolFormer_Explore_More_Comprehensive_Cube_Interaction_for_Hyperspectral_Image_Restoration_CVPR_2025_paper.html

title:
VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and Beyond

abstract:
Capitalizing on the talent of self-attention in capturing non-local features, Transformer architectures have exhibited remarkable performance in single hyperspectral image restoration. For hyperspectral images, each pixel is located in the hyperspectral image cubes with a large spectral dimension and two spatial dimensions. Although uni-dimensional self-attention, like channel self-attention or spatial self-attention, builds long-range dependencies in spectral or spatial dimensions, they lack more comprehensive interactions across dimensions. To tackle the above drawback, we propose a VolFormer, a volumetric self-attention embedded Transformer network for single hyperspectral image restoration. Specifically, we propose volumetric self-attention (VolSA), which extends the interaction from 2D flat to 3D cube. VolSA can simultaneously model token interaction in the 3D cube, mining the potential correlations between the hyperspectral image cube. An attention decomposition form is proposed to reduce the computational burden of modeling volumetric information. In practical terms, VolSA adapts double similarity matrixes in spatial and channel dimensions to implicitly model 3D context information while transforming the complexity from cubic to quadratic. Additionally, we introduce the explicit spectral location prior to enhance the proposed self-attention. This property allows the target token to perceive global spectral information while simultaneously assigning different levels of attention to tokens at varying wavelength bands. Extensive experiments demonstrate that VolFormer achieves record-high performance on hyperspectral image super-resolution, denoise and classification benchmarks. Particularly, VolSA is portable and achieves inspiring results in hyperspectral classification. The source code is available at https://github.com/yudadabing/VolFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_VolFormer_Explore_More_Comprehensive_Cube_Interaction_for_Hyperspectral_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_VolFormer_Explore_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Dabing and Gao, Zheng},
    title     = {VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and Beyond},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28091-28101}
}

raw_affiliation:
[{"affiliation": "OVOPARK", "city": "Suzhou", "country": "China"}, {"affiliation": "Hohai University", "city": "Changzhou", "country": "China"}]


title: Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation
authors: Lexin Fang, Yunyang Xu, Xiang Ma, Xuemei Li, Caiming Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10425-10434
raw_affiliation: [{"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}]
affiliations: ["School of Software, Shandong University"]
cities: ["Jinan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Minding_Fuzzy_Regions_A_Data-driven_Alternating_Learning_Paradigm_for_Stable_CVPR_2025_paper.html

abstract:
Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density differences, leading to label ambiguity. However, the existing model treats all data equally without taking quality differences into account in the training process, resulting in noisy labels negatively impacting model training and unstable feature representations. In this paper, a  **d**ata-driven **a**lternating **le**arning (**DALE**) paradigm is proposed to optimize the model's training process, achieving stable and high-precision segmentation. The paradigm focuses on two key points: (1) reducing the impact of noisy labels, and (2) calibrating unstable representations. To mitigate the negative impact of noisy labels, a loss consistency-based collaborative optimization method is proposed, and its effectiveness is theoretically demonstrated. Specifically, the label confidence parameters are introduced to dynamically adjust the influence of labels of different confidence levels during model training, thus reducing the influence of noise labels. To calibrate the learning bias of unstable representations, a distribution alignment method is proposed. This method restores the underlying distribution of unstable representations, thereby enhancing the discriminative capability of fuzzy region representations. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of the DALE paradigm, achieving an average performance improvement of up to 7.16%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Minding_Fuzzy_Regions_A_Data-driven_Alternating_Learning_Paradigm_for_Stable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Minding_Fuzzy_Regions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Lexin and Xu, Yunyang and Ma, Xiang and Li, Xuemei and Zhang, Caiming},
    title     = {Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10425-10434}
}


title: BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23615-23624
cities: ["Beijing", "Providence", "Liverpool", "Beijing", "Redmond"]
countries: ["China", "USA", "UK", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_BizGen_Advancing_Article-level_Visual_Text_Rendering_for_Infographics_Generation_CVPR_2025_paper.html

authors:
Yuyang Peng, Shishi Xiao, Keming Wu, Qisheng Liao, Bohan Chen, Kevin Lin, Danqing Huang, Ji Li, Yuhui Yuan

abstract:
Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made significant progress in sentence-level visual text rendering. In this paper, we focus on the more challenging scenarios of article-level visual text rendering and address a novel task of generating high-quality business content, including infographics and slides, based on user provided article-level descriptive prompts and ultra-dense layouts.  The fundamental challenges are twofold: significantly longer context lengths and the scarcity of high-quality business content data. In contrast to most previous works that focus on a limited number of sub-regions and sentence-level prompts, ensuring precise adherence to ultra-dense layouts with tens or even hundreds of sub-regions in business content is far more challenging. We make two key technical contributions: (i) the construction of scalable, high-quality business content dataset, i.e.,Infographics-650K, equipped with ultra-dense layouts and prompts by implementing a layer-wise retrieval-augmented infographic generation scheme; and (ii) a layout-guided cross attention scheme, which injects tens of region-wise prompts into a set of cropped region latent space according to the ultra-dense layouts, and refine each sub-regions flexibly during inference using a layout conditional CFG. We demonstrate the  strong results of our system compared to previous SOTA systems such as Flux and SD3 on our BizEval prompt set. Additionally, we conduct thorough ablation experiments to verify the effectiveness of each component. We hope our constructed Infographics-650K and BizEval can encourage the broader community to advance the progress of business content generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_BizGen_Advancing_Article-level_Visual_Text_Rendering_for_Infographics_Generation_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Yuyang and Xiao, Shishi and Wu, Keming and Liao, Qisheng and Chen, Bohan and Lin, Kevin and Huang, Danqing and Li, Ji and Yuan, Yuhui},
    title     = {BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23615-23624}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]

affiliations:
["Tsinghua University", "Brown University", "University of Liverpool", "Microsoft Research Asia", "Microsoft"]


title: MLVU: Benchmarking Multi-task Long Video Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13691-13701
cities: ["Beijing", "Beijing", "Shanghai", "Hangzhou", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_MLVU_Benchmarking_Multi-task_Long_Video_Understanding_CVPR_2025_paper.html

authors:
Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, Zheng Liu

abstract:
The evaluation of Long Video Understanding (LVU) performance poses an important but challenging research problem. Despite previous efforts, the existing video understanding benchmarks are severely constrained by several issues, especially the insufficient lengths of videos, a lack of diversity in video types and evaluation tasks, and the inappropriateness for evaluating LVU performances. To address the above problems, we propose a new benchmark called MLVU (Multi-task Long Video Understanding Benchmark) for the comprehensive and in-depth evaluation of LVU. MLVU presents the following critical values: 1) The substantial and flexible extension of video lengths, which enables the benchmark to evaluate LVU performance across a wide range of durations. 2) The inclusion of various video genres, such as movies, surveillance, egocentric videos, and cartoons, reflects the models' LVU performances in different scenarios. 3) The development of diversified evaluation tasks, which enables a comprehensive examination of MLLMs' key abilities in long-video understanding. The empirical study with 23 latest MLLMs reveals significant room for improvement in today's technique, as all existing methods struggle with most of the evaluation tasks and exhibit severe performance degradation when handling longer videos. Additionally, it suggests that factors such as context length, image-understanding ability, and the choice of LLM backbone can play critical roles in future advancements. We anticipate that MLVU will advance the research of LVU by providing a comprehensive and in-depth analysis of MLLMs. The code and dataset can be accessed from https://github.com/JUNJIE99/MLVU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_MLVU_Benchmarking_Multi-task_Long_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_MLVU_Benchmarking_Multi-task_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Junjie and Shu, Yan and Zhao, Bo and Wu, Boya and Liang, Zhengyang and Xiao, Shitao and Qin, Minghao and Yang, Xi and Xiong, Yongping and Zhang, Bo and Huang, Tiejun and Liu, Zheng},
    title     = {MLVU: Benchmarking Multi-task Long Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13691-13701}
}

raw_affiliation:
[{"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Academy of Arti\ufb01cial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "School of AI, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing University of Posts and Telecommunications", "Beijing Academy of Arti\ufb01cial Intelligence", "School of AI, Shanghai Jiao Tong University", "Zhejiang University", "Peking University"]


title: Recovering Dynamic 3D Sketches from Videos
authors: Jaeah Lee, Changwoon Choi, Young Min Kim, Jaesik Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12423-12432
raw_affiliation: [{"affiliation": "Seoul National University", "city": "Seoul", "country": "Republic of Korea"}]
affiliations: ["Seoul National University"]
cities: ["Seoul"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Recovering_Dynamic_3D_Sketches_from_Videos_CVPR_2025_paper.html

abstract:
Understanding 3D motion from videos presents inherent challenges due to the diverse types of movement, ranging from rigid and deformable objects to articulated structures. To overcome this, we propose Liv3Stroke, a novel approach for abstracting objects in motion with deformable 3D strokes. The detailed movements of an object may be represented by unstructured motion vectors or a set of motion primitives using a pre-defined articulation from a template model. Just as a free-hand sketch can intuitively visualize scenes or intentions with a sparse set of lines, we utilize a set of parametric 3D curves to capture a set of spatially smooth motion elements for general objects with unknown structures. We first extract noisy, 3D point cloud motion guidance from video frames using semantic features, and our approach deforms a set of curves to abstract essential motion features as a set of explicit 3D representations. Such abstraction enables an understanding of prominent components of motions while maintaining robustness to environmental factors. Our approach allows direct analysis of 3D object movements from video, tackling the uncertainty that typically occurs when translating real-world motion into recorded footage. The project page is accessible via: https://jaeah.me/liv3stroke_web.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Recovering_Dynamic_3D_Sketches_from_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Recovering_Dynamic_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jaeah and Choi, Changwoon and Kim, Young Min and Park, Jaesik},
    title     = {Recovering Dynamic 3D Sketches from Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12423-12432}
}


title: IM-Zero: Instance-level Motion Controllable Video Generation in a Zero-shot Manner
authors: Yuyang Huang, Yabo Chen, Li Ding, Xiaopeng Zhang, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7265-7275
affiliations: ["Shanghai Jiao Tong University", "Huawei Inc."]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_IM-Zero_Instance-level_Motion_Controllable_Video_Generation_in_a_Zero-shot_Manner_CVPR_2025_paper.html

abstract:
Controllability of video generation has been recently concerned in addition to the quality of generated videos. The main challenge to controllable video generation is to synthesize videos based on user-specified instance spatial locations and movement trajectories. However, existing methods suffer from a dilemma between the resource consumption, generation quality, and user controllability. As an efficient alternative to prohibitive training-based video generation, existing zero-shot video generation methods cannot generate high-quality and motion-consistent videos under the control of layouts and movement trajectories. In this paper, we propose a novel zero-shot method named IM-Zero that ameliorates instance-level motion controllable video generation with enhanced control accuracy, motion consistency, and richness of details to address this problem. Specifically, we first present a motion generation stage that extracts motion and textural guidance from keyframe candidates from pre-trained grounded text-to-image model to generate the desired coarse motion video. Subsequently, we develop a video refinement stage that injects the motion priors of pre-trained text-to-video models and detail priors of pre-trained text-to-image models into the latents of coarse motion videos to further enhance video motion consistency and richness of details. To our best knowledge, IM-Zero is the first to simultaneously achieve high-quality video generation and allow to control both layouts and movement trajectories in a zero-shot manner. Extensive experiments demonstrate that IM-Zero outperforms existing methods in terms of video quality, inter-frame consistency, and the alignment of location and trajectory. Furthermore, compared with existing methods, IM-Zero enjoys extra advantages of versatility in video generation, including motion control of subparts within instances, finer control of specifying instance shapes via masks, and more difficult tasks of motion transfer for customizing fine-grained motion patterns through reference videos and high-quality text-to-video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_IM-Zero_Instance-level_Motion_Controllable_Video_Generation_in_a_Zero-shot_Manner_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_IM-Zero_Instance-level_Motion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yuyang and Chen, Yabo and Ding, Li and Zhang, Xiaopeng and Dai, Wenrui and Zou, Junni and Xiong, Hongkai and Tian, Qi},
    title     = {IM-Zero: Instance-level Motion Controllable Video Generation in a Zero-shot Manner},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7265-7275}
}

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


title: EigenGS Representation: From Eigenspace to Gaussian Image Space
authors: Lo-Wei Tai, Ching-En Li, Cheng-Lin Chen, Chih-Jung Tsai, Hwann-Tzong Chen, Tyng-Luh Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13487-13496
affiliations: ["National Tsing Hua University", "Aeolus Robotics", "Academia Sinica"]
cities: ["Hsinchu", "Unknown", "Taipei"]
countries: ["Taiwan", "Unknown", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tai_EigenGS_Representation_From_Eigenspace_to_Gaussian_Image_Space_CVPR_2025_paper.html

abstract:
Principal Component Analysis (PCA), a classical dimensionality reduction technique, and 2D Gaussian representation, an adaptation of 3D Gaussian Splatting for image representation, offer distinct approaches to modeling visual data. We present EigenGS, a novel method that bridges these paradigms through an efficient transformation pipeline connecting eigenspace and image-space Gaussian representations. Our approach enables instant initialization of Gaussian parameters for new images without requiring per-image optimization from scratch, dramatically accelerating convergence. EigenGS introduces a frequency-aware learning mechanism that encourages Gaussians to adapt to different scales, effectively modeling varied spatial frequencies and preventing artifacts in high-resolution reconstruction. Extensive experiments demonstrate that EigenGS not only achieves superior reconstruction quality compared to direct 2D Gaussian fitting but also reduces the necessary parameter count and training time. The results highlight EigenGS's effectiveness and generalization ability across images with varying resolutions and diverse categories, making Gaussian-based image representation both high-quality and viable for real-time applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tai_EigenGS_Representation_From_Eigenspace_to_Gaussian_Image_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tai_EigenGS_Representation_From_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tai_2025_CVPR,
    author    = {Tai, Lo-Wei and Li, Ching-En and Chen, Cheng-Lin and Tsai, Chih-Jung and Chen, Hwann-Tzong and Liu, Tyng-Luh},
    title     = {EigenGS Representation: From Eigenspace to Gaussian Image Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13487-13496}
}

raw_affiliation:
[{"affiliation": "National Tsing Hua University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "Aeolus Robotics", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Academia Sinica", "city": "Taipei", "country": "Taiwan"}]


title: Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation
authors: Yue Zhang, Mingyue Bin, Yuyang Zhang, Zhongyuan Wang, Zhen Han, Chao Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4916-4926
cities: ["Wuhan", "Wuhan", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Link-based_Contrastive_Learning_for_One-Shot_Unsupervised_Domain_Adaptation_CVPR_2025_paper.html

abstract:
Unsupervised domain adaptation (UDA) aims to learn discriminative features from a labeled source domain by supervised learning and to transfer the knowledge to an unlabeled target domain via distribution alignment. However, in some real-world scenarios, e.g., public safety or access control, it's difficult to obtain sufficient source domain data, which hinders the application of existing UDA methods. To this end, this paper investigates a realistic but rarely studied problem called one-shot unsupervised domain adaptation (OSUDA), where there is only one example per category in the source domain and abundant unlabeled samples in the target domain. Compared with UDA, OSUDA faces dual challenges in both feature learning and domain alignment due to the lack of sufficient source data. To address these challenges, we propose a simple but effective link-based contrastive learning (LCL) method for OSUDA. On the one hand, with the help of in-domain links that indicate whether two samples are from the same cluster, LCL can learn discriminative features with abundant unlabeled target data. On the other hand, by constructing cross-domain links that show whether two clusters are bidirectionally matched, LCL can realize accurate domain alignment with only one source sample per category. Extensive experiments conducted on 4 public domain adaptation benchmarks, including VisDA-2017, Office-31, Office-Home, and DomainNet, demonstrate the effectiveness of the proposed LCL under the OSUDA setting. In addition, we build a realistic OSUDA surveillance video face recognition dataset, where LCL consistently improves the recognition accuracy across various face recognition methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Link-based_Contrastive_Learning_for_One-Shot_Unsupervised_Domain_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Link-based_Contrastive_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yue and Bin, Mingyue and Zhang, Yuyang and Wang, Zhongyuan and Han, Zhen and Liang, Chao},
    title     = {Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4916-4926}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "National Engineering Research Center for Multimedia Software(NERCMS)", "city": "Wuhan", "country": "China"}, {"affiliation": "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "National Engineering Research Center for Multimedia Software(NERCMS)", "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan"]


title: SmartCLIP: Modular Vision-language Alignment with Identification Guarantees
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29780-29790
cities: ["Pittsburgh", "Abu Dhabi", "Sydney"]
countries: ["USA", "UAE", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_SmartCLIP_Modular_Vision-language_Alignment_with_Identification_Guarantees_CVPR_2025_paper.html

authors:
Shaoan Xie, Lingjing Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang

abstract:
Contrastive Language-Image Pre-training (CLIP) \citep radford2021learning  has emerged as a pivotal model in computer vision and multimodal learning, achieving state-of-the-art performance at aligning visual and textual representations through contrastive learning. However, CLIP struggles with potential information misalignment in many image-text datasets and suffers from entangled representation. On the one hand, short captions for a single image in datasets like MSCOCO may describe disjoint regions in the image, leaving the model uncertain about which visual features to retain or disregard. On the other hand, directly aligning long captions with images can lead to the retention of entangled details, preventing the model from learning disentangled, atomic concepts -- ultimately limiting its generalization on certain downstream tasks involving short prompts. In this paper, we establish theoretical conditions that enable flexible alignment between textual and visual representations across varying levels of granularity. Specifically, our framework ensures that a model can not only preserve cross-modal semantic information in its entirety but also disentangle visual representations to capture fine-grained textual concepts.  Building on this foundation, we introduce \ours, a novel approach that identifies and aligns the most relevant visual and textual representations in a modular manner. Superior performance across various tasks demonstrates its capability to handle information misalignment and supports our identification theory. The code is available at https://github.com/Mid-Push/SmartCLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_SmartCLIP_Modular_Vision-language_Alignment_with_Identification_Guarantees_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Shaoan and Lingjing, Lingjing and Zheng, Yujia and Yao, Yu and Tang, Zeyu and Xing, Eric P. and Chen, Guangyi and Zhang, Kun},
    title     = {SmartCLIP: Modular Vision-language Alignment with Identification Guarantees},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29780-29790}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Mohamed bin Zayed University of Artificial Intelligence", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "University of Sydney", "city": "Sydney", "country": "Australia"}]

affiliations:
["Carnegie Mellon University", "Mohamed bin Zayed University of Artificial Intelligence", "University of Sydney"]


authors: Xin Jin, Haisheng Su, Kai Liu, Cong Ma, Wei Wu, Fei HUI, Junchi Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1407-1417
cities: ["Xi'an", "Shanghai", "Unknown", "Beijing"]
countries: ["China", "China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_UniMamba_Unified_Spatial-Channel_Representation_Learning_with_Group-Efficient_Mamba_for_LiDAR-based_CVPR_2025_paper.html

title:
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection

abstract:
Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces, which serialize the 3D voxels into the flattened 1D sequence for iterative self-attention. However, the spatial structure of 3D voxels will be inevitably destroyed during the serialization process. Besides, due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field. Inspired by the impressive performance of State Space Models (SSM), in this paper, we propose a novel Unified Mamba (UniMamba), which seamlessly integrates the merits of 3D convolution and SSM in a concise multi-head manner, aiming to perform "local and global" spatial context aggregation efficiently and simultaneously. Specifically, a UniMamba block is designed which mainly consists of spatial locality modeling, complementary Z-order serialization and local-global sequential aggregator. The spatial locality modeling module integrates 3D submanifold convolution to capture the dynamic spatial position embedding before serialization. Then the efficient Z-order curve is adopted for serialization both horizontally and vertically. Furthermore, the local-global sequential aggregator adopts the channel grouping strategy to efficiently encode both "local and global" spatial inter-dependencies using multi-head SSM. Additionally, an encoder-decoder architecture with stacked UniMamba blocks is formed to facilitate multi-scale spatial learning hierarchically. Extensive experiments are conducted on three popular datasets: nuScenes, Waymo and Argoverse 2. Particularly, our UniMamba achieves 70.2 mAP on the nuScenes dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_UniMamba_Unified_Spatial-Channel_Representation_Learning_with_Group-Efficient_Mamba_for_LiDAR-based_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Xin and Su, Haisheng and Liu, Kai and Ma, Cong and Wu, Wei and HUI, Fei and Yan, Junchi},
    title     = {UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1407-1417}
}

raw_affiliation:
[{"affiliation": "Chang\u2019an University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Computer Science, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "SenseAuto Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Chang\u2019an University", "School of Computer Science, Shanghai Jiao Tong University", "SenseAuto Research", "Tsinghua University"]


title: MaSS13K: A Matting-level Semantic Segmentation Benchmark
authors: Chenxi Xie, Minghan Li, Hui Zeng, Jun Luo, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14046-14056
affiliations: ["The Hong Kong Polytechnic University", "OPPO Research Institute"]
cities: ["Hong Kong", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_MaSS13K_A_Matting-level_Semantic_Segmentation_Benchmark_CVPR_2025_paper.html

abstract:
High-resolution semantic segmentation is essential for applications such as image editing, bokeh imaging, AR/VR, etc. Unfortunately, existing datasets often have limited resolution and lack precise mask details and boundaries. In this work, we build a large-scale, matting-level semantic segmentation dataset, named MaSS13K, which consists of 13,348 real-world images, all at 4K resolution. MaSS13K provides high-quality mask annotations of a number of objects, which are categorized into seven categories: human, vegetation, ground, sky, water, building, and others. MaSS13K features precise masks, with an average mask complexity 20-50 times higher than existing semantic segmentation datasets. We consequently present a method specifically designed for high-resolution semantic segmentation, namely MaSSFormer, which employs an efficient pixel decoder that aggregates high-level semantic features and low-level texture features across three stages, aiming to produce high-resolution masks with minimal computational cost. Finally, we propose a new learning paradigm, which integrates the high-quality masks of the seven given categories with pseudo labels from new classes, enabling MaSSFormer to transfer its accurate segmentation capability to other classes of objects. Our proposed MaSSFormer is comprehensively evaluated on the MaSS13K benchmark together with 14 representative segmentation models.  We expect that our meticulously annotated MaSS13K dataset and the MaSSFormer model can facilitate the research of high-resolution and high-quality semantic segmentation. Datasets and codes can be found at https://github.com/xiechenxi99/MaSS13K.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_MaSS13K_A_Matting-level_Semantic_Segmentation_Benchmark_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_MaSS13K_A_Matting-level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Chenxi and Li, Minghan and Zeng, Hui and Luo, Jun and Zhang, Lei},
    title     = {MaSS13K: A Matting-level Semantic Segmentation Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14046-14056}
}

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


title: Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game
authors: Keyizhi Xu, Chi Zhang, Zhan Chen, Zhongyuan Wang, Chunxia Xiao, Chao Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10265-10274
cities: ["Wuhan", "Wuhan", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Rethinking_the_Adversarial_Robustness_of_Multi-Exit_Neural_Networks_in_an_CVPR_2025_paper.html

abstract:
Multi-exit neural networks represent a promising approach to enhancing model inference efficiency, yet like common neural networks, they suffer from significantly reduced robustness against adversarial attacks. While some defense methods have been raised to strengthen the adversarial robustness of multi-exit neural networks, we identify a long-neglected flaw in the evaluation of previous studies: simply using a fixed set of exits for attack may lead to an overestimation of their defense capacity. Based on this finding, our work explores the following three key aspects in the adversarial robustness of multi-exit neural networks: (1) we discover that a mismatch of the network exits used by the attacker and defender is responsible for the overestimated robustness of previous defense methods; (2) by finding the best strategy in a two-player zero-sum game, we propose AIMER as an improved evaluation scheme to measure the intrinsic robustness of multi-exit neural networks; (3) going further, we introduce NEED defense method under the evaluation of AIMER that can optimize the defender's strategy by finding a Nash equilibrium of the game. Experiments over 3 datasets, 7 architectures, 7 attacks and 4 baselines show that AIMER evaluates the robustness 13.52% lower than previous methods under AutoAttack, while the robust performance of NEED surpasses single-exit networks of the same backbones by 5.58% maximally.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Rethinking_the_Adversarial_Robustness_of_Multi-Exit_Neural_Networks_in_an_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Rethinking_the_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Keyizhi and Zhang, Chi and Chen, Zhan and Wang, Zhongyuan and Xiao, Chunxia and Liang, Chao},
    title     = {Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10265-10274}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "National Engineering Research Center for Multimedia Software (NERCMS)", "city": "Wuhan", "country": "China"}, {"affiliation": "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "National Engineering Research Center for Multimedia Software (NERCMS)", "Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan"]


title: Enhancing Testing-Time Robustness for Trusted Multi-View Classification in the Wild
authors: Wei Liu, Yufei Chen, Xiaodong Yue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15508-15517
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Enhancing_Testing-Time_Robustness_for_Trusted_Multi-View_Classification_in_the_Wild_CVPR_2025_paper.html

abstract:
Trusted multi-view classification (TMVC) addresses variations in data quality by evaluating the reliability of each view based on prediction uncertainty at the evidence level, reducing the impact of low-quality views commonly encountered in real-world scenarios. However, existing TMVC methods often struggle to maintain robustness during testing, particularly when integrating noisy or corrupted views.  This limitation arises because the evidence collected by TMVC may be unreliable, frequently providing incorrect information due to complex view distributions and optimization challenges, ultimately leading to classification performance degradation. To enhance the robustness of TMVC methods in real-world conditions, we propose a generalized evidence filtering mechanism that is compatible with various fusion strategies commonly used in TMVC, including Belief Constraint Fusion, Aleatory Cumulative Belief Fusion, and Averaging Belief Fusion. Specifically, we frame the identification of unreliable evidence as a multiple testing problem and introduce p-values to control the risk of false identification. By selectively down-weighting unreliable evidence during testing, our mechanism ensures robust fusion and mitigates performance degradation. Both theoretical guarantees and empirical results demonstrate significant improvements in the classification performance of TMVC methods, supporting their reliable application in challenging, real-world environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Enhancing_Testing-Time_Robustness_for_Trusted_Multi-View_Classification_in_the_Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Enhancing_Testing-Time_Robustness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Wei and Chen, Yufei and Yue, Xiaodong},
    title     = {Enhancing Testing-Time Robustness for Trusted Multi-View Classification in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15508-15517}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Tongji University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "Artificial Intelligence Institute, Shanghai University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "VLN Lab, Shanghai, China", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Tongji University, Shanghai, China", "Artificial Intelligence Institute, Shanghai University, Shanghai, China", "VLN Lab, Shanghai, China"]


title: Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
authors: Lei Chen, Yuan Meng, Chen Tang, Xinzhu Ma, Jingyan Jiang, Xin Wang, Zhi Wang, Wenwu Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28306-28315
affiliations: ["Tsinghua University", "MMLab, CUHK", "Shenzhen Technology University"]
cities: ["Beijing", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Q-DiT_Accurate_Post-Training_Quantization_for_Diffusion_Transformers_CVPR_2025_paper.html

abstract:
Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality and scalability of image and video generation. However, despite their impressive capabilities, the substantial computational costs of these large-scale models pose significant challenges for real-world deployment. Post-Training Quantization (PTQ) emerges as a promising solution, enabling model compression and accelerated inference for pretrained models, without the costly retraining. However, research on DiT quantization remains sparse, and existing PTQ frameworks, primarily designed for traditional diffusion models, tend to suffer from biased quantization, leading to notable performance degradation. In this work, we identify that DiTs typically exhibit significant spatial variance in both weights and activations, along with temporal variance in activations. To address these issues, we propose Q-DiT, a novel approach that seamlessly integrates two key techniques: automatic quantization granularity allocation to handle the significant variance of weights and activations across input channels, and sample-wise dynamic activation quantization to adaptively capture activation changes across both timesteps and samples. Extensive experiments conducted on ImageNet and VBench demonstrate the effectiveness of the proposed Q-DiT. Specifically, when quantizing DiT-XL/2 to W6A8 on ImageNet (256 x256), Q-DiT achieves a remarkable reduction in FID by 1.09 compared to the baseline. Under the more challenging W4A8 setting, it maintains high fidelity in image and video generation, establishing a new benchmark for efficient, high-quality quantization in DiTs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Q-DiT_Accurate_Post-Training_Quantization_for_Diffusion_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Q-DiT_Accurate_Post-Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Lei and Meng, Yuan and Tang, Chen and Ma, Xinzhu and Jiang, Jingyan and Wang, Xin and Wang, Zhi and Zhu, Wenwu},
    title     = {Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28306-28315}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "MMLab, CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shenzhen Technology University", "city": "Shenzhen", "country": "China"}]


