# papers

title: Towards RAW Object Detection in Diverse Conditions
authors: Zhong-Yu Li, Xin Jin, Bo-Yuan Sun, Chun-Le Guo, Ming-Ming Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8859-8868
affiliations: ["VCIP, CS, Nankai University", "NKIARI, Shenzhen Futian"]
cities: ["Tianjin", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Towards_RAW_Object_Detection_in_Diverse_Conditions_CVPR_2025_paper.html

abstract:
Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light and weather conditions. We introduce the AODRaw dataset, which offers 7,785 high-resolution real RAW images with 135,601 annotated instances spanning 62 categories, capturing a broad range of indoor and outdoor scenes under 9 distinct light and weather conditions. Based on AODRaw that supports RAW and sRGB object detection, we provide a comprehensive benchmark for evaluating current detection methods. We find that sRGB pre-training constrains the potential of RAW object detection due to the domain gap between sRGB and RAW, prompting us to directly pre-train on the RAW domain.  However, it is harder for RAW pre-training to learn rich representations than sRGB pre-training due to the camera noise. To assist RAW pre-training, we distill the knowledge from an off-the-shelf model pre-trained on the sRGB domain. As a result, we achieve substantial improvements under diverse and adverse conditions without relying on extra pre-processing modules. The code and dataset are available at https://github.com/lzyhha/AODRaw.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Towards_RAW_Object_Detection_in_Diverse_Conditions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Towards_RAW_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhong-Yu and Jin, Xin and Sun, Bo-Yuan and Guo, Chun-Le and Cheng, Ming-Ming},
    title     = {Towards RAW Object Detection in Diverse Conditions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8859-8868}
}

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


title: FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training
authors: Anjia Cao, Xing Wei, Zhiheng Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4080-4090
cities: ["Xi'an", "Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_FLAME_Frozen_Large_Language_Models_Enable_Data-Efficient_Language-Image_Pre-training_CVPR_2025_paper.html

abstract:
Language-image pre-training faces significant challenges due to limited data in specific formats and the constrained capacities of text encoders. While prevailing methods attempt to address these issues through data augmentation and architecture modifications, they continue to struggle with processing long-form text inputs, and the inherent limitations of traditional CLIP text encoders lead to suboptimal downstream generalization. In this paper, we propose FLAME (Frozen Large lAnguage Models Enable data-efficient language-image pre-training) that leverages frozen large language models as text encoders, naturally processing long text inputs and demonstrating impressive multilingual generalization. FLAME comprises two key components: 1) a multifaceted prompt distillation technique for extracting diverse semantic representations from long captions, which better aligns with the multifaceted nature of images, and 2) a facet-decoupled attention mechanism, complemented by an offline embedding strategy, to ensure efficient computation. Extensive empirical evaluations demonstrate FLAME's superior performance. When trained on CC3M, FLAME surpasses the previous state-of-the-art by 4.9% in ImageNet top-1 accuracy. On YFCC15M, FLAME surpasses the WIT-400M-trained CLIP by 44.4% in average image-to-text recall@1 across 36 languages, and by 34.6% in text-to-image recall@1 for long-context retrieval on Urban-1k. Code is available at https://github.com/MIV-XJTU/FLAME.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_FLAME_Frozen_Large_Language_Models_Enable_Data-Efficient_Language-Image_Pre-training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_FLAME_Frozen_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Anjia and Wei, Xing and Ma, Zhiheng},
    title     = {FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4080-4090}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Computility Microelectronics", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Software Engineering, Xi\u2019an Jiaotong University", "Shenzhen University of Advanced Technology", "Guangdong Provincial Key Laboratory of Computility Microelectronics", "Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences"]


title: CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sections
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30928-30937
affiliations: ["University of Edinburgh", "University of British Columbia"]
cities: ["Edinburgh", "Vancouver"]
countries: ["UK", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Walker_CrossSDF_3D_Reconstruction_of_Thin_Structures_From_Cross-Sections_CVPR_2025_paper.html

authors:
Thomas Walker, Salvatore Esposito, Daniel Rebain, Amir Vaxman, Arno Onken, Changjian Li, Oisin Mac Aodha

abstract:
Reconstructing complex structures from planar cross-sections is a challenging problem, with wide-reaching applications in medical imaging, manufacturing, and topography. Out-of-the-box point cloud reconstruction methods can often fail due to the data sparsity between slicing planes, while current bespoke methods struggle to reconstruct thin geometric structures and preserve topological continuity. This is important for medical applications where thin vessel structures are present in CT and MRI scans. This paper introduces CrossSDF, a novel approach for extracting a 3D signed distance field from 2D signed distances generated from planar contours. Our approach makes the training of neural SDFs contour-aware by using losses designed for the case where geometry is known within 2D slices. Our results demonstrate a significant improvement over existing methods, effectively reconstructing thin structures and producing accurate 3D models without the interpolation artifacts or over-smoothing of prior approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Walker_CrossSDF_3D_Reconstruction_of_Thin_Structures_From_Cross-Sections_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Walker_CrossSDF_3D_Reconstruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Walker_2025_CVPR,
    author    = {Walker, Thomas and Esposito, Salvatore and Rebain, Daniel and Vaxman, Amir and Onken, Arno and Li, Changjian and Mac Aodha, Oisin},
    title     = {CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sections},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30928-30937}
}

raw_affiliation:
[{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}, {"affiliation": "University of British Columbia", "city": "Vancouver", "country": "Canada"}]


title: DV-Matcher: Deformation-based Non-rigid Point Cloud Matching Guided by Pre-trained Visual Features
authors: Zhangquan Chen, Puhua Jiang, Ruqi Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27264-27274
affiliations: ["Tsinghua Shenzhen International Graduate School", "Pengcheng Laboratory"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_DV-Matcher_Deformation-based_Non-rigid_Point_Cloud_Matching_Guided_by_Pre-trained_Visual_CVPR_2025_paper.html

abstract:
In this paper, we present DV-Matcher, a novel learning-based framework for estimating dense correspondences between non-rigidly deformable point clouds. Learning directly from unstructured point clouds without meshing or manual labelling, our framework delivers high-quality dense correspondences, which is of significant practical utility in point cloud processing. Our key contributions are two-fold: First, we propose a scheme to inject prior knowledge from pre-trained vision models into geometric feature learning, which effectively complements the local nature of geometric features with global and semantic information; Second, we propose a novel deformation-based module to promote the extrinsic alignment induced by the learned correspondences, which effectively enhances the feature learning. Experimental results show that our method achieves state-of-the-art results in matching non-rigid point clouds in both near-isometric and heterogeneous shape collection as well as more realistic partial and noisy data. Our code is available at https://github.com/rqhuang88/DV-Matcher.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_DV-Matcher_Deformation-based_Non-rigid_Point_Cloud_Matching_Guided_by_Pre-trained_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_DV-Matcher_Deformation-based_Non-rigid_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhangquan and Jiang, Puhua and Huang, Ruqi},
    title     = {DV-Matcher: Deformation-based Non-rigid Point Cloud Matching Guided by Pre-trained Visual Features},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27264-27274}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]


authors: Yuxuan Wang, Aming Wu, Muli Yang, Yukuan Min, Yihang Zhu, Cheng Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27618-27627
cities: ["Xian", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Reasoning_Mamba_Hypergraph-Guided_Region_Relation_Calculating_for_Weakly_Supervised_Affordance_CVPR_2025_paper.html

title:
Reasoning Mamba: Hypergraph-Guided Region Relation Calculating for Weakly Supervised Affordance Grounding

abstract:
This paper pays attention to Weakly Supervised Affordance Grounding (WSAG) task that aims to train model to identify affordance regions using human-object interaction images and egocentric images without the need for costly pixel-level annotations. Most existing methods usually consider the affordance regions to be isolated and directly employ class activation maps to conduct localization, ignoring the relationships with other object components and weakening the performance. For example, a cup's handle is combined with its body to achieve the pouring ability. Obviously, capturing the region relationships is beneficial for improving the localization accuracy of affordance regions. To this end, we first explore exploiting hypergraph to discover these relations and propose a Reasoning Mamba (R-Mamba) framework. We first extract feature embeddings from exo-centric and egocentric images to construct the hypergraphs consisting of multiple vertices and hyperedges, which capture the in-context local region relationships between different visual components. Subsequently, we design a Hypergraph-guided State Space (HSS) block to reorganize these local relationships from the global perspective. By this mechanism, the model could leverage the captured relationships to improve the localization accuracy of affordance regions. Extensive experiments and visualization analyses demonstrate the superiority of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Reasoning_Mamba_Hypergraph-Guided_Region_Relation_Calculating_for_Weakly_Supervised_Affordance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Reasoning_Mamba_Hypergraph-Guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuxuan and Wu, Aming and Yang, Muli and Min, Yukuan and Zhu, Yihang and Deng, Cheng},
    title     = {Reasoning Mamba: Hypergraph-Guided Region Relation Calculating for Weakly Supervised Affordance Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27618-27627}
}

raw_affiliation:
[{"affiliation": "School of Electronic Engineering, Xidian University", "city": "Xian", "country": "China"}, {"affiliation": "Institute for Infocomm Research (I2R), A*STAR", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Electronic Engineering, Xidian University", "Institute for Infocomm Research (I2R), A*STAR"]


title: Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
authors: Takuma Fukuda, Hiroshi Kera, Kazuhiko Kawamoto
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4884-4893
affiliations: ["Chiba University", "Chiba University", "Zuse Institute Berlin", "Chiba University"]
cities: ["Chiba", "Chiba", "Berlin", "Chiba"]
countries: ["Japan", "Japan", "Germany", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fukuda_Adapter_Merging_with_Centroid_Prototype_Mapping_for_Scalable_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods trade-off between inference time and accuracy, ACMap consolidates task-specific adapters into a single adapter, ensuring constant inference time across tasks without compromising accuracy.The framework employs adapter merging to build a shared subspace that aligns task representations and mitigates forgetting, while centroid prototype mapping maintains high accuracy through consistent adaptation in the shared subspace. To further improve scalability, an early stopping strategy limits adapter merging as tasks increase. Extensive experiments on five benchmark datasets demonstrate that ACMap matches state-of-the-art accuracy while maintaining inference speed comparable to the fastest existing methods.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fukuda_Adapter_Merging_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fukuda_2025_CVPR,
    author    = {Fukuda, Takuma and Kera, Hiroshi and Kawamoto, Kazuhiko},
    title     = {Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4884-4893}
}

raw_affiliation:
[{"affiliation": "Chiba University", "city": "Chiba", "country": "Japan"}, {"affiliation": "Chiba University", "city": "Chiba", "country": "Japan"}, {"affiliation": "Zuse Institute Berlin", "city": "Berlin", "country": "Germany"}, {"affiliation": "Chiba University", "city": "Chiba", "country": "Japan"}]


title: OpenSDI: Spotting Diffusion-Generated Images in the Open World
authors: Yabin Wang, Zhiwu Huang, Xiaopeng Hong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4291-4301
affiliations: ["Xi\u2019an Jiaotong University", "University of Southampton", "Harbin Institute of Technology"]
cities: ["Xi'an", "Southampton", "Harbin"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OpenSDI_Spotting_Diffusion-Generated_Images_in_the_Open_World_CVPR_2025_paper.html

abstract:
This paper identifies OpenSDI, a challenge for spotting diffusion-generated images in open-world settings. In response to this challenge, we define a new benchmark, the OpenSDI dataset (OpenSDID), which stands out from existing datasets due to its diverse use of large vision-language models that simulate open-world diffusion-based manipulations. Another outstanding feature of OpenSDID is its inclusion of both detection and localization tasks for images manipulated globally and locally by diffusion models. To address the OpenSDI challenge, we propose a Synergizing Pretrained Models (SPM) scheme to build up a mixture of foundation models. This approach exploits a collaboration mechanism with multiple pretrained foundation models to enhance generalization in the OpenSDI context, moving beyond traditional training by synergizing multiple pretrained models through prompting and attending strategies. Building on this scheme, we introduce MaskCLIP, an SPM-based model that aligns Contrastive Language-Image Pre-Training (CLIP) with Masked Autoencoder (MAE). Extensive evaluations on OpenSDID show that MaskCLIP significantly outperforms current state-of-the-art methods for the OpenSDI challenge, achieving remarkable relative improvements of 14.23% in IoU (14.11% in F1) and 2.05% in accuracy (2.38% in F1) compared to the second-best model in localization and detection tasks, respectively. Our dataset and code are available at https://github.com/iamwangyabin/OpenSDI.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OpenSDI_Spotting_Diffusion-Generated_Images_in_the_Open_World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OpenSDI_Spotting_Diffusion-Generated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
    title     = {OpenSDI: Spotting Diffusion-Generated Images in the Open World},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4291-4301}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Southampton", "city": "Southampton", "country": "UK"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}]


title: Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement
authors: Qiyuan Dai, Hanzhuo Huang, Yu Wu, Sibei Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25444-25453
cities: ["Shanghai", "Wuhan", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Adaptive_Part_Learning_for_Fine-Grained_Generalized_Category_Discovery_A_Plug-and-Play_CVPR_2025_paper.html

abstract:
Generalized Category Discovery (GCD) aims to recognize unlabeled images from known and novel classes by distinguishing novel classes from known ones, while also transferring knowledge from another set of labeled images with known classes. Existing GCD methods rely on self-supervised vision transformers such as DINO for representation learning. However, focusing solely on the global representation of the DINO CLS token introduces an inherent trade-off between discriminability and generalization. In this paper, we introduce an adaptive part discovery and learning method, called APL, which generates consistent object parts and their correspondences across different similar images using a set of shared learnable part queries and DINO part priors, without requiring any additional annotations. More importantly, we propose a novel all-min contrastive loss to learn discriminative yet generalizable part representation, which adaptively highlights discriminative object parts to distinguish similar categories for enhanced discriminability while simultaneously sharing other parts to facilitate knowledge transfer for improved generalization. Our APL can easily be incorporated into different GCD frameworks by replacing their CLS token feature with our part representations, showing significant enhancements on fine-grained datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Adaptive_Part_Learning_for_Fine-Grained_Generalized_Category_Discovery_A_Plug-and-Play_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_Adaptive_Part_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Qiyuan and Huang, Hanzhuo and Wu, Yu and Yang, Sibei},
    title     = {Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25444-25453}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Technology, ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Information Science and Technology, ShanghaiTech University", "Wuhan University", "Sun Yat-sen University"]


title: Online Task-Free Continual Learning via Dynamic Expansionable Memory Distribution
authors: Fei Ye, Adrian G. Bors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20512-20522
affiliations: ["University of Electronic Science and Technology of China", "University of York"]
cities: ["Chengdu", "York"]
countries: ["China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Online_Task-Free_Continual_Learning_via_Dynamic_Expansionable_Memory_Distribution_CVPR_2025_paper.html

abstract:
Recent continuous learning (CL) research primarily addresses catastrophic forgetting within a straightforward learning framework where class and task information are predefined. However, in the Task-Free Continual Learning (TFCL), representing a more realistic and challenging CL scenarios, such information is typically absent. In this paper, we address the online TFCL by introducing an innovative memory management approach, by incorporating a dynamic memory system for storing selected data representatives from evolving distributions while a dynamically expandable memory system enables the retention of essential long-term knowledge. The proposed dynamic expandable memory system manages a series of memory distributions, each designed to represent the information from a distinct data category. A new memory expansion mechanism that assesses the proximity between incoming samples and existing memory distributions is proposed for evaluating when to add new memory distributions into the system. Additionally, a novel memory distribution augmentation technique is proposed for selectively gathering suitable samples for each memory distribution, enhancing the statistical robustness over time. To prevent memory saturation before the training phase, we introduce a memory distribution reduction strategy that automatically eliminates overlapping memory distributions, ensuring adequate capacity for accommodating new information in subsequent learning episodes. We conduct a series of experiments demonstrating that our proposed approach attains state-of-the-art performance in both supervised and unsupervised learning contexts. The source code is available at https://github.com/dtuzi123/DEMD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Online_Task-Free_Continual_Learning_via_Dynamic_Expansionable_Memory_Distribution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Online_Task-Free_Continual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Fei and Bors, Adrian G.},
    title     = {Online Task-Free Continual Learning via Dynamic Expansionable Memory Distribution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20512-20522}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5510-5522
affiliations: ["Netflix Eyeline Studios", "Johns Hopkins University"]
cities: ["Los Angeles", "Baltimore"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_Lux_Post_Facto_Learning_Portrait_Performance_Relighting_with_Conditional_Video_CVPR_2025_paper.html

title:
Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Dataset

authors:
Yiqun Mei, Mingming He, Li Ma, Julien Philip, Wenqi Xian, David M George, Xueming Yu, Gabriel Dedic, Ahmet Levent Taşel, Ning Yu, Vishal M. Patel, Paul Debevec

abstract:
Video portrait relighting remains challenging because the results need to be both photorealistic and temporally stable.This typically requires a strong model design that can capture complex facial reflections as well as intensive training on a high-quality paired video dataset, such as dynamic one-light-at-a-time (OLAT). In this work, we introduce Lux Post Facto, a novel portrait video relighting method that produces both photorealistic and temporally consistent lighting effects. From the model side, we design a new conditional video diffusion model built upon state-of-the-art pre-trained video diffusion model, alongside a new lighting injection mechanism to enable precise control. This way we leverage strong spatial and temporal generative capability to generate plausible solutions to the ill-posed relighting problem. Our technique uses a hybrid dataset consisting of static expression OLAT data and in-the-wild portrait performance videos to jointly learn relighting and temporal modeling. This avoids the need to acquire paired video data in different lighting conditions. Our extensive experiments show that our model produces state-of-the-art results both in terms of photorealism and temporal consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mei_Lux_Post_Facto_Learning_Portrait_Performance_Relighting_with_Conditional_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_Lux_Post_Facto_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Yiqun and He, Mingming and Ma, Li and Philip, Julien and Xian, Wenqi and George, David M and Yu, Xueming and Dedic, Gabriel and Ta\c{s}el, Ahmet Levent and Yu, Ning and Patel, Vishal M. and Debevec, Paul},
    title     = {Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5510-5522}
}

raw_affiliation:
[{"affiliation": "Netflix Eyeline Studios", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


title: DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
authors: Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7664-7674
affiliations: ["School of EIC, Huazhong University of Science & Technology", "ByteDance"]
cities: ["Wuhan", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_DiG_Scalable_and_Efficient_Diffusion_Models_with_Gated_Linear_Attention_CVPR_2025_paper.html

abstract:
Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT). However, DiT models have faced challenges with quadratic complexity efficiency, especially when handling long sequences. In this paper, we aim to incorporate the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone. Specifically, we introduce Diffusion Gated Linear Attention Transformers (DiG), a simple, adoptable solution with minimal parameter overhead. We offer two variants, i.e., a plain and U-shape architecture, showing superior efficiency and competitive effectiveness. In addition to superior performance to DiT and other sub-quadratic-time diffusion models at 256x256 resolution, DiG demonstrates greater efficiency than these methods starting from a 512 resolution. Specifically, DiG-S/2 is 2.5x faster and saves 75.7% GPU memory compared to DiT-S/2 at a 1792 resolution. Additionally, DiG-XL/2 is 4.2x faster than the Mamba-based model at a 1024 resolution and 1.8x faster than DiT with FlashAttention-2 at a 2048 resolution. The code is released at https://github.com/hustvl/DiG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_DiG_Scalable_and_Efficient_Diffusion_Models_with_Gated_Linear_Attention_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_DiG_Scalable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Lianghui and Huang, Zilong and Liao, Bencheng and Liew, Jun Hao and Yan, Hanshu and Feng, Jiashi and Wang, Xinggang},
    title     = {DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7664-7674}
}

raw_affiliation:
[{"affiliation": "School of EIC, Huazhong University of Science & Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]


title: Monocular and Generalizable Gaussian Talking Head Animation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5523-5534
cities: ["Guangzhou", "Munich", "Guangzhou", "Guangzhou", "Hong Kong"]
countries: ["China", "Germany", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gong_Monocular_and_Generalizable_Gaussian_Talking_Head_Animation_CVPR_2025_paper.html

authors:
Shengjie Gong, Haojie Li, Jiapeng Tang, Dongming Hu, Shuangping Huang, Hao Chen, Tianshui Chen, Zhuoman Liu

abstract:
In this work, we introduce Monocular and Generalizable Gaussian Talking Head Animation (MGGTalk), which requires monocular datasets and generalizes to unseen identities without personalized re-training. Compared with previous 3D Gaussian Splatting (3DGS) methods that requires elusive multi-view datasets or tedious personalized learning/inference, MGGtalk enables more practical and broader applications. However, in the absence of multi-view and personalized training data, the incompleteness of geometric and appearance information poses a significant challenge. To address these challenges, MGGTalk explores depth information to enhance geometric and facial symmetry characteristics to supplement both geometric and appearance features. Initially, based on the pixel-wise geometric information obtained from depth estimation, we incorporate symmetry operations and point cloud filtering techniques to ensure a complete and precise position parameter for 3DGS. Subsequently, we adopt a two-stage strategy with symmetric priors for predicting the remaining 3DGS parameters. We begin by predicting Gaussian parameters for the visible facial regions of the source image. These parameters are subsequently utilized to improve the prediction of Gaussian parameters for the non-visible regions. Extensive experiments demonstrate that MGGTalk surpasses previous state-of-the-art methods, achieving superior performance across various metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gong_Monocular_and_Generalizable_Gaussian_Talking_Head_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gong_Monocular_and_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gong_2025_CVPR,
    author    = {Gong, Shengjie and Li, Haojie and Tang, Jiapeng and Hu, Dongming and Huang, Shuangping and Chen, Hao and Chen, Tianshui and Liu, Zhuoman},
    title     = {Monocular and Generalizable Gaussian Talking Head Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5523-5534}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Pazhou Laboratory", "city": "Guangzhou", "country": "China"}, {"affiliation": "Guangdong University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]

affiliations:
["South China University of Technology", "Technical University of Munich", "Pazhou Laboratory", "Guangdong University of Technology", "The Hong Kong Polytechnic University"]


title: Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level Tasks
authors: Cheng Lei, Ao Li, Hu Yao, Ce Zhu, Le Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14954-14964
affiliations: ["University of Electronic Science and Technology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_Rethinking_Token_Reduction_with_Parameter-Efficient_Fine-Tuning_in_ViT_for_Pixel-Level_CVPR_2025_paper.html

abstract:
Parameter-efficient fine-tuning (PEFT) adapts pre-trained models to new tasks by updating only a small subset of parameters, achieving efficiency but still facing significant inference costs driven by input token length. This challenge is even more pronounced in pixel-level tasks, which require longer input sequences compared to image-level tasks. Although token reduction (TR) techniques can help reduce computational demands, they often lead to homogeneous attention patterns that compromise performance in pixel-level scenarios. This study underscores the importance of maintaining attention diversity for these tasks and proposes to enhance attention diversity while ensuring the completeness of token sequences. Our approach effectively reduces the number of tokens processed within transformer blocks, improving computational efficiency without sacrificing performance on several pixel-level tasks. We also demonstrate the superior generalization capability of our proposed method compared to challenging baseline models. The source code will be made available at https://github.com/AVC2-UESTC/DAR-TR-PEFT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_Rethinking_Token_Reduction_with_Parameter-Efficient_Fine-Tuning_in_ViT_for_Pixel-Level_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_Rethinking_Token_Reduction_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Cheng and Li, Ao and Yao, Hu and Zhu, Ce and Zhang, Le},
    title     = {Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14954-14964}
}


title: SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion
authors: Xuan Zhu, Jijun Xiang, Xianqi Wang, Longliang Liu, Yu Wang, Hong Zhang, Fei Guo, Xin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16619-16628
affiliations: ["Huazhong University of Science and Technology", "Honor Device Co., Ltd"]
cities: ["Wuhan", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_SVDC_Consistent_Direct_Time-of-Flight_Video_Depth_Completion_with_Frequency_Selective_CVPR_2025_paper.html

abstract:
Lightweight direct Time-of-Flight (dToF) sensors are ideal for 3D sensing on mobile devices. However, due to the manufacturing constraints of compact devices and the inherent physical principles of imaging, dToF depth maps are sparse and noisy. In this paper, we propose a novel video depth completion method, called SVDC, by fusing the sparse dToF data with the corresponding RGB guidance. Our method employs a multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the sparse dToF imaging. Misalignment between consecutive frames during multi-frame fusion could cause blending between object edges and the background, which results in a loss of detail. To address this, we introduce an adaptive frequency selective fusion (AFSF) module, which automatically selects convolution kernel sizes to fuse multi-frame features. Our AFSF utilizes a channel-spatial enhancement attention (CSEA) module to enhance features and generates an attention map as fusion weights. The AFSF ensures edge detail recovery while suppressing high-frequency noise in smooth regions. To further enhance temporal consistency, We propose a cross-window consistency loss to ensure consistent predictions across different windows, effectively reducing flickering. Our proposed SVDC achieves optimal accuracy and consistency on the TartanAir and Dynamic Replica datasets. Code is available at https://github.com/Lan1eve/SVDC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SVDC_Consistent_Direct_Time-of-Flight_Video_Depth_Completion_with_Frequency_Selective_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_SVDC_Consistent_Direct_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Xuan and Xiang, Jijun and Wang, Xianqi and Liu, Longliang and Wang, Yu and Zhang, Hong and Guo, Fei and Yang, Xin},
    title     = {SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16619-16628}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Honor Device Co., Ltd", "city": "Shenzhen", "country": "China"}]


title: Locally Orderless Images for Optimization in Differentiable Rendering
authors: Ishit Mehta, Manmohan Chandraker, Ravi Ramamoorthi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5763-5772
raw_affiliation: [{"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}]
affiliations: ["University of California San Diego"]
cities: ["San Diego"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mehta_Locally_Orderless_Images_for_Optimization_in_Differentiable_Rendering_CVPR_2025_paper.html

abstract:
Problems in differentiable rendering often involve optimizing scene parameters that cause motion in image space. The gradients for such parameters tend to be sparse, leading to poor convergence. While existing methods address this sparsity through proxy gradients such as topological derivatives or lagrangian derivatives, they make simplifying assumptions about rendering. Multi-resolution image pyramids offer an alternative approach but prove unreliable in practice. We introduce a method that uses locally orderless images --- where each pixel maps to a histogram of intensities that preserves local variations in appearance. Using an inverse rendering objective that minimizes histogram distance, our method extends support for sparsely defined image gradients and recovers optimal parameters. We validate our method on various inverse problems using both synthetic and real data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mehta_Locally_Orderless_Images_for_Optimization_in_Differentiable_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mehta_Locally_Orderless_Images_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mehta_2025_CVPR,
    author    = {Mehta, Ishit and Chandraker, Manmohan and Ramamoorthi, Ravi},
    title     = {Locally Orderless Images for Optimization in Differentiable Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5763-5772}
}


authors: Basim Azam, Naveed Akhtar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2976-2985
affiliations: ["School of Computing and Information Systems, The University of Melbourne"]
cities: ["Melbourne"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Azam_Plug-and-Play_Interpretable_Responsible_Text-to-Image_Generation_via_Dual-Space_Multi-facet_Concept_Control_CVPR_2025_paper.html

title:
Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control

abstract:
Ethical issues around text-to-image (T2I) models demand a comprehensive control over the generative content. Existing techniques addressing these issues for responsible T2I models aim for the  generated content to be fair and safe (non-violent/explicit). However, these methods  remain bounded to handling the facets of responsibility concepts individually, while also lacking in interpretability. Moreover,  they often require alteration to the original model, which compromises the model performance. In this work, we propose a unique technique to enable  responsible T2I generation by simultaneously accounting for an  extensive range of concepts for fair and safe content generation in a scalable manner. The key idea is to distill the target T2I pipeline with an external plug-and-play mechanism that learns an interpretable composite responsible space for the desired concepts, conditioned on the target T2I pipeline. We use knowledge distillation and concept whitening to enable this. At inference, the learned space is utilized to modulate the generative content. A typical T2I pipeline presents two plug-in points for our approach, namely; the text embedding space and the diffusion model latent space. We develop modules for both points and show the effectiveness of our approach with a range of strong results. Our code and models will be made  public after paper acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Azam_Plug-and-Play_Interpretable_Responsible_Text-to-Image_Generation_via_Dual-Space_Multi-facet_Concept_Control_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Azam_Plug-and-Play_Interpretable_Responsible_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Azam_2025_CVPR,
    author    = {Azam, Basim and Akhtar, Naveed},
    title     = {Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2976-2985}
}

raw_affiliation:
[{"affiliation": "School of Computing and Information Systems, The University of Melbourne", "city": "Melbourne", "country": "Australia"}]


authors: Eunji Kim, Siwon Kim, Minjun Park, Rahim Entezari, Sungroh Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13361-13370
cities: ["Seoul", "Seoul", "Unknown", "Seoul"]
countries: ["South Korea", "South Korea", "Unknown", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Rethinking_Training_for_De-biasing_Text-to-Image_Generation_Unlocking_the_Potential_of_CVPR_2025_paper.html

title:
Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion

abstract:
Recent advancements in text-to-image models, such as Stable Diffusion, show significant demographic biases. Existing de-biasing techniques rely heavily on additional training, which imposes high computational costs and risks of compromising core image generation functionality. This hinders them from being widely adopted to real-world applications. In this paper, we explore Stable Diffusion's overlooked potential to reduce bias without requiring additional training. Through our analysis, we uncover that initial noises associated with minority attributes form "minority regions" rather than scattered. We view these "minority regions" as opportunities in SD to reduce bias. To unlock the potential, we propose a novel de-biasing method called 'weak guidance,' carefully designed to guide a random noise to the minority regions without compromising semantic integrity. Through analysis and experiments on various versions of SD, we demonstrate that our proposed approach effectively reduces bias without additional training, achieving both efficiency and preservation of core image generation functionality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Rethinking_Training_for_De-biasing_Text-to-Image_Generation_Unlocking_the_Potential_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Rethinking_Training_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Eunji and Kim, Siwon and Park, Minjun and Entezari, Rahim and Yoon, Sungroh},
    title     = {Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13361-13370}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Interdisciplinary Program in Artificial Intelligence, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Stability AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "AIIS, ASRI, INMC, ISRC, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of Electrical and Computer Engineering, Seoul National University", "Interdisciplinary Program in Artificial Intelligence, Seoul National University", "Stability AI", "AIIS, ASRI, INMC, ISRC, Seoul National University"]


title: FLAIR: VLM with Fine-grained Language-informed Image Representations
authors: Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu, Zeynep Akata, Stephan Alaniz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24884-24894
cities: ["Munich", "Munich", "Munich", "Munich"]
countries: ["Germany", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_FLAIR_VLM_with_Fine-grained_Language-informed_Image_Representations_CVPR_2025_paper.html

abstract:
CLIP has shown impressive results in aligning images and text at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and texts at a global level. To address this issue, we propose FLAIR, Fine-grained Language-informed Image Representations, an approach that utilizes long and detailed image descriptions to learn localized image embeddings. By sampling diverse sub-captions that describe fine-grained details about an image, we train our vision-language model to produce not only global embeddings but also text-specific image representations. Our model introduces text-conditioned attention pooling on top of local image tokens to produce fine-grained image representations that excel at retrieving detailed image content. We achieve state-of-the-art performance on both, existing multimodal retrieval benchmarks, as well as, our newly introduced fine-grained retrieval task which evaluates vision-language models' ability to retrieve partial image content. Furthermore, our experiments demonstrate the effectiveness of FLAIR trained on 30M image-text pairs in capturing fine-grained visual information, including zero-shot semantic segmentation, outperforming models trained on billions of pairs. Code is available at: https://github.com/ExplainableML/flair.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_FLAIR_VLM_with_Fine-grained_Language-informed_Image_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_FLAIR_VLM_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Rui and Kim, Sanghwan and Georgescu, Mariana-Iuliana and Akata, Zeynep and Alaniz, Stephan},
    title     = {FLAIR: VLM with Fine-grained Language-informed Image Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24884-24894}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning (MCML)", "city": "Munich", "country": "Germany"}, {"affiliation": "Helmholtz Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Data Science Institute (MDSI)", "city": "Munich", "country": "Germany"}]

affiliations:
["Technical University of Munich", "Munich Center for Machine Learning (MCML)", "Helmholtz Munich", "Munich Data Science Institute (MDSI)"]


title: GG-SSMs: Graph-Generating State Space Models
authors: Nikola Zubic, Davide Scaramuzza
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28863-28873
affiliations: ["Robotics and Perception Group, University of Zurich, Switzerland"]
cities: ["Zurich"]
countries: ["Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zubic_GG-SSMs_Graph-Generating_State_Space_Models_CVPR_2025_paper.html

abstract:
State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data. While methods like Mamba and VMamba introduce selective and flexible scanning strategies, they rely on predetermined paths, which fails to efficiently capture complex dependencies. We introduce Graph-Generating State Space Models (GG-SSMs), a novel framework that overcomes these limitations by dynamically constructing graphs based on feature relationships. Using Chazelle's Minimum Spanning Tree algorithm, GG-SSMs adapt to the inherent data structure, enabling robust feature propagation across dynamically generated graphs and efficiently modeling complex dependencies. We validate GG-SSMs on 11 diverse datasets, including event-based eye-tracking, ImageNet classification, optical flow estimation, and six time series datasets. GG-SSMs achieve state-of-the-art performance across all tasks, surpassing existing methods by significant margins. Specifically, GG-SSM attains a top-1 accuracy of 84.9% on ImageNet, outperforming prior SSMs by 1%, reducing the KITTI-15 error rate to 2.77%, and improving eye-tracking detection rates by up to 0.33% with fewer parameters. These results demonstrate that dynamic scanning based on feature relationships significantly improves SSMs' representational power and efficiency, offering a versatile tool for various applications in computer vision and beyond.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zubic_GG-SSMs_Graph-Generating_State_Space_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zubic_GG-SSMs_Graph-Generating_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zubic_2025_CVPR,
    author    = {Zubic, Nikola and Scaramuzza, Davide},
    title     = {GG-SSMs: Graph-Generating State Space Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28863-28873}
}

raw_affiliation:
[{"affiliation": "Robotics and Perception Group, University of Zurich, Switzerland", "city": "Zurich", "country": "Switzerland"}]


title: Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
authors: Amir Barda, Matheus Gadelha, Vladimir G. Kim, Noam Aigerman, Amit H. Bermano, Thibault Groueix
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16273-16282
cities: ["Tel Aviv", "San Jose", "San Jose", "Montreal", "Tel Aviv", "San Jose"]
countries: ["Israel", "USA", "USA", "Canada", "Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Barda_Instant3dit_Multiview_Inpainting_for_Fast_Editing_of_3D_Objects_CVPR_2025_paper.html

abstract:
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in ~3 seconds, without the need for running an SDS type of optimization.Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Barda_Instant3dit_Multiview_Inpainting_for_Fast_Editing_of_3D_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Barda_Instant3dit_Multiview_Inpainting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Barda_2025_CVPR,
    author    = {Barda, Amir and Gadelha, Matheus and Kim, Vladimir G. and Aigerman, Noam and Bermano, Amit H. and Groueix, Thibault},
    title     = {Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16273-16282}
}

raw_affiliation:
[{"affiliation": "T el A viv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Universit\u00b4e de Montr \u00b4eal", "city": "Montreal", "country": "Canada"}, {"affiliation": "T el A viv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]

affiliations:
["T el A viv University", "Adobe Research", "Adobe Research", "Universit\u00b4e de Montr \u00b4eal", "T el A viv University", "Adobe Research"]


title: STDD: Spatio-Temporal Dual Diffusion for Video Generation
authors: Shuaizhen Yao, Xiaoya Zhang, Xin Liu, Mengyi Liu, Zhen Cui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12575-12584
cities: ["Nanjing", "Nanjing", "Unknown", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_STDD_Spatio-Temporal_Dual_Diffusion_for_Video_Generation_CVPR_2025_paper.html

abstract:
Diffusion probabilistic model is becoming the cornerstone of data generation, especially generating high-quality images. As an extension, video diffusion generation is in urgent need of a principled temporal-sequence diffusion way, while the spatial-domain diffusion dominates most video diffusion methods. In this work, we propose an explicit Spatio-Temporal Dual Diffusion (STDD) method by principledly extending the standard diffusion model to a spatio-temporal diffusion model for joint spatial and temporal noise propagation/reduction. Mathematically, an analysable dual diffusion process is derived to accumulate noises/information in temporal sequence as well as spatial domain. Correspondingly, we theoretically derive a spatio-temporal probabilistic reverse diffusion process and propose an accelerated sampling way to reduce the inference cost.  In principle, the spatio-temporal dual diffusion enables the information of previous frames to be transferred to the current frame, which thus could be beneficial for video consistency. Extensive experiments demonstrate that our proposed STDD is more competitive over the state-of-the-art methods in the task of video generation/prediction as well as text-to-video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_STDD_Spatio-Temporal_Dual_Diffusion_for_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_STDD_Spatio-Temporal_Dual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Shuaizhen and Zhang, Xiaoya and Liu, Xin and Liu, Mengyi and Cui, Zhen},
    title     = {STDD: Spatio-Temporal Dual Diffusion for Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12575-12584}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing Seetacloud Technology Co., Ltd", "city": "Nanjing", "country": "China"}, {"affiliation": "Kuaishou", "city": "Unknown", "country": "China"}, {"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}]

affiliations:
["Nanjing University of Science and Technology", "Nanjing Seetacloud Technology Co., Ltd", "Kuaishou", "Beijing Normal University"]


title: Implicit Correspondence Learning for Image-to-Point Cloud Registration
authors: Xinjun Li, Wenfei Yang, Jiacheng Deng, Zhixin Cheng, Xu Zhou, Tianzhu Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16922-16931
cities: ["Hefei", "Hefei", "Shenzhen"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Implicit_Correspondence_Learning_for_Image-to-Point_Cloud_Registration_CVPR_2025_paper.html

abstract:
Image-to-point cloud registration aims to estimate the camera pose of a given image within a 3D scene point cloud. In this area, matching-based methods have achieved leading performance by first detecting the overlapping region, then matching point and pixel features learned by neural networks and finally using the PnP-RANSAC algorithm to estimate camera pose. However, achieving accurate image-to-point cloud registration remains challenging because the overlapping region detection is unreliable merely relying on point-wise classification, direct alignment of cross-modal data is difficult and indirect optimization objective leads to unstable registration results. To address these challenges, we propose a novel implicit correspondence learning method, including a Geometric Prior-guided overlapping region Detection Module (GPDM), an Implicit Correspondence Learning Module (ICLM), and a Pose Regression Module (PRM). The proposed method enjoys several merits. First, the proposed GPDM can precisely detect the overlapping region. Second, the ICLM can generate robust cross-modality correspondences. Third, the PRM can enable end-to-end optimization. Extensive experimental results on KITTI and nuScenes datasets demonstrate that the proposed model sets a new state-of-the-art performance in registration accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Implicit_Correspondence_Learning_for_Image-to-Point_Cloud_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Implicit_Correspondence_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xinjun and Yang, Wenfei and Deng, Jiacheng and Cheng, Zhixin and Zhou, Xu and Zhang, Tianzhu},
    title     = {Implicit Correspondence Learning for Image-to-Point Cloud Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16922-16931}
}

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

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


title: Continuous Adverse Weather Removal via Degradation-Aware Distillation
authors: Xin Lu, Jie Xiao, Yurui Zhu, Xueyang Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28113-28123
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Continuous_Adverse_Weather_Removal_via_Degradation-Aware_Distillation_CVPR_2025_paper.html

abstract:
All-in-one models for adverse weather removal aim to process various degraded images using a single set of parameters, making them ideal for real-world scenarios. However, they encounter two main challenges: catastrophic forgetting and limited degradation awareness. The former causes the model to lose knowledge of previously learned scenarios, reducing its overall effectiveness. While the later hampers the model's ability to accurately identify and respond to specific types of degradation, limiting its performance across diverse adverse weather conditions. To address these issues, we introduce the Incremental Learning Adverse Weather Removal (ILAWR) framework, which uses a novel degradation-aware distillation strategy for continuous weather removal. Specifically, we first design a degradation-aware module that utilizes Fourier priors to capture a broad range of degradation features, effectively mitigating catastrophic forgetting in low-level visual tasks. Then, we implement multilateral distillation, which combines knowledge from multiple teacher models using an importance-guided aggregation approach. This enables the model to balance adaptation to new degradation types with the preservation of background details. Extensive experiments on both synthetic and real-world datasets confirm that ILAWR outperforms existing models across multiple benchmarks, proving its effectiveness in continuous adverse weather removal.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Continuous_Adverse_Weather_Removal_via_Degradation-Aware_Distillation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Xin and Xiao, Jie and Zhu, Yurui and Fu, Xueyang},
    title     = {Continuous Adverse Weather Removal via Degradation-Aware Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28113-28123}
}

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

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


title: Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models
authors: Kartik Thakral, Tamar Glaser, Tal Hassner, Mayank Vatsa, Richa Singh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9121-9130
affiliations: ["IIT Jodhpur", "Harman International", "Weir AI"]
cities: ["Jodhpur", "Unknown", "Unknown"]
countries: ["India", "Unknown", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Thakral_Fine-Grained_Erasure_in_Text-to-Image_Diffusion-based_Foundation_Models_CVPR_2025_paper.html

abstract:
Existing unlearning algorithms in text-to-image generative models often fail to preserve the knowledge of semantically related concepts when removing specific target concepts--a challenge known as adjacency. To address this, we propose FADE (Fine-grained Attenuation for Diffusion Erasure), introducing adjacency-aware unlearning in diffusion models. FADE comprises two components: (1) the Concept Neighborhood, which identifies an adjacency set of related concepts, and (2) Mesh Modules, employing a structured combination of Expungement, Adjacency, and Guidance loss components. These enable precise erasure of target concepts while preserving fidelity across related and unrelated concepts. Evaluated on datasets like Stanford Dogs, Oxford Flowers, CUB, I2P, Imagenette, and ImageNet-1k, FADE effectively removes target concepts with minimal impact on correlated concepts, achieving at least a 12% improvement in retention performance over state-of-the-art methods. Our code and models are available on the project page: iab-rubric/unlearning/FG-Un.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Thakral_Fine-Grained_Erasure_in_Text-to-Image_Diffusion-based_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Thakral_Fine-Grained_Erasure_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Thakral_2025_CVPR,
    author    = {Thakral, Kartik and Glaser, Tamar and Hassner, Tal and Vatsa, Mayank and Singh, Richa},
    title     = {Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9121-9130}
}

raw_affiliation:
[{"affiliation": "IIT Jodhpur", "city": "Jodhpur", "country": "India"}, {"affiliation": "Harman International", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Weir AI", "city": "Unknown", "country": "Unknown"}]


title: ILIAS: Instance-Level Image retrieval At Scale
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14777-14787
affiliations: ["VRG, FEE, Czech Technical University in Prague"]
cities: ["Prague"]
countries: ["Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kordopatis-Zilos_ILIAS_Instance-Level_Image_retrieval_At_Scale_CVPR_2025_paper.html

authors:
Giorgos Kordopatis-Zilos, Vladan Stojnić, Anna Manko, Pavel Suma, Nikolaos-Antonios Ypsilantis, Nikos Efthymiadis, Zakaria Laskar, Jiri Matas, Ondrej Chum, Giorgos Tolias

abstract:
This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retrieval techniques to recognize particular objects. The key benefits over existing datasets include large scale, domain diversity, accurate ground truth, and a performance that is far from saturated. ILIAS includes query and positive images for 1,000 object instances, manually collected to capture challenging conditions and diverse domains. Large-scale retrieval is conducted against 100 million distractor images from YFCC100M. To avoid false negatives without extra annotation effort, we include only query objects confirmed to have emerged after 2014, i.e. the compilation date of YFCC100M. An extensive benchmarking is performed with the following observations: i) models fine-tuned on specific domains, such as landmarks or products, excel in that domain but fail on ILIAS ii) learning a linear adaptation layer using multi-domain class supervision results in performance improvements, especially for vision-language models iii) local descriptors in retrieval re-ranking are still a key ingredient, especially in the presence of severe background clutter iv) the text-to-image performance of the vision-language foundation models is surprisingly close to the corresponding image-to-image case. website: https://vrg.fel.cvut.cz/ilias/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kordopatis-Zilos_ILIAS_Instance-Level_Image_retrieval_At_Scale_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kordopatis-Zilos_ILIAS_Instance-Level_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kordopatis-Zilos_2025_CVPR,
    author    = {Kordopatis-Zilos, Giorgos and Stojni\'c, Vladan and Manko, Anna and Suma, Pavel and Ypsilantis, Nikolaos-Antonios and Efthymiadis, Nikos and Laskar, Zakaria and Matas, Jiri and Chum, Ondrej and Tolias, Giorgos},
    title     = {ILIAS: Instance-Level Image retrieval At Scale},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14777-14787}
}

raw_affiliation:
[{"affiliation": "VRG, FEE, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}]


title: Exploiting Temporal State Space Sharing for Video Semantic Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24211-24221
cities: ["Singapore", "Singapore", "Tianjin", "Zurich", "Shanghai", "Guiyang"]
countries: ["Singapore", "Singapore", "China", "Switzerland", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hesham_Exploiting_Temporal_State_Space_Sharing_for_Video_Semantic_Segmentation_CVPR_2025_paper.html

authors:
Syed Ariff Syed Hesham, Yun Liu, Guolei Sun, Henghui Ding, Jing Yang, Ender Konukoglu, Xue Geng, Xudong Jiang

abstract:
Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes. Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements. To this end, we introduce a Temporal Video State Space Sharing (TV3S) architecture to leverage Mamba state space models for temporal feature sharing. Our model features a selective gating mechanism that efficiently propagates relevant information across video frames, eliminating the need for a memory-heavy feature pool. By processing spatial patches independently and incorporating shifted operation, TV3S supports highly parallel computation in both training and inference stages, which reduces the delay in sequential state space processing and improves the scalability for long video sequences. Moreover, TV3S incorporates information from prior frames during inference, achieving long-range temporal coherence and superior adaptability to extended sequences. Evaluations on the VSPW and Cityscapes datasets reveal that our approach outperforms current state-of-the-art methods, establishing a new standard for VSS with consistent results across long video sequences. By achieving a good balance between accuracy and efficiency, TV3S shows a significant advancement in spatiotemporal modeling, paving the way for efficient video analysis. The code is publicly available at https://github.com/Ashesham/TV3S.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hesham_Exploiting_Temporal_State_Space_Sharing_for_Video_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hesham_Exploiting_Temporal_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hesham_2025_CVPR,
    author    = {Hesham, Syed Ariff Syed and Liu, Yun and Sun, Guolei and Ding, Henghui and Yang, Jing and Konukoglu, Ender and Geng, Xue and Jiang, Xudong},
    title     = {Exploiting Temporal State Space Sharing for Video Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24211-24221}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute for Infocomm Research, A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "VCIP, CS, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "CVL, ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Guizhou University", "city": "Guiyang", "country": "China"}]

affiliations:
["Nanyang Technological University", "Institute for Infocomm Research, A*STAR", "VCIP, CS, Nankai University", "CVL, ETH Zurich", "Fudan University", "Guizhou University"]


title: DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models
authors: Yongqi Huang, Peng Ye, Chenyu Huang, Jianjian Cao, Lin Zhang, Baopu Li, Gang Yu, Tao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10056-10066
cities: ["Shanghai", "Hong Kong", "Shanghai", "Sunnyvale", "Beijing"]
countries: ["China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_DeRS_Towards_Extremely_Efficient_Upcycled_Mixture-of-Experts_Models_CVPR_2025_paper.html

abstract:
Upcycled Mixture-of-Experts (MoE) models have shown great potential in various tasks by converting the original Feed-Forward Network (FFN) layers in pre-trained dense models into MoE layers. However, these models still suffer from significant parameter inefficiency due to the introduction of multiple experts. In this work, we propose a novel DeRS (Decompose, Replace, and Synthesis) paradigm to overcome this shortcoming, which is motivated by our observations about the unique redundancy mechanisms of upcycled MoE experts. Specifically, DeRS decomposes the experts into one expert-shared base weight and multiple expert-specific delta weights, and subsequently represents these delta weights in lightweight forms. Our proposed DeRS paradigm can be applied to enhance parameter efficiency in two different scenarios, including: 1) DeRS Compression for inference stage, using sparsification or quantization to compress vanilla upcycled MoE models; and 2) DeRS Upcycling for training stage, employing lightweight sparse or low-rank matrixes to efficiently upcycle dense models into MoE models. Extensive experiments across three different tasks show that the proposed methods can achieve extreme parameter efficiency while maintaining the performance for both training and compression of upcycled MoE models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_DeRS_Towards_Extremely_Efficient_Upcycled_Mixture-of-Experts_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_DeRS_Towards_Extremely_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yongqi and Ye, Peng and Huang, Chenyu and Cao, Jianjian and Zhang, Lin and Li, Baopu and Yu, Gang and Chen, Tao},
    title     = {DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10056-10066}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Baidu USA", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "StepFun", "city": "Beijing", "country": "China"}]

affiliations:
["Fudan University", "The Chinese University of Hong Kong", "Shanghai AI Laboratory", "Baidu USA", "StepFun"]


authors: Haifeng Wu, Shuhang Gu, Lixin Duan, Wen Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11525-11535
affiliations: ["University of Electronic Science and T echnology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_GeoDepth_From_Point-to-Depth_to_Plane-to-Depth_Modeling_for_Self-Supervised_Monocular_Depth_CVPR_2025_paper.html

title:
GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation

abstract:
Self-supervised monocular depth estimation has long been treated as a point-wise prediction problem, where the depth of each pixel is usually estimated independently. However, artifacts are often observed in the estimated depth map, e.g., depth values for points located in the same region may jump dramatically. To address this issue, we propose a novel self-supervised monocular depth estimation framework called GeoDepth, where we explore the intrinsic geometric representation in 3D scenes for producing accurate and continuous depth maps. In particular, we model the complex 3D scene as a collection of planes with varying sizes, where each plane is characterized by a unique set of parameters, namely planar normal (indicating plane orientation) and planar offset (defining the perpendicular distance from the camera center to the plane). Under this modeling, points in the same plane are enforced to share a unique representation and their depth variations related only to pixel coordinates, thus this geometric relationship can be exploited to regularize the depth variations of these points. To this end, we design a structured plane generation module that introduces spatio-temporal geometric cues and the plane uniqueness principle to recover the correct scene plane representation. In addition, we develop a depth discontinuity module to identify depth discontinuity regions and subsequently optimize them. Our experiments on the KITTI and NYUv2 datasets demonstrate that GeoDepth achieves state-of-the-art performance, with additional tests on Make3D and ScanNet validating its generalization capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_GeoDepth_From_Point-to-Depth_to_Plane-to-Depth_Modeling_for_Self-Supervised_Monocular_Depth_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_GeoDepth_From_Point-to-Depth_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Haifeng and Gu, Shuhang and Duan, Lixin and Li, Wen},
    title     = {GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11525-11535}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16685-16694
cities: ["Ningbo", "Hangzhou", "Ningbo", "Hangzhou"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_SSHNet_Unsupervised_Cross-modal_Homography_Estimation_via_Problem_Reformulation_and_Split_CVPR_2025_paper.html

title:
SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization

authors:
Junchen Yu, Si-Yuan Cao, Runmin Zhang, Chenghao Zhang, Zhu Yu, Shujie Chen, Bailin Yang, Hui-Liang Shen

abstract:
We propose a novel unsupervised cross-modal homography estimation learning framework, named Split Supervised Homography estimation Network (SSHNet). SSHNet reformulates the unsupervised cross-modal homography estimation into two supervised sub-problems, each addressed by its specialized network: a homography estimation network and a modality transfer network. To realize stable training, we introduce an effective split optimization strategy to train each network separately within its respective sub-problem. We also formulate an extra homography feature space supervision to enhance feature consistency, further boosting the estimation accuracy. Moreover, we employ a simple yet effective distillation training technique to reduce model parameters and improve cross-domain generalization ability while maintaining comparable performance. The training stability of SSHNet enables its cooperation with various homography estimation architectures. Experiments reveal that the SSHNet using IHN as homography estimation network, namely SSHNet-IHN, outperforms previous unsupervised approaches by a significant margin. Even compared to supervised approaches MHN and LocalTrans, SSHNet-IHN achieves 47.4% and 85.8% mean average corner errors (MACEs) reduction on the challenging OPT-SAR dataset. Source code is available at https://github.com/Junchen-Yu/SSHNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_SSHNet_Unsupervised_Cross-modal_Homography_Estimation_via_Problem_Reformulation_and_Split_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_SSHNet_Unsupervised_Cross-modal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Junchen and Cao, Si-Yuan and Zhang, Runmin and Zhang, Chenghao and Yu, Zhu and Chen, Shujie and Yang, Bailin and Shen, Hui-Liang},
    title     = {SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16685-16694}
}

raw_affiliation:
[{"affiliation": "Ningbo Innovation Center, Zhejiang University", "city": "Ningbo", "country": "China"}, {"affiliation": "College of Information Science and Electronic Engineering, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "NingboTech University", "city": "Ningbo", "country": "China"}, {"affiliation": "Zhejiang Key Laboratory of Big Data and Future ECommerce Technology, Hangzhou, China", "city": "Hangzhou", "country": "China"}]

affiliations:
["Ningbo Innovation Center, Zhejiang University", "College of Information Science and Electronic Engineering, Zhejiang University", "NingboTech University", "Zhejiang Key Laboratory of Big Data and Future ECommerce Technology, Hangzhou, China"]


title: High-fidelity 3D Object Generation from Single Image with RGBN-Volume Gaussian Reconstruction Model
authors: Yiyang Shen, Kun Zhou, He Wang, Yin Yang, Tianjia Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21558-21569
cities: ["Hangzhou", "London", "Salt Lake City"]
countries: ["China", "UK", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_High-fidelity_3D_Object_Generation_from_Single_Image_with_RGBN-Volume_Gaussian_CVPR_2025_paper.html

abstract:
Recently single-view 3D generation via Gaussian splatting has emerged and developed quickly. They learn 3D Gaussians from 2D RGB images generated from pre-trained multi-view diffusion (MVD) models, and have shown a promising avenue for 3D generation through a single image. Despite the current progress, these methods still suffer from the inconsistency jointly caused by the geometric ambiguity in the 2D images, and the lack of structure of 3D Gaussians, leading to distorted and blurry 3D object generation. In this paper, we propose to fix these issues by GS-RGBN, a new RGBN-volume Gaussian Reconstruction Model designed to generate high-fidelity 3D objects from single-view images. Our key insight is a structured 3D representation can simultaneously mitigate the afore-mentioned two issues. To this end, we propose a novel hybrid Voxel-Gaussian representation, where a 3D voxel representation contains explicit 3D geometric information, eliminating the geometric ambiguity from 2D images. It also structures Gaussians during learning so that the optimization tends to find better local optima. Our 3D voxel representation is obtained by a fusion module that aligns RGB features and surface normal features, both of which can be estimated from 2D images. Extensive experiments demonstrate the superiority of our methods over prior works in terms of high-quality reconstruction results, robust generalization, and good efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_High-fidelity_3D_Object_Generation_from_Single_Image_with_RGBN-Volume_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_High-fidelity_3D_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Yiyang and Zhou, Kun and Wang, He and Yang, Yin and Shao, Tianjia},
    title     = {High-fidelity 3D Object Generation from Single Image with RGBN-Volume Gaussian Reconstruction Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21558-21569}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "AI Centre, University College London", "city": "London", "country": "UK"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}]

affiliations:
["State Key Lab of CAD&CG, Zhejiang University", "AI Centre, University College London", "University of Utah"]


title: Steepest Descent Density Control for Compact 3D Gaussian Splatting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26663-26672
affiliations: ["The University of Texas at Austin", "Meta Reality Labs"]
cities: ["Austin", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Steepest_Descent_Density_Control_for_Compact_3D_Gaussian_Splatting_CVPR_2025_paper.html

authors:
Peihao Wang, Yuehao Wang, Dilin Wang, Sreyas Mohan, Zhiwen Fan, Lemeng Wu, Ruisi Cai, Yu-Ying Yeh, Zhangyang Wang, Qiang Liu, Rakesh Ranjan

abstract:
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for efficient rendering and reconstruction. To optimize scene coverage and capture fine details, 3DGS employs a densification algorithm to generate additional points. However, this process often leads to redundant point clouds, resulting in excessive memory usage, slower performance, and substantial storage demands - posing significant challenges for deployment on resource-constrained devices. To address this limitation, we propose a theoretical framework that demystifies and improves density control in 3DGS. Our analysis reveals that splitting is crucial for escaping saddle points. Through an optimization-theoretic approach, we establish the necessary conditions for densification, determine the minimal number of offspring Gaussians, identify the optimal parameter update direction, and provide an analytical solution for normalizing off-spring opacity. Building on these insights, we introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud. SteepGS achieves a  50% reduction in Gaussian points without compromising rendering quality, significantly enhancing both efficiency and scalability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Steepest_Descent_Density_Control_for_Compact_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Steepest_Descent_Density_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Peihao and Wang, Yuehao and Wang, Dilin and Mohan, Sreyas and Fan, Zhiwen and Wu, Lemeng and Cai, Ruisi and Yeh, Yu-Ying and Wang, Zhangyang and Liu, Qiang and Ranjan, Rakesh},
    title     = {Steepest Descent Density Control for Compact 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26663-26672}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}]


title: Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24351-24363
affiliations: ["Rutgers University", "AWS AI Labs"]
cities: ["New Brunswick", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Optimal_Transport-Guided_Source-Free_Adaptation_for_Face_Anti-Spoofing_CVPR_2025_paper.html

authors:
Zhuowei Li, Tianchen Zhao, Xiang Xu, Zheng Zhang, Zhihua Li, Xuanbai Chen, Qin Zhang, Alessandro Bergamo, Anil K. Jain, Yifan Xing

abstract:
Developing a face anti-spoofing model that meets the security requirements of clients worldwide is challenging due to the domain gap between training datasets and the diverse end-user test data. Moreover, for security and privacy reasons, it is undesirable for clients to share a large amount of their face data with service providers. In this work, we introduce a novel method in which the face anti-spoofing model can be adapted by the client itself to a target domain at test time using only a small sample of data while keeping model parameters and training data inaccessible to the client. Specifically, we develop a prototype-based base model and an optimal transport-guided adaptor that enables adaptation in either a lightweight training or training-free fashion, without updating the base model's parameters. Furthermore, we propose geodesic mixup, an optimal transport-based synthesis method that generates augmented training data along the geodesic path between source prototypes and the target data distribution. This allows training a lightweight classifier to effectively adapt to target-specific characteristics while retaining essential knowledge learned from the source domain. In cross-domain and cross-attack settings, compared with recent methods, our method achieves average relative improvements of 19.17% in HTER and 8.58% in AUC, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Optimal_Transport-Guided_Source-Free_Adaptation_for_Face_Anti-Spoofing_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhuowei and Zhao, Tianchen and Xu, Xiang and Zhang, Zheng and Li, Zhihua and Chen, Xuanbai and Zhang, Qin and Bergamo, Alessandro and Jain, Anil K. and Xing, Yifan},
    title     = {Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24351-24363}
}

raw_affiliation:
[{"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}, {"affiliation": "AWS AI Labs", "city": "Seattle", "country": "USA"}]


title: USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting
authors: Kang Chen, Jiyuan Zhang, Zecheng Hao, Yajing Zheng, Tiejun Huang, Zhaofei Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16609-16618
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_USP-Gaussian_Unifying_Spike-based_Image_Reconstruction_Pose_Correction_and_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Spike camera, as an innovative type of neuromorphic camera that captures scenes with 0-1 bit stream at 40 kHz, is increasingly being employed for the novel view synthesis task building on the techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Previous spike-based approaches typically follow a three-stage pipeline: I. Spike-to-image reconstruction based on established algorithms. II. Camera poses estimation. III. Novel view synthesis. However, the cascading framework suffers from substantial cumulative errors, i.e., the quality of the initially reconstructed images will impact pose estimation, ultimately limiting the fidelity of the 3D reconstruction. To address this limitation, we propose a synergistic optimization framework USP-Gaussian, which unifies spike-to-image reconstruction, pose correction, and gaussian splatting into an end-to-end pipeline. Leveraging the multi-view consistency afforded by 3DGS and the motion capture capability of the spike camera, our framework enables iterative optimization between the spike-to-image reconstruction network and 3DGS. Experiments on synthetic datasets demonstrate that our method surpasses previous approaches by effectively eliminating cascading errors. Moreover, in real-world scenarios, our method achieves robust 3D reconstruction benefiting from the integration of pose optimization. Our code, data, and trained models are available at https://github.com/chenkang455/USP-Gaussian.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_USP-Gaussian_Unifying_Spike-based_Image_Reconstruction_Pose_Correction_and_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_USP-Gaussian_Unifying_Spike-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Kang and Zhang, Jiyuan and Hao, Zecheng and Zheng, Yajing and Huang, Tiejun and Yu, Zhaofei},
    title     = {USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16609-16618}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory for Multimedia Information Processing, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science, Peking University", "State Key Laboratory for Multimedia Information Processing, Peking University", "Institute for Artificial Intelligence, Peking University"]


title: Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild
authors: Junhyeong Cho, Kim Youwang, Hunmin Yang, Tae-Hyun Oh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22786-22798
cities: ["Daejeon", "Pohang", "Daejeon", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cho_Robust_3D_Shape_Reconstruction_in_Zero-Shot_from_a_Single_Image_CVPR_2025_paper.html

abstract:
Recent monocular 3D shape reconstruction methods have shown promising zero-shot results on object-segmented images without any occlusions. However, their effectiveness is significantly compromised in real-world conditions, due to imperfect object segmentation by off-the-shelf models and the prevalence of occlusions. To effectively address these issues, we propose a unified regression model that integrates segmentation and reconstruction, specifically designed for occlusion-aware 3D shape reconstruction. To facilitate its reconstruction in the wild, we also introduce a scalable data synthesis pipeline that simulates a wide range of variations in objects, occluders, and backgrounds. Training on our synthetic data enables the proposed model to achieve state-of-the-art zero-shot results on real-world images, using significantly fewer parameters than competing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cho_Robust_3D_Shape_Reconstruction_in_Zero-Shot_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cho_Robust_3D_Shape_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cho_2025_CVPR,
    author    = {Cho, Junhyeong and Youwang, Kim and Yang, Hunmin and Oh, Tae-Hyun},
    title     = {Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22786-22798}
}

raw_affiliation:
[{"affiliation": "ADD", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Department of Electrical Engineering, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Department of Mechanical Engineering, KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "School of Computing, KAIST", "city": "Daejeon", "country": "South Korea"}]

affiliations:
["ADD", "Department of Electrical Engineering, POSTECH", "Department of Mechanical Engineering, KAIST", "School of Computing, KAIST"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29352-29362
cities: ["Pittsburgh", "Boston", "Boston", "Mumbai", "Amethi"]
countries: ["USA", "USA", "USA", "India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_BOE-ViT_Boosting_Orientation_Estimation_with_Equivariance_in_Self-Supervised_3D_Subtomogram_CVPR_2025_paper.html

title:
BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment

authors:
Runmin Jiang, Jackson Daggett, Shriya Pingulkar, Yizhou Zhao, Priyanshu Dhingra, Daniel Brown, Qifeng Wu, Xiangrui Zeng, Xingjian Li, Min Xu

abstract:
Subtomogram alignment is a critical task in cryo-electron tomography (cryo-ET) analysis, essential for achieving high-resolution reconstructions of macromolecular complexes. However, learning effective positional representations remains challenging due to limited labels and high noise levels inherent in cryo-ET data. In this work, we address this challenge by proposing a self-supervised learning approach that leverages intrinsic geometric transformations as implicit supervisory signals, enabling robust representation learning despite data scarcity. We introduce BOE-ViT, the first Vision Transformer (ViT) framework for 3D subtomogram alignment. Recognizing that traditional ViTs lack equivariance and are therefore suboptimal for orientation estimation, we enhance the model with two innovative modules that introduce equivariance include 1) the Polyshift module for improved shift estimation and 2) Multi-Axis Rotation Encoding (MARE) for enhanced rotation estimation. Experimental results demonstrate that BOE-ViT significantly outperforms state-of-the-art methods. Notably, at SNR 0.01 dataset, our approach achieves a 77.3% reduction in rotation estimation error and a 62.5% reduction in translation estimation error, effectively overcoming the challenges in cryo-ET subtomogram alignment.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_BOE-ViT_Boosting_Orientation_Estimation_with_Equivariance_in_Self-Supervised_3D_Subtomogram_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_BOE-ViT_Boosting_Orientation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Runmin and Daggett, Jackson and Pingulkar, Shriya and Zhao, Yizhou and Dhingra, Priyanshu and Brown, Daniel and Wu, Qifeng and Zeng, Xiangrui and Li, Xingjian and Xu, Min},
    title     = {BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29352-29362}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Harvard Medical School", "city": "Boston", "country": "USA"}, {"affiliation": "Massachusetts General Hospital", "city": "Boston", "country": "USA"}, {"affiliation": "K. J. Somaiya College of Engineering", "city": "Mumbai", "country": "India"}, {"affiliation": "Rajiv Gandhi Institute of Petroleum Technology", "city": "Amethi", "country": "India"}]

affiliations:
["Carnegie Mellon University", "Harvard Medical School", "Massachusetts General Hospital", "K. J. Somaiya College of Engineering", "Rajiv Gandhi Institute of Petroleum Technology"]


title: Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13843-13853
cities: ["Wuhan", "Ann Arbor", "Yokohama"]
countries: ["China", "USA", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Holmes-VAU_Towards_Long-term_Video_Anomaly_Understanding_at_Any_Granularity_CVPR_2025_paper.html

authors:
Huaxin Zhang, Xiaohao Xu, Xiang Wang, Jialong Zuo, Xiaonan Huang, Changxin Gao, Shanjun Zhang, Li Yu, Nong Sang

abstract:
How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts?Traditional Video Anomaly Understanding (VAU) methods focus on frame-level anomaly prediction, often missing the interpretability of complex and diverse real-world anomalies. Recent multimodal approaches leverage visual and textual data but lack hierarchical annotations that capture both short-term and long-term anomalies.To address this challenge, we introduce HIVAU-70k, a large-scale benchmark for hierarchical video anomaly understanding across any granularity. We develop a semi-automated annotation engine that efficiently scales high-quality annotations by combining manual video segmentation with recursive free-text annotation using large language models (LLMs). This results in over 70,000 multi-granular annotations organized at clip-level, event-level, and video-level segments.For efficient anomaly detection in long videos, we propose the Anomaly-focused Temporal Sampler (ATS). ATS integrates an anomaly scorer with a density-aware sampler to adaptively select frames based on anomaly scores, ensuring that the multimodal LLM concentrates on anomaly-rich regions, which significantly enhances both efficiency and accuracy.Extensive experiments demonstrate that our hierarchical instruction data markedly improves anomaly comprehension. The integrated ATS and visual-language model outperform traditional methods in processing long videos.Our benchmark and model will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Holmes-VAU_Towards_Long-term_Video_Anomaly_Understanding_at_Any_Granularity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Holmes-VAU_Towards_Long-term_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Huaxin and Xu, Xiaohao and Wang, Xiang and Zuo, Jialong and Huang, Xiaonan and Gao, Changxin and Zhang, Shanjun and Yu, Li and Sang, Nong},
    title     = {Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13843-13853}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Kanagawa University", "city": "Yokohama", "country": "Japan"}]

affiliations:
["Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "University of Michigan", "Kanagawa University"]


title: Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30157-30166
affiliations: ["Johns Hopkins University", "UC Berkeley", "University of Florida", "UC Santa Cruz"]
cities: ["Baltimore", "Berkeley", "Gainesville", "Santa Cruz"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Adventurer_Optimizing_Vision_Mamba_Architecture_Designs_for_Efficiency_CVPR_2025_paper.html

authors:
Feng Wang, Timing Yang, Yaodong Yu, Sucheng Ren, Guoyizhe Wei, Angtian Wang, Wei Shao, Yuyin Zhou, Alan Yuille, Cihang Xie

abstract:
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representations. This modeling paradigm allows us to process images in a recurrent formulation with linear complexity relative to the sequence length, which can effectively address the memory and computation explosion issues posed by high-resolution and fine-grained images. In detail, we introduce two simple designs that seamlessly integrate image inputs into the causal inference framework: a global pooling token placed at the beginning of the sequence and a flipping operation between every two layers. Extensive empirical studies highlight that compared with the existing plain architectures such as DeiT and Vim, Adventurer offers an optimal efficiency-accuracy trade-off. For example, our Adventurer-Base attains a competitive test accuracy of 84.3% on the standard ImageNet-1k benchmark with 216 images/s training throughput, which is 3.8x and 6.2x faster than Vim and DeiT to achieve the same result. As Adventurer offers great computation and memory efficiency and allows scaling with linear complexity, we hope this architecture can benefit future explorations in modeling long sequences for high-resolution or fine-grained images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Adventurer_Optimizing_Vision_Mamba_Architecture_Designs_for_Efficiency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Adventurer_Optimizing_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Feng and Yang, Timing and Yu, Yaodong and Ren, Sucheng and Wei, Guoyizhe and Wang, Angtian and Shao, Wei and Zhou, Yuyin and Yuille, Alan and Xie, Cihang},
    title     = {Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30157-30166}
}

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


authors: Debora Caldarola, Pietro Cagnasso, Barbara Caputo, Marco Ciccone
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25187-25197
affiliations: ["Politecnico di Torino", "Vector Institute"]
cities: ["Turin", "Toronto"]
countries: ["Italy", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Caldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.html

title:
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning

abstract:
Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minima, negatively impacting generalization and robustness. Recent approaches use client-side sharpness-aware minimization (SAM) to encourage flatter minima, but the discrepancy between local and global loss landscapes often undermines their effectiveness, as optimizing for local sharpness does not ensure global flatness. This work introduces FedGloSS (Federated Global Server-side Sharpness), a novel FL approach that prioritizes the optimization of global sharpness on the server, using SAM. To reduce communication overhead, FedGloSS cleverly approximates sharpness using the previous global gradient, eliminating the need for additional client communication. Our extensive evaluations demonstrate that FedGloSS consistently reaches flatter minima and better performance compared to state-of-the-art FL methods across various federated vision benchmarks. Code available at https://github.com/pietrocagnasso/fedgloss.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Caldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Caldarola_Beyond_Local_Sharpness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Caldarola_2025_CVPR,
    author    = {Caldarola, Debora and Cagnasso, Pietro and Caputo, Barbara and Ciccone, Marco},
    title     = {Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25187-25197}
}

raw_affiliation:
[{"affiliation": "Politecnico di Torino", "city": "Turin", "country": "Italy"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}]


title: ALIEN: Implicit Neural Representations for Human Motion Prediction under Arbitrary Latency
authors: Dong Wei, Xiaoning Sun, Xizhan Gao, Shengxiang Hu, Huaijiang Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1861-1870
affiliations: ["Nanjing University of Science and Technology", "University of Jinan"]
cities: ["Nanjing", "Jinan"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_ALIEN_Implicit_Neural_Representations_for_Human_Motion_Prediction_under_Arbitrary_CVPR_2025_paper.html

abstract:
We investigate a new task in human motion prediction, which aims to forecast future body poses from historically observed sequences while accounting for arbitrary latency. This differs from existing works that assume an ideal scenario where future motions can be "instantaneously" predicted, thereby neglecting time delays caused by network transmission and algorithm execution. Addressing this task requires tackling two key challenges: The length of latency period can vary significantly across samples; the prediction model must be efficient. In this paper, we propose ALIEN, which treats the motion as a continuous function parameterized by a neural network, enabling predictions under any latency condition. By incorporating Mamba-like linear attention as a hyper-network and designing subsequent low-rank modulation, ALIEN efficiently learns a set of implicit neural representation weights from the observed motion to encode instance-specific information. Additionally, our model integrates the primary motion prediction task with an extra-designed variable-delay pose reconstruction task in a unified multi-task learning framework, enhancing its ability to capture richer motion patterns. Extensive experiments demonstrate that our approach outperforms state-of-the-art baselines adapted for our new task, while maintaining competitive performance in traditional prediction setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_ALIEN_Implicit_Neural_Representations_for_Human_Motion_Prediction_under_Arbitrary_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_ALIEN_Implicit_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Dong and Sun, Xiaoning and Gao, Xizhan and Hu, Shengxiang and Sun, Huaijiang},
    title     = {ALIEN: Implicit Neural Representations for Human Motion Prediction under Arbitrary Latency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1861-1870}
}

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


title: Parameterized Blur Kernel Prior Learning for Local Motion Deblurring
authors: Zhenxuan Fang, Fangfang Wu, Tao Huang, Le Dong, Weisheng Dong, Xin Li, Guangming Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23006-23015
affiliations: ["Xidian University", "State University of New York at Albany"]
cities: ["Xi'an", "Albany"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Parameterized_Blur_Kernel_Prior_Learning_for_Local_Motion_Deblurring_CVPR_2025_paper.html

abstract:
Unlike global motion blur, Local Motion Deblurring (LMD) presents a more complex challenge, as it requires precise restoration of blurry regions while preserving the sharpness of the background. Existing LMD methods rely on manually annotated blur masks and often overlook the blur kernel's characteristics, which are crucial for accurate restoration. To address these limitations, we propose a novel parameterized motion kernel modeling approach that defines the motion blur kernel with three key parameters: length, angle, and curvature. We then use networks to estimate these kernel parameters, significantly improving the accuracy of blur kernel estimation. To effectively learn the motion blur representation, we incorporate a shared memory bank that stores blur prior information. Additionally, we introduce a dual-branch deblurring network: one branch leverages Mamba to capture long-range dependencies, while the other uses a mask-guided CNN focused on refining the local blurry regions. By fully utilizing the estimated bur prior information, our approach greatly enhances deblurring outcomes. Experimental results show that our method achieves state-of-the-art performance both quantitatively and visually, with a substantial reduction in computational complexity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Parameterized_Blur_Kernel_Prior_Learning_for_Local_Motion_Deblurring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Parameterized_Blur_Kernel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Zhenxuan and Wu, Fangfang and Huang, Tao and Dong, Le and Dong, Weisheng and Li, Xin and Shi, Guangming},
    title     = {Parameterized Blur Kernel Prior Learning for Local Motion Deblurring},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23006-23015}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "State University of New York at Albany", "city": "Albany", "country": "USA"}]


title: QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11448-11460
cities: ["Boston", "Nanjing", "Melbourne", "Nanjing", "Shanghai", "Athens", "San Jose"]
countries: ["USA", "China", "Australia", "China", "China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_QuartDepth_Post-Training_Quantization_for_Real-Time_Depth_Estimation_on_the_Edge_CVPR_2025_paper.html

authors:
Xuan Shen, Weize Ma, Jing Liu, Changdi Yang, Rui Ding, Quanyi Wang, Henghui Ding, Wei Niu, Yanzhi Wang, Pu Zhao, Jun Lin, Jiuxiang Gu

abstract:
Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications. However, deploying accurate depth estimation models on resource-limited edge devices, especially Application-Specific Integrated Circuits (ASICs), is challenging due to the high computational and memory demands. Recent advancements in foundational depth estimation deliver impressive results but further amplify the difficulty of deployment on ASICs. To address this, we propose QuartDepth which adopts post-training quantization to quantize MDE models with hardware accelerations for ASICs. Our approach involves quantizing both weights and activations to 4-bit precision, reducing the model size and computation cost. To mitigate the performance degradation, we introduce activation polishing and compensation algorithm applied before and after activation quantization, as well as a weight reconstruction method for minimizing errors in weight quantization. Furthermore, we design a flexible and programmable hardware accelerator by supporting kernel fusion and customized instruction programmability, enhancing throughput and efficiency. Experimental results demonstrate that our framework achieves competitive accuracy while enabling fast inference and higher energy efficiency on ASICs, bridging the gap between high-performance depth estimation and practical edge-device applicability. Code: https://github.com/shawnricecake/quart-depth

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_QuartDepth_Post-Training_Quantization_for_Real-Time_Depth_Estimation_on_the_Edge_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_QuartDepth_Post-Training_Quantization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Xuan and Ma, Weize and Liu, Jing and Yang, Changdi and Ding, Rui and Wang, Quanyi and Ding, Henghui and Niu, Wei and Wang, Yanzhi and Zhao, Pu and Lin, Jun and Gu, Jiuxiang},
    title     = {QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11448-11460}
}

raw_affiliation:
[{"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Nanjing University of Information Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Georgia", "city": "Athens", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]

affiliations:
["Northeastern University", "Nanjing University", "Monash University", "Nanjing University of Information Science and Technology", "Fudan University", "University of Georgia", "Adobe Research"]


title: ReWind: Understanding Long Videos with Instructed Learnable Memory
authors: Anxhelo Diko, Tinghuai Wang, Wassim Swaileh, Shiyan Sun, Ioannis Patras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13734-13743
affiliations: ["La Sapienza University of Roma", "Huawei Helsinki Research Center"]
cities: ["Rome", "Helsinki"]
countries: ["Italy", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Diko_ReWind_Understanding_Long_Videos_with_Instructed_Learnable_Memory_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) are crucial for real-world applications that require understanding textual and visual information. However, existing VLMs face multiple challenges in processing long videos, including computational inefficiency, memory limitations, and difficulties maintaining coherent understanding across extended sequences. These issues stem partly from the quadratic scaling of self-attention w.r.t. number of tokens but also encompass broader challenges in temporal reasoning and information integration over long sequences. To address these challenges, we introduce ReWind, a novel two-stage framework for long video understanding. In the first stage, ReWind maintains a dynamic memory that stores and updates instruction-relevant visual information as the video unfolds.Memory updates leverage novel read and write mechanisms utilizing learnable queries and cross-attentions between memory contents and the input stream. This approach maintains low memory requirements as the cross-attention layers scale linearly w.r.t. number of tokens. In the second stage, the memory content guides the selection of a few relevant frames, represented at high spatial resolution, which are combined with the memory contents and fed into an LLM to generate the final answer. We empirically demonstrate ReWind's superiority in visual question answering (VQA) and temporal grounding tasks, surpassing previous methods on long video benchmarks. Notably, ReWind achieves a +13% score gain and a +12% accuracy improvement on the MovieChat-1K VQA dataset and an +8% mIoU increase on Charades-STA for temporal grounding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Diko_ReWind_Understanding_Long_Videos_with_Instructed_Learnable_Memory_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Diko_ReWind_Understanding_Long_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Diko_2025_CVPR,
    author    = {Diko, Anxhelo and Wang, Tinghuai and Swaileh, Wassim and Sun, Shiyan and Patras, Ioannis},
    title     = {ReWind: Understanding Long Videos with Instructed Learnable Memory},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13734-13743}
}

raw_affiliation:
[{"affiliation": "La Sapienza University of Roma", "city": "Rome", "country": "Italy"}, {"affiliation": "Huawei Helsinki Research Center", "city": "Helsinki", "country": "Finland"}]


title: Sufficient Invariant Learning for Distribution Shift
authors: Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4958-4967
affiliations: ["Yonsei University", "Purdue University", "CISPA Helmholtz Center for Information Security"]
cities: ["Seoul", "West Lafayette", "Saarbr\u00fccken"]
countries: ["South Korea", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Sufficient_Invariant_Learning_for_Distribution_Shift_CVPR_2025_paper.html

abstract:
Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning. While learning invariant features across environments is a popular approach, it often assumes that these features are fully observed in both training and test sets--a condition frequently violated in practice. When models rely on invariant features absent in the test set, their robustness in new environments can deteriorate. To tackle this problem, we introduce a novel learning principle called the Sufficient Invariant Learning (SIL) framework, which focuses on learning a sufficient subset of invariant features rather than relying on a single feature. After demonstrating the limitation of existing invariant learning methods, we propose a new algorithm, Adaptive Sharpness-aware Group Distributionally Robust Optimization (ASGDRO), to learn diverse invariant features by seeking common flat minima across the environments. We theoretically demonstrate that finding a common flat minima enables robust predictions based on diverse invariant features. Empirical evaluations on multiple datasets, including our new benchmark, confirm ASGDRO's robustness against distribution shifts, highlighting the limitations of existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Sufficient_Invariant_Learning_for_Distribution_Shift_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Sufficient_Invariant_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Taero and Park, Subeen and Lim, Sungjun and Jung, Yonghan and Muandet, Krikamol and Song, Kyungwoo},
    title     = {Sufficient Invariant Learning for Distribution Shift},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4958-4967}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "CISPA Helmholtz Center for Information Security", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: DirectTriGS: Triplane-based Gaussian Splatting Field Representation for 3D Generation
authors: Xiaoliang Ju, Hongsheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16229-16239
affiliations: ["CUHK MMLab", "CPII under InnoHK"]
cities: ["Hong Kong", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ju_DirectTriGS_Triplane-based_Gaussian_Splatting_Field_Representation_for_3D_Generation_CVPR_2025_paper.html

abstract:
We present DirectTriGS, a novel framework designed for 3D object generation with Gaussian Splatting (GS). GS-based rendering for 3D content has gained considerable attention recently. However, there has been limited exploration in directly generating 3D Gaussians compared to traditional generative modeling approaches. The main challenge lies in the complex data structure of GS represented by discrete point clouds with multiple channels.To overcome this challenge, we propose employing the triplane representation, which allows us to represent Gaussian Splatting as an image-like continuous field. This representation effectively encodes both the geometry and texture information, enabling smooth transformation back to Gaussian point clouds and rendering into images by a TriRenderer, with only 2D supervisions. The proposed TriRenderer is fully differentiable, so that the rendering loss can supervise both texture and geometry encoding. Furthermore, the triplane representation can be compressed using a Variational Autoencoder (VAE), which can subsequently be utilized in latent diffusion to generate 3D objects.The experiments demonstrate that the proposed generation framework can produce high-quality 3D object geometry and rendering results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ju_DirectTriGS_Triplane-based_Gaussian_Splatting_Field_Representation_for_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ju_DirectTriGS_Triplane-based_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ju_2025_CVPR,
    author    = {Ju, Xiaoliang and Li, Hongsheng},
    title     = {DirectTriGS: Triplane-based Gaussian Splatting Field Representation for 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16229-16239}
}

raw_affiliation:
[{"affiliation": "CUHK MMLab", "city": "Hong Kong", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]


title: Domain Generalization in CLIP via Learning with Diverse Text Prompts
authors: Changsong Wen, Zelin Peng, Yu Huang, Xiaokang Yang, Wei Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9559-9569
affiliations: ["MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_Domain_Generalization_in_CLIP_via_Learning_with_Diverse_Text_Prompts_CVPR_2025_paper.html

abstract:
Domain generalization (DG) aims to train a model on source domains that can generalize well to unseen domains. Recent advances in Vision-Language Models (VLMs), such as CLIP, exhibit remarkable generalization capabilities across a wide range of data distributions, benefiting tasks like DG. However, CLIP is pre-trained by aligning images with their descriptions, which inevitably captures domain-specific details. Moreover, adapting CLIP to source domains with limited feature diversity introduces bias. These limitations hinder the model's ability to generalize across domains. In this paper, we propose a new DG approach by learning with diverse text prompts. These text prompts incorporate varied contexts to imitate different domains, enabling DG model to learn domain-invariant features. The text prompts guide DG model learning in three aspects: feature suppression, which uses these prompts to identify domain-sensitive features and suppress them; feature consistency, which ensures the model's features are robust to domain variations imitated by the diverse prompts; and feature diversification, which diversifies features based on the prompts to mitigate bias. Experimental results show that our approach improves domain generalization performance on five datasets on the DomainBed benchmark, achieving state-of-the-art results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_Domain_Generalization_in_CLIP_via_Learning_with_Diverse_Text_Prompts_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wen_2025_CVPR,
    author    = {Wen, Changsong and Peng, Zelin and Huang, Yu and Yang, Xiaokang and Shen, Wei},
    title     = {Domain Generalization in CLIP via Learning with Diverse Text Prompts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9559-9569}
}

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


authors: Zilong Huang, Jun He, Junyan Ye, Lihan Jiang, Weijia Li, Yiping Chen, Ting Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26723-26733
cities: ["Guangzhou", "Shanghai", "Hefei"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Scene4U_Hierarchical_Layered_3D_Scene_Reconstruction_from_Single_Panoramic_Image_CVPR_2025_paper.html

title:
Scene4U: Hierarchical Layered 3D Scene Reconstruction from Single Panoramic Image for Your Immerse Exploration

abstract:
The reconstruction of immersive and realistic 3D scenes holds significant practical importance in various fields of computer vision and computer graphics. Typically, immersive and realistic scenes should be free from obstructions by dynamic objects, maintain global texture consistency, and allow for unrestricted exploration. The current mainstream methods for image-driven scene construction involves iteratively refining the initial image using a moving virtual camera to generate the scene. However, previous methods struggle with visual discontinuities due to global texture inconsistencies under varying camera poses, and they frequently exhibit scene voids caused by foreground-background occlusions. To this end, we propose a novel layered 3D scene reconstruction framework from panoramic image, named Scene4U. Specifically, Scene4U integrates an open-vocabulary segmentation model with a large language model to decompose a real panorama into multiple layers. Then, we employs a layered repair module based on diffusion model to restore occluded regions using visual cues and depth information, generating a hierarchical representation of the scene. The multi-layer panorama is then initialized as a 3D Gaussian Splatting representation, followed by layered optimization, which ultimately produces an immersive 3D scene with semantic and structural consistency that supports free exploration. Our Scene4U outperforms state-of-the-art method, improving by 24.24% in LPIPS and 24.40% in BRISQUE, while also achieving the fastest training speed. Additionally, to demonstrate the robustness of Scene4U and allow users to experience immersive scenes from various landmarks, we build WorldVista3D dataset for 3D scene reconstruction, which contains panoramic images of globally renowned sites. The implementation code and dataset will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Scene4U_Hierarchical_Layered_3D_Scene_Reconstruction_from_Single_Panoramic_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Scene4U_Hierarchical_Layered_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zilong and He, Jun and Ye, Junyan and Jiang, Lihan and Li, Weijia and Chen, Yiping and Han, Ting},
    title     = {Scene4U: Hierarchical Layered 3D Scene Reconstruction from Single Panoramic Image for Your Immerse Exploration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26723-26733}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Shanghai Artificial Intelligence Laboratory", "University of Science and Technology of China"]


title: Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters
authors: Zhiyang Guo, Jinxu Xiang, Kai Ma, Wengang Zhou, Houqiang Li, Ran Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10783-10792
cities: ["Hefei"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Make-It-Animatable_An_Efficient_Framework_for_Authoring_Animation-Ready_3D_Characters_CVPR_2025_paper.html

abstract:
3D characters are essential to modern creative industries, but making them animatable often demands extensive manual work in tasks like rigging and skinning. Existing automatic rigging tools face several limitations, including the necessity for manual annotations, rigid skeleton topologies, and limited generalization across diverse shapes and poses. An alternative approach generates animatable avatars pre-bound to a rigged template mesh. However, this method often lacks flexibility and is typically limited to realistic human shapes. To address these issues, we present Make-It-Animatable, a novel data-driven method to make any 3D humanoid model ready for character animation in less than one second, regardless of its shapes and poses. Our unified framework generates high-quality blend weights, bones, and pose transformations. By incorporating a particle-based shape autoencoder, our approach supports various 3D representations, including meshes and 3D Gaussian splats. Additionally, we employ a coarse-to-fine representation and a structure-aware modeling strategy to ensure both accuracy and robustness, even for characters with non-standard skeleton structures. We conducted extensive experiments to validate our framework's effectiveness. Compared to existing methods, our approach demonstrates significant improvements in both quality and speed. More demos and code are available at https://jasongzy.github.io/Make-It-Animatable/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Make-It-Animatable_An_Efficient_Framework_for_Authoring_Animation-Ready_3D_Characters_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Make-It-Animatable_An_Efficient_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Zhiyang and Xiang, Jinxu and Ma, Kai and Zhou, Wengang and Li, Houqiang and Zhang, Ran},
    title     = {Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10783-10792}
}

raw_affiliation:
[{"affiliation": "CAS Key Laboratory of Technology in GIPAS, EEIS Department, University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["CAS Key Laboratory of Technology in GIPAS, EEIS Department, University of Science and Technology of China"]


title: IterIS: Iterative Inference-Solving Alignment for LoRA Merging
authors: Hongxu Chen, Zhen Wang, Runshi Li, Bowei Zhu, Long Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4829-4838
affiliations: ["University of Science and Technology of China", "Hong Kong University of Science and Technology"]
cities: ["Hefei", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_IterIS_Iterative_Inference-Solving_Alignment_for_LoRA_Merging_CVPR_2025_paper.html

abstract:
Low-rank adaptations (LoRA) are widely used to fine-tune large models across various domains for specific downstream tasks. While task-specific LoRAs are often available, concerns about data privacy and intellectual property can restrict access to training data, limiting the acquisition of a multi-task model through gradient-based training. In response, LoRA merging presents an effective solution by combining multiple LoRAs into a unified adapter while maintaining data privacy. Prior works on LoRA merging primarily frame it as an optimization problem, yet these approaches face several limitations, including the rough assumption about input features utilized in optimization, massive sample requirements, and the unbalanced optimization objective. These limitations can significantly degrade performance. To address these, we propose a novel optimization-based method, named IterIS: 1) We formulate LoRA merging as an advanced optimization problem to mitigate the rough assumption. Additionally, we employ an iterative inference-solving framework in our algorithm. It can progressively refine the optimization objective for improved performance. 2) We introduce an efficient regularization term to reduce the need for massive sample requirements (requiring only 1-5% of the unlabeled samples compared to prior methods). 3) We utilize adaptive weights in the optimization objective to mitigate potential unbalances in LoRA merging process. Our method demonstrates significant improvements over multiple baselines and state-of-the-art methods in composing tasks for text-to-image diffusion, vision-language models, and large language models. Furthermore, our layer-wise algorithm can achieve convergence with minimal steps, ensuring efficiency in both memory and computation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_IterIS_Iterative_Inference-Solving_Alignment_for_LoRA_Merging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_IterIS_Iterative_Inference-Solving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Hongxu and Wang, Zhen and Li, Runshi and Zhu, Bowei and Chen, Long},
    title     = {IterIS: Iterative Inference-Solving Alignment for LoRA Merging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4829-4838}
}

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


title: ACAttack: Adaptive Cross Attacking RGB-T Tracker via Multi-Modal Response Decoupling
authors: Xinyu Xiang, Qinglong Yan, Hao Zhang, Jiayi Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22099-22108
raw_affiliation: [{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]
affiliations: ["Wuhan University"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_ACAttack_Adaptive_Cross_Attacking_RGB-T_Tracker_via_Multi-Modal_Response_Decoupling_CVPR_2025_paper.html

abstract:
The research on adversarial attacks against trackers primarily concentrates on the RGB modality, whereas the methodology for attacking RGB-T multi-modal trackers has seldom been explored so far. This work represents an innovative attempt to develop an adaptive cross attack framework via multi-modal response decoupling, generating multi-modal adversarial patches to evade RGB-T trackers. Specifically, a modal-aware adaptive attack strategy is introduced to weaken the modality with high common information contribution alternately and iteratively, achieving the modal decoupling attack. In order to perturb the judgment of the modal balance mechanism in the tracker, we design a modal disturbance loss to increase the distance of the response map of the single-modal adversarial samples in the tracker. Besides, we also propose a novel spatio-temporal joint attack loss to progressively deteriorate the tracker's perception of the target. Moreover, the design of the shared adversarial shape enables the generated multi-modal adversarial patches to be readily deployed in real-world scenarios, effectively reducing the interference of the patch posting process on the shape attack of the infrared adversarial layer. Extensive digital and physical domain experiments demonstrate the effectiveness of our multi-modal adversarial patch attack. Our code is available at https://github.com/Xinyu-Xiang/ACAttack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_ACAttack_Adaptive_Cross_Attacking_RGB-T_Tracker_via_Multi-Modal_Response_Decoupling_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xiang_2025_CVPR,
    author    = {Xiang, Xinyu and Yan, Qinglong and Zhang, Hao and Ma, Jiayi},
    title     = {ACAttack: Adaptive Cross Attacking RGB-T Tracker via Multi-Modal Response Decoupling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22099-22108}
}


title: DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24066-24076
affiliations: ["Microsoft", "Northeastern University"]
cities: ["Redmond", "Boston"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_DeCafNet_Delegate_and_Conquer_for_Efficient_Temporal_Grounding_in_Long_CVPR_2025_paper.html

authors:
Zijia Lu, A S M Iftekhar, Gaurav Mittal, Tianjian Meng, Xiawei Wang, Cheng Zhao, Rohith Kukkala, Ehsan Elhamifar, Mei Chen

abstract:
Long Video Temporal Grounding (LVTG) aims at identifying specific moments within lengthy videos based on user-provided text queries for effective content retrieval. The approach taken by existing methods of dividing video into clips and processing each clip via a full-scale expert encoder is challenging to scale due to prohibitive computational costs of processing a large number of clips in long videos. To address this issue, we introduce DeCafNet, an approach employing "delegate-and-conquer" strategy to achieve computation efficiency without sacrificing grounding performance. DeCafNet introduces a sidekick encoder that performs dense feature extraction over all video clips in a resource-efficient manner, while generating a saliency map to identify the most relevant clips for full processing by the expert encoder. To effectively leverage features from sidekick and expert encoders that exist at different temporal resolutions, we introduce DeCaf-Grounder, which unifies and refines them via query-aware temporal aggregation and multi-scale temporal refinement for accurate grounding. Experiments on two LTVG benchmark datasets demonstrate that DeCafNet reduces computation by up to 47% while still outperforming existing methods, establishing a new state-of-the-art for LTVG in terms of both efficiency and performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_DeCafNet_Delegate_and_Conquer_for_Efficient_Temporal_Grounding_in_Long_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_DeCafNet_Delegate_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Zijia and Iftekhar, A S M and Mittal, Gaurav and Meng, Tianjian and Wang, Xiawei and Zhao, Cheng and Kukkala, Rohith and Elhamifar, Ehsan and Chen, Mei},
    title     = {DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24066-24076}
}

raw_affiliation:
[{"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}]


authors: Shu Yang, Chengting Yu, Lei Liu, Hanzhi Ma, Aili Wang, Erping Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10025-10035
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Efficient_ANN-Guided_Distillation_Aligning_Rate-based_Features_of_Spiking_Neural_Networks_CVPR_2025_paper.html

title:
Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement

abstract:
Spiking Neural Networks (SNNs) have garnered considerable attention as a potential alternative to Artificial Neural Networks (ANNs). Recent studies have highlighted SNNs' potential on large-scale datasets. For SNN training, two main approaches exist: direct training and ANN-to-SNN (ANN2SNN) conversion. To fully leverage existing ANN models in guiding SNN learning, either direct ANN-to-SNN conversion or ANN-SNN distillation training can be employed. In this paper, we propose an ANN-SNN distillation framework from the ANN-to-SNN perspective, designed with a block-wise replacement strategy for ANN-guided learning. By generating intermediate hybrid models that progressively align SNN feature spaces to those of ANN through rate-based features, our framework naturally incorporates rate-based backpropagation as a training method. Our approach achieves results comparable to or better than state-of-the-art SNN distillation methods, showing both training and learning efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Efficient_ANN-Guided_Distillation_Aligning_Rate-based_Features_of_Spiking_Neural_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Efficient_ANN-Guided_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Shu and Yu, Chengting and Liu, Lei and Ma, Hanzhi and Wang, Aili and Li, Erping},
    title     = {Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10025-10035}
}

raw_affiliation:
[{"affiliation": "ZJU-UIUC Institute, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "College of Information Science and Electronic Engineering, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["ZJU-UIUC Institute, Zhejiang University", "College of Information Science and Electronic Engineering, Zhejiang University"]


title: PrEditor3D: Fast and Precise 3D Shape Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 640-649
affiliations: ["Technical University of Munich", "Snap Inc", "KAUST"]
cities: ["Munich", "Los Angeles", "Thuwal"]
countries: ["Germany", "USA", "Saudi Arabia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Erkoc_PrEditor3D_Fast_and_Precise_3D_Shape_Editing_CVPR_2025_paper.html

authors:
Ziya Erkoç, Can Gümeli, Chaoyang Wang, Matthias Nießner, Angela Dai, Peter Wonka, Hsin-Ying Lee, Peiye Zhuang

abstract:
We propose a training-free approach to 3D editing that enables the editing of a single shape and the reconstruction of a mesh within a few minutes. Leveraging 4-view images, user-guided text prompts, and rough 2D masks, our method produces an edited 3D mesh that aligns with the prompt. For this, our approach performs synchronized multi-view image editing in 2D. However, targeted regions to be edited are ambiguous due to projection from 3D to 2D. To ensure precise editing only in intended regions, we develop a 3D segmentation pipeline that detects edited areas in 3D space. Additionally, we introduce a merging algorithm to seamlessly integrate edited 3D regions with original input. Extensive experiments demonstrate the superiority of our method over previous approaches, enabling fast, high-quality editing while preserving unintended regions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Erkoc_PrEditor3D_Fast_and_Precise_3D_Shape_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Erkoc_PrEditor3D_Fast_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Erkoc_2025_CVPR,
    author    = {Erko\c{c}, Ziya and G\"umeli, Can and Wang, Chaoyang and Nie{\ss}ner, Matthias and Dai, Angela and Wonka, Peter and Lee, Hsin-Ying and Zhuang, Peiye},
    title     = {PrEditor3D: Fast and Precise 3D Shape Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {640-649}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Snap Inc", "city": "Los Angeles", "country": "USA"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}]


title: Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning
authors: Xiangtao Zhang, Sheng Li, Ao Li, Yipeng Liu, Fan Zhang, Ce Zhu, Le Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20632-20642
raw_affiliation: [{"affiliation": "SICE, UESTC", "city": "Chengdu", "country": "China"}]
affiliations: ["SICE, UESTC"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.html

abstract:
Heterogeneous Federated Learning (HFL) has received widespread attention due to its adaptability to different models and data. The HFL approach utilizing auxiliary models for knowledge transfer enhances flexibility.  However, existing frameworks face the challenges of aggregation bias and local overfitting. To address these issues, we propose FedSCE. It reduces the degree of freedom of update and improves generalization performance by limiting the specific layer of local model update to the local subspace. The subspace is dynamically updated to ensure coverage of the latest model update trajectory. Additionally, FedSCE evaluates client contributions based on the update distance of the auxiliary model in feature space and parameter space, achieving adaptive weighted aggregation. We validate our approach in both feature-skewed and label-skewed scenarios, demonstrating that on Office10, our method exceeds the best baseline by 3.87%. The code will be available at https://github.com/AVC2-UESTC/FedSCE.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Subspace_Constraint_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xiangtao and Li, Sheng and Li, Ao and Liu, Yipeng and Zhang, Fan and Zhu, Ce and Zhang, Le},
    title     = {Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20632-20642}
}


title: HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting
authors: Xinpeng Liu, Zeyi Huang, Fumio Okura, Yasuyuki Matsushita
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26714-26722
affiliations: ["The University of Osaka", "Microsoft Research Asia \u2013 Tokyo"]
cities: ["Osaka", "Tokyo"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_HoGS_Unified_Near_and_Far_Object_Reconstruction_via_Homogeneous_Gaussian_CVPR_2025_paper.html

abstract:
Novel view synthesis has demonstrated impressive progress recently, with 3D Gaussian splatting (3DGS) offering efficient training time and photorealistic real-time rendering. However, reliance on Cartesian coordinates limits 3DGS's performance on distant objects, which is important for reconstructing unbounded outdoor environments. We found that, despite its ultimate simplicity, using homogeneous coordinates, a concept on the projective geometry, for the 3DGS pipeline remarkably improves the rendering accuracies of distant objects. We therefore propose Homogeneous Gaussian Splatting (HoGS) incorporating homogeneous coordinates into the 3DGS framework, providing a unified representation for enhancing near and distant objects. HoGS effectively manages both expansive spatial positions and scales particularly in outdoor unbounded environments by adopting projective geometry principles. Experiments show that HoGS significantly enhances accuracy in reconstructing distant objects while maintaining high-quality rendering of nearby objects, along with fast training speed and real-time rendering capability. Our implementation will be released upon acceptance. Our implementations are available on our project page https://kh129.github.io/hogs/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_HoGS_Unified_Near_and_Far_Object_Reconstruction_via_Homogeneous_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_HoGS_Unified_Near_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xinpeng and Huang, Zeyi and Okura, Fumio and Matsushita, Yasuyuki},
    title     = {HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26714-26722}
}

raw_affiliation:
[{"affiliation": "The University of Osaka", "city": "Osaka", "country": "Japan"}, {"affiliation": "Microsoft Research Asia \u2013 Tokyo", "city": "Tokyo", "country": "Japan"}]


title: SmartEraser: Remove Anything from Images using Masked-Region Guidance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24452-24462
affiliations: ["University of Science and Technology of China", "Microsoft Research Asia"]
cities: ["Hefei", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_SmartEraser_Remove_Anything_from_Images_using_Masked-Region_Guidance_CVPR_2025_paper.html

authors:
Longtao Jiang, Zhendong Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Lei Shi, Dong Chen, Houqiang Li

abstract:
Object removal has so far been dominated by the mask-and-inpaint paradigm, where the masked region is excluded from the input, leaving models relying on unmasked areas to inpaint the missing region. However, this approach lacks contextual information for the masked area, often resulting in unstable performance. In this work, we introduce SmartEraser, built with a new removing paradigm called Masked-Region Guidance. This paradigm retains the masked region in the input, using it as guidance for the removal process. It offers several distinct advantages: (a) it guides the model to accurately identify the object to be removed, preventing its regeneration in the output; (b) since the user mask often extends beyond the object itself, it aids in preserving the surrounding context in the final result. Leveraging this new paradigm, we present Syn4Removal, a large-scale object removal dataset, where instance segmentation data is used to copy and paste objects onto images as removal targets, with the original images serving as ground truths. Experimental results demonstrate that SmartEraser significantly outperforms existing methods, achieving superior performance in object removal, especially in complex scenes with intricate compositions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_SmartEraser_Remove_Anything_from_Images_using_Masked-Region_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_SmartEraser_Remove_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Longtao and Wang, Zhendong and Bao, Jianmin and Zhou, Wengang and Chen, Dongdong and Shi, Lei and Chen, Dong and Li, Houqiang},
    title     = {SmartEraser: Remove Anything from Images using Masked-Region Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24452-24462}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4508-4517
affiliations: ["Tsinghua University", "Shenzhen University"]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_ComRoPE_Scalable_and_Robust_Rotary_Position_Embedding_Parameterized_by_Trainable_CVPR_2025_paper.html

title:
ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices

authors:
Hao Yu, Tangyu Jiang, Shuning Jia, Shannan Yan, Shunning Liu, Haolong Qian, Guanghao Li, Shuting Dong, Chun Yuan

abstract:
The Transformer architecture has revolutionized various fields since it was proposed, where positional encoding plays an essential role in effectively capturing sequential order and context. Therefore, Rotary Positional Encoding (RoPE) was proposed to alleviate these issues, which integrates positional information by rotating the embeddings in the attention mechanism. However, RoPE utilizes manually defined rotation matrices, a design choice that favors computational efficiency but limits the model's flexibility and adaptability. In this work, we propose ComRoPE, which generalizes RoPE by defining it in terms of trainable commuting angle matrices. Specifically, we demonstrate that pairwise commutativity of these matrices is essential for RoPE to achieve scalability and positional robustness. We formally define the RoPE Equation, which is an essential condition that ensures consistent performance with position offsets. Based on the theoretical analysis, we present two types of trainable commuting angle matrices as sufficient solutions to the RoPE equation, which significantly improve performance, surpassing the current state-of-the-art method by 1.6% at training resolution and 2.9% at higher resolution on the ImageNet-1K dataset. Furthermore, our framework shows versatility in generalizing to existing RoPE formulations and offering new insights for future positional encoding research. To ensure reproducibility, the source code and instructions are available at https://github.com/Longin-Yu/ComRoPE

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_ComRoPE_Scalable_and_Robust_Rotary_Position_Embedding_Parameterized_by_Trainable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_ComRoPE_Scalable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Hao and Jiang, Tangyu and Jia, Shuning and Yan, Shannan and Liu, Shunning and Qian, Haolong and Li, Guanghao and Dong, Shuting and Yuan, Chun},
    title     = {ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4508-4517}
}

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


title: Sample- and Parameter-Efficient Auto-Regressive Image Models
authors: Elad Amrani, Leonid Karlinsky, Alex Bronstein
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30127-30136
affiliations: ["Apple, Technion", "MIT-IBM Watson AI Lab", "Technion"]
cities: ["Haifa", "Cambridge", "Haifa"]
countries: ["Israel", "USA", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Amrani_Sample-_and_Parameter-Efficient_Auto-Regressive_Image_Models_CVPR_2025_paper.html

abstract:
We introduce XTRA, a vision model pre-trained with a novel auto-regressive objective that significantly enhances both sample and parameter efficiency compared to previous auto-regressive image models. Unlike contrastive or masked image modeling methods, which have not been demonstrated as having consistent scaling behavior on unbalanced internet data,auto-regressive vision models exhibit scalable and promising performance as model and dataset size increase. In contrast to standard auto-regressive models, XTRA employs a Block Causal Mask, where each Block represents k x k tokens rather than relying on a standard causal mask. By reconstructing pixel values block by block, XTRA captures higher-level structural patterns over larger image regions. Predicting on blocks allows the model to learn relationships across broader areas of pixels, enabling more abstract and semantically meaningful representations than traditional next-token prediction.This simple modification yields two key results. First, XTRA is sample-efficient. Despite being trained on 152x fewer samples (13.1M vs. 2B), XTRA ViT-H/14 surpasses the top-1 average accuracy of the previous state-of-the-art auto-regressive model across 15 diverse image recognition benchmarks. Second, XTRA is parameter-efficient. Compared to auto-regressive models trained on ImageNet-1k, XTRA ViT-B/16 outperforms in linear and attentive probing tasks, using 7-16x fewer parameters (85M vs. 1.36B/0.63B).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Amrani_Sample-_and_Parameter-Efficient_Auto-Regressive_Image_Models_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Amrani_2025_CVPR,
    author    = {Amrani, Elad and Karlinsky, Leonid and Bronstein, Alex},
    title     = {Sample- and Parameter-Efficient Auto-Regressive Image Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30127-30136}
}

raw_affiliation:
[{"affiliation": "Apple, Technion", "city": "Haifa", "country": "Israel"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}, {"affiliation": "Technion", "city": "Haifa", "country": "Israel"}]


title: Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment
authors: Chen Liu, Peike Li, Liying Yang, Dadong Wang, Lincheng Li, Xin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28922-28931
cities: ["Brisbane", "Hangzhou", "Unknown", "Canberra", "Macau"]
countries: ["Australia", "China", "Unknown", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Robust_Audio-Visual_Segmentation_via_Audio-Guided_Visual_Convergent_Alignment_CVPR_2025_paper.html

abstract:
Accurately localizing audible objects based on audio-visual cues is the core objective of audio-visual segmentation. Most previous methods emphasize spatial or temporal multi-modal modeling, yet overlook challenges from ambiguous audio-visual correspondences--such as nearby visually similar but acoustically different objects and frequent shifts in objects' sounding status. Consequently, they may struggle to reliably correlate audio and visual cues, leading to over- or under-segmentation. To address these limitations, we propose a novel framework with two primary components: an audio-guided modality alignment (AMA) module and an uncertainty estimation (UE) module. Instead of indiscriminately correlating audio-visual cues through a global attention mechanism, AMA performs audio-visual interactions within multiple groups and consolidates group features into compact representations based on their responsiveness to audio cues, effectively directing the model's attention to audio-relevant areas.  Leveraging contrastive learning, AMA further distinguishes sounding regions from silent areas by treating features with strong audio responses as positive samples and weaker responses as negatives. Additionally, UE integrates spatial and temporal information to identify high-uncertainty regions caused by frequent changes in sound state, reducing prediction errors by lowering confidence in these areas. Experimental results demonstrate that our approach achieves superior accuracy compared to existing state-of-the-art methods, particularly in challenging scenarios where traditional approaches struggle to maintain reliable segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Robust_Audio-Visual_Segmentation_via_Audio-Guided_Visual_Convergent_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Robust_Audio-Visual_Segmentation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Chen and Li, Peike and Yang, Liying and Wang, Dadong and Li, Lincheng and Yu, Xin},
    title     = {Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28922-28931}
}

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": "Canberra", "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"]


title: LOCORE: Image Re-ranking with Long-Context Sequence Modeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9580-9590
affiliations: ["Rice University", "VRG, FEE, Czech Technical University in Prague"]
cities: ["Houston", "Prague"]
countries: ["USA", "Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_LOCORE_Image_Re-ranking_with_Long-Context_Sequence_Modeling_CVPR_2025_paper.html

authors:
Zilin Xiao, Pavel Suma, Ayush Sachdeva, Hao-Jen Wang, Giorgos Kordopatis-Zilos, Giorgos Tolias, Vicente Ordonez

abstract:
We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery images and outputs similarity scores between the query and each gallery image. This model is used for image retrieval, where typically a first ranking is performed with an efficient similarity measure, and then a shortlist of top-ranked images is re-ranked based on a more fine-grained similarity measure. Compared to existing methods that perform pair-wise similarity estimation with local descriptors or list-wise re-ranking with global descriptors, LOCORE is the first method to perform list-wise re-ranking with local descriptors. To achieve this, we leverage efficient long-context sequence models to effectively capture the dependencies between query and gallery images at the local-descriptor level. During testing, we process long shortlists with a sliding window strategy that is tailored to overcome the context size limitations of sequence models. Our approach achieves superior performance compared with other re-rankers on established image retrieval benchmarks of landmarks (\mathcal R Oxf and \mathcal R Par), products (SOP), fashion items (In-Shop), and bird species (CUB-200) while having comparable latency to the pair-wise local descriptor re-rankers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_LOCORE_Image_Re-ranking_with_Long-Context_Sequence_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_LOCORE_Image_Re-ranking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Zilin and Suma, Pavel and Sachdeva, Ayush and Wang, Hao-Jen and Kordopatis-Zilos, Giorgos and Tolias, Giorgos and Ordonez, Vicente},
    title     = {LOCORE: Image Re-ranking with Long-Context Sequence Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9580-9590}
}

raw_affiliation:
[{"affiliation": "Rice University", "city": "Houston", "country": "USA"}, {"affiliation": "VRG, FEE, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}]


title: NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction
authors: Wenyuan Zhang, Emily Yue-ting Jia, Junsheng Zhou, Baorui Ma, Kanle Shi, Yu-Shen Liu, Zhizhong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11317-11327
cities: ["Beijing", "Beijing", "Detroit"]
countries: ["China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_NeRFPrior_Learning_Neural_Radiance_Field_as_a_Prior_for_Indoor_CVPR_2025_paper.html

abstract:
Recently, it has shown that priors are vital for neural implicit functions to reconstruct high-quality surfaces from multi-view RGB images. However, current priors require large-scale pre-training, and merely provide geometric clues without considering the importance of color. In this paper, we present NeRFPrior, which adopts a neural radiance field as a prior to learn signed distance fields using volume rendering for surface reconstruction. Our NeRF prior can provide both geometric and color clues, and also get trained fast under the same scene without additional data. Based on the NeRF prior, we are enabled to learn a signed distance function (SDF) by explicitly imposing a multi-view consistency constraint on each ray intersection for surface inference. Specifically, at each ray intersection, we use the density in the prior as a coarse geometry estimation, while using the color near the surface as a clue to check its visibility from another view angle. For the textureless areas where the multi-view consistency constraint does not work well, we further introduce a depth consistency loss with confidence weights to infer the SDF. Our experimental results outperform the state-of-the-art methods under the widely used benchmarks. The source code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_NeRFPrior_Learning_Neural_Radiance_Field_as_a_Prior_for_Indoor_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_NeRFPrior_Learning_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Wenyuan and Jia, Emily Yue-ting and Zhou, Junsheng and Ma, Baorui and Shi, Kanle and Liu, Yu-Shen and Han, Zhizhong},
    title     = {NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11317-11327}
}

raw_affiliation:
[{"affiliation": "School of Software, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science, Wayne State University", "city": "Detroit", "country": "USA"}]

affiliations:
["School of Software, Tsinghua University", "Kuaishou Technology", "Department of Computer Science, Wayne State University"]


title: BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning
authors: Hao Zhu, Yifei Zhang, Junhao Dong, Piotr Koniusz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25613-25622
affiliations: ["Data61 CSIRO", "Nanyang Technological University", "Australian National University"]
cities: ["Canberra", "Singapore", "Canberra"]
countries: ["Australia", "Singapore", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_BiLoRA_Almost-Orthogonal_Parameter_Spaces_for_Continual_Learning_CVPR_2025_paper.html

abstract:
Continual learning requires models to learn tasks sequentially while maintaining a delicate balance between stability (retaining knowledge of previous tasks) and plasticity (adapting to new tasks). A key challenge is preventing interference between tasks - where learning new tasks degrades performance on previously learned ones. Recent approaches have leveraged parameter-efficient fine-tuning (PEFT) methods, which adapt pre-trained models by injecting a small number of learnable parameters. However, existing PEFT-based continual learning methods like InfLoRA face fundamental limitations: they rely on complex optimization procedures to learn orthogonal task-specific spaces, and finding such spaces becomes increasingly difficult as tasks accumulate. We propose a novel bilinear reformulation that fundamentally reimagines task separation through fixed orthogonal bases. Our key insight is that by expanding the parameter space quadratically through two fixed bases, we can achieve "almost orthogonal" task subspaces probabilistically, eliminating the need for explicit interference elimination procedures. We provide theoretical guarantees that this approach reduces the probability of task interference from \bigO((k/d)^2) to \bigO((k/d^2)^2), ensuring reliable task separation without complex optimization. Through extensive experiments on ImageNet-R, CIFAR100, and DomainNet, we validate our theoretical bounds and demonstrate state-of-the-art performance with reduced parameter count.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_BiLoRA_Almost-Orthogonal_Parameter_Spaces_for_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_BiLoRA_Almost-Orthogonal_Parameter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Hao and Zhang, Yifei and Dong, Junhao and Koniusz, Piotr},
    title     = {BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25613-25622}
}

raw_affiliation:
[{"affiliation": "Data61 CSIRO", "city": "Canberra", "country": "Australia"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}]


authors: Chuhao Chen, Zhiyang Dou, Chen Wang, Yiming Huang, Anjun Chen, Qiao Feng, Jiatao Gu, Lingjie Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26545-26555
affiliations: ["University of Pennsylvania", "The University of Hong Kong", "Zhejiang University"]
cities: ["Philadelphia", "Hong Kong", "Hangzhou"]
countries: ["USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Vid2Sim_Generalizable_Video-based_Reconstruction_of_Appearance_Geometry_and_Physics_for_CVPR_2025_paper.html

title:
Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry and Physics for Mesh-free Simulation

abstract:
Faithfully reconstructing textured shapes and physical properties from videos presents an intriguing yet challenging problem. Significant efforts have been dedicated to advancing such a system identification problem in this area. Previous methods often rely on heavy optimization pipelines with a differentiable simulator and renderer to estimate physical parameters. However, these approaches frequently necessitate extensive hyperparameter tuning for each scene and involve a costly optimization process, which limits both their practicality and generalizability. In this work, we propose a novel framework, Vid2Sim, a generalizable video-based approach for recovering geometry and physical properties through a mesh-free reduced simulation based on Linear Blend Skinning (LBS), offering high computational efficiency and versatile representation capability. Specifically, Vid2Sim first reconstructs the observed configuration of the physical system from video using a feed-forward neural network trained to capture physical world knowledge. A lightweight optimization pipeline then refines the estimated appearance, geometry, and physical properties to closely align with video observations within just a few minutes. Additionally, after the reconstruction, Vid2Sim enables high-quality, mesh-free simulation with high efficiency. Extensive experiments demonstrate that our method achieves superior accuracy and efficiency in reconstructing geometry and physical properties from video data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Vid2Sim_Generalizable_Video-based_Reconstruction_of_Appearance_Geometry_and_Physics_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Vid2Sim_Generalizable_Video-based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Chuhao and Dou, Zhiyang and Wang, Chen and Huang, Yiming and Chen, Anjun and Feng, Qiao and Gu, Jiatao and Liu, Lingjie},
    title     = {Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry and Physics for Mesh-free Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26545-26555}
}

raw_affiliation:
[{"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


authors: Yue Cao, Yun Xing, Jie Zhang, Di Lin, Tianwei Zhang, Ivor Tsang, Yang Liu, Qing Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25050-25059
cities: ["Singapore", "Singapore", "Edmonton", "Tianjin", "Tianjin"]
countries: ["Singapore", "Singapore", "Canada", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_SceneTAP_Scene-Coherent_Typographic_Adversarial_Planner_against_Vision-Language_Models_in_Real-World_CVPR_2025_paper.html

title:
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments

abstract:
Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models' vulnerability to deliberately placed adversarial texts, such texts are often easily identifiable as anomalous. In this paper, we present the first approach to generate scene-coherent typographic adversarial attacks that mislead advanced LVLMs while maintaining visual naturalness through the capability of the LLM-based agent. Our approach addresses three critical questions: what adversarial text to generate, where to place it within the scene, and how to integrate it seamlessly. We propose a training-free, multi-modal LLM-driven scene-coherent typographic adversarial planning (SceneTAP) that employs a three-stage process: scene understanding, adversarial planning, and seamless integration. The SceneTAP utilizes chain-of-thought reasoning to comprehend the scene, formulate effective adversarial text, strategically plan its placement, and provide detailed instructions for natural integration within the image. This is followed by a scene-coherent TextDiffuser that executes the attack using a local diffusion mechanism. We extend our method to real-world scenarios by printing and placing generated patches in physical environments, demonstrating its practical implications. Extensive experiments show that our scene-coherent adversarial text successfully misleads state-of-the-art LVLMs, including ChatGPT-4o, even after capturing new images of physical setups. Our evaluations demonstrate a significant increase in attack success rates while maintaining visual naturalness and contextual appropriateness. This work highlights vulnerabilities in current vision-language models to sophisticated, scene-coherent adversarial attacks and provides insights into potential defense mechanisms. We release our code at https://github.com/tsingqguo/scenetap.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_SceneTAP_Scene-Coherent_Typographic_Adversarial_Planner_against_Vision-Language_Models_in_Real-World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_SceneTAP_Scene-Coherent_Typographic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Yue and Xing, Yun and Zhang, Jie and Lin, Di and Zhang, Tianwei and Tsang, Ivor and Liu, Yang and Guo, Qing},
    title     = {SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25050-25059}
}

raw_affiliation:
[{"affiliation": "CFAR and IHPC, Agency for Science, Technology and Research (A*STAR)", "city": "Singapore", "country": "Singapore"}, {"affiliation": "College of Computing and Data Science, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Alberta", "city": "Edmonton", "country": "Canada"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "CS, Nankai University", "city": "Tianjin", "country": "China"}]

affiliations:
["CFAR and IHPC, Agency for Science, Technology and Research (A*STAR)", "College of Computing and Data Science, Nanyang Technological University", "University of Alberta", "Tianjin University", "CS, Nankai University"]


title: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient
authors: Zigeng Chen, Xinyin Ma, Gongfan Fang, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23334-23344
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/Chen_Collaborative_Decoding_Makes_Visual_Auto-Regressive_Modeling_Efficient_CVPR_2025_paper.html

abstract:
In the rapidly advancing field of image generation, *Visual Auto-Regressive* (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. This paradigm offers substantial improvements in efficiency, scalability, and zero-shot generalization. Yet, the inherently coarse-to-fine nature of VAR introduces a prolonged token sequence, leading to prohibitive memory consumption and computational redundancies. To overcome these bottlenecks, we propose *Collaborative Decoding* (CoDe), a novel decoding strategy tailored to the VAR framework. CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales. Based on these insights, we partition the multi-scale inference process into a seamless collaboration between a large model and a small model. The large model serves as the 'drafter', specializing in generating low-frequency content at smaller scales, while the smaller model serves as the 'refiner', solely focusing on predicting high-frequency details at larger scales. This collaboration yields remarkable efficiency with minimal impact on quality: CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98. When drafting steps are further decreased, CoDe can achieve an impressive 2.9x acceleration, reaching over 41 images/s at 256x256 resolution on a single NVIDIA 4090 GPU, while preserving a commendable FID of 2.27.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Collaborative_Decoding_Makes_Visual_Auto-Regressive_Modeling_Efficient_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Collaborative_Decoding_Makes_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zigeng and Ma, Xinyin and Fang, Gongfan and Wang, Xinchao},
    title     = {Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23334-23344}
}


title: AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis
authors: Khiem Vuong, Anurag Ghosh, Deva Ramanan, Srinivasa Narasimhan, Shubham Tulsiani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21674-21684
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vuong_AerialMegaDepth_Learning_Aerial-Ground_Reconstruction_and_View_Synthesis_CVPR_2025_paper.html

abstract:
We explore the task of geometric reconstruction of images captured from a mixture of ground and aerial views. Current state-of-the-art learning-based approaches fail to handle the extreme viewpoint variation between aerial-ground image pairs. Our hypothesis is that the lack of high-quality, co-registered aerial-ground datasets for training is a key reason for this failure. Such data is difficult to assemble precisely because it is difficult to reconstruct in a scalable way. To overcome this challenge, we propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes (e.g., Google Earth) with real, ground-level crowd-sourced images (e.g., MegaDepth). The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images where mesh-based renderings lack sufficient detail, effectively bridging the domain gap between real images and pseudo-synthetic renderings. Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks. For example, we observe that baseline DUSt3R localizes fewer than 5% of aerial-ground pairs within 5 degrees of camera rotation error, while fine-tuning with our data raises accuracy to nearly 56%, addressing a major failure point in handling large viewpoint changes. Beyond camera estimation and scene reconstruction, our dataset also improves performance on downstream tasks like novel-view synthesis in challenging aerial-ground scenarios, demonstrating the practical value of our approach in real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vuong_AerialMegaDepth_Learning_Aerial-Ground_Reconstruction_and_View_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vuong_AerialMegaDepth_Learning_Aerial-Ground_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vuong_2025_CVPR,
    author    = {Vuong, Khiem and Ghosh, Anurag and Ramanan, Deva and Narasimhan, Srinivasa and Tulsiani, Shubham},
    title     = {AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21674-21684}
}


title: Towards Training-free Anomaly Detection with Vision and Language Foundation Models
authors: Jinjin Zhang, Guodong Wang, Yizhou Jin, Di Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15204-15213
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Towards_Training-free_Anomaly_Detection_with_Vision_and_Language_Foundation_Models_CVPR_2025_paper.html

abstract:
Anomaly detection is valuable for real-world applications, such as industrial quality inspection. However, most approaches focus on detecting local structural anomalies while neglecting compositional anomalies incorporating logical constraints. In this paper, we introduce LogSAD, a novel multi-modal framework that requires no training for both Logical and Structural Anomaly Detection. First, we propose a match-of-thought architecture that employs advanced large multi-modal models (i.e. GPT-4V) to generate matching proposals, formulating interests and compositional rules of thought for anomaly detection. Second, we elaborate on multi-granularity anomaly detection, consisting of patch tokens, sets of interests, and composition matching with vision and language foundation models. Subsequently, we present a calibration module to align anomaly scores from different detectors, followed by integration strategies for the final decision. Consequently, our approach addresses both logical and structural anomaly detection within a unified framework and achieves state-of-the-art results without the need for training, even when compared to supervised approaches, highlighting its robustness and effectiveness. Code is available at https://github.com/zhang0jhon/LogSAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Towards_Training-free_Anomaly_Detection_with_Vision_and_Language_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Towards_Training-free_Anomaly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jinjin and Wang, Guodong and Jin, Yizhou and Huang, Di},
    title     = {Towards Training-free Anomaly Detection with Vision and Language Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15204-15213}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Complex and Critical Software Environment, Beihang University, Beijing 100191, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University, Beijing 100191, China", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Complex and Critical Software Environment, Beihang University, Beijing 100191, China", "School of Computer Science and Engineering, Beihang University, Beijing 100191, China"]


title: LiVOS: Light Video Object Segmentation with Gated Linear Matching
authors: Qin Liu, Jianfeng Wang, Zhengyuan Yang, Linjie Li, Kevin Lin, Marc Niethammer, Lijuan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8668-8678
affiliations: ["UNC Chapel Hill", "Microsoft"]
cities: ["Chapel Hill", "Redmond"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_LiVOS_Light_Video_Object_Segmentation_with_Gated_Linear_Matching_CVPR_2025_paper.html

abstract:
Semi-supervised video object segmentation (VOS) has been largely driven by space-time memory (STM) networks, which store past frame features in a spatiotemporal memory to segment the current frame via softmax attention. However, STM networks face memory limitations due to the quadratic complexity of softmax matching, restricting their applicability as video length and resolution increase. To address this, we propose LiVOS, a lightweight memory network that employs linear matching via linear attention, reformulating memory matching into a recurrent process that reduces the quadratic attention matrix to a constant-size, spatiotemporal-agnostic 2D state. To enhance selectivity, we introduce gated linear matching, where a data-dependent gate matrix is multiplied with the state matrix to control what information to retain or discard. Experiments on diverse benchmarks demonstrated the effectiveness of our method. It achieved 64.8 J&F on MOSE and 85.1 J&F on DAVIS, surpassing all non-STM methods and narrowing the gap with STM-based approaches. For longer and higher-resolution videos, it matched STM-based methods with 53% less GPU memory and supports 4096p inference on a 32G consumer-grade GPU--a previously cost-prohibitive capability--opening the door for long and high-resolution video foundation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_LiVOS_Light_Video_Object_Segmentation_with_Gated_Linear_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_LiVOS_Light_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Qin and Wang, Jianfeng and Yang, Zhengyuan and Li, Linjie and Lin, Kevin and Niethammer, Marc and Wang, Lijuan},
    title     = {LiVOS: Light Video Object Segmentation with Gated Linear Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8668-8678}
}

raw_affiliation:
[{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]


title: Dynamic Content Prediction with Motion-aware Priors for Blind Face Video Restoration
authors: Lianxin Xie, Bingbing Zheng, Si Wu, Hau San Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17821-17830
cities: ["Guangzhou", "Guangzhou", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Dynamic_Content_Prediction_with_Motion-aware_Priors_for_Blind_Face_Video_CVPR_2025_paper.html

abstract:
Blind Face Video Restoration (BFVR) focuses on reconstructing high-quality facial image sequences from degraded video inputs. The main challenge is address unknown degradations, while maintaining temporal consistency across frames. Current blind face restoration methods are primarily designed for images, and directly applying these approaches to BFVR will encounter a significant drop in restoration performance. In this work, we proposed Dynamic Content Prediction with Motion-aware Priors, referred to as DCP-MP. We develop a motion-aware semantic dictionary by encoding the semantic information of high-quality videos into discrete elements, and capturing the motion information in terms of element relationships, which are derived from the dynamic temporal changes within videos. For the purpose of utilizing dictionary to represent the degraded video, we train a temporal-aware element predictor, conditioned on degraded content, to learn the prediction of discrete elements in dictionary. The predicted elements will be refined, conditioned on motion information captured by the motion-aware semantic dictionary, to enhance temporal coherence. To alleviate deviation from the original structure information, we propose a conditional structure feature correction module that corrects the features flowing from the encoder to the generator. Through extensive experiments, we validate the effectiveness of our design components and demonstrate the superior performance of DCP-MP in synthesizing high-quality video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Dynamic_Content_Prediction_with_Motion-aware_Priors_for_Blind_Face_Video_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Lianxin and Zheng, Bingbing and Wu, Si and Wong, Hau San},
    title     = {Dynamic Content Prediction with Motion-aware Priors for Blind Face Video Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17821-17830}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Institute of Super Robotics(Huangpu)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, South China University of Technology", "Institute of Super Robotics(Huangpu)", "Department of Computer Science, City University of Hong Kong"]


title: Polarized Color Screen Matting
authors: Kenji Enomoto, Scott Cohen, Brian Price, TJ Rhodes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 391-399
raw_affiliation: [{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]
affiliations: ["Adobe Research"]
cities: ["San Jose"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Enomoto_Polarized_Color_Screen_Matting_CVPR_2025_paper.html

abstract:
This paper considers the long-standing problem of extracting alpha mattes from video using a known background. While various color-based or polarization-based approaches have been studied in past decades, the problem remains ill-posed because the solutions solely rely on either color or polarization. We introduce Polarized Color Screen Matting, a single-shot, per-pixel matting theory for alpha matte and foreground color recovery using both color and polarization cues. Through a theoretical analysis of our diffuse-specular polarimetric compositing equation, we derive practical closed-form matting methods with their solvability conditions. Our theory concludes that an alpha matte can be extracted without manual corrections using off-the-shelf equipment such as an LCD monitor, polarization camera, and unpolarized lights with calibrated color. Experiments on synthetic and real-world datasets verify the validity of our theory and show the capability of our matting methods on real videos with quantitative and qualitative comparisons to color-based and polarization-based matting methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Enomoto_Polarized_Color_Screen_Matting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Enomoto_Polarized_Color_Screen_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Enomoto_2025_CVPR,
    author    = {Enomoto, Kenji and Cohen, Scott and Price, Brian and Rhodes, TJ},
    title     = {Polarized Color Screen Matting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {391-399}
}


title: Visual Representation Learning through Causal Intervention for Controllable Image Editing
authors: Shanshan Huang, Haoxuan Li, Chunyuan Zheng, Lei Wang, Guorui Liao, Zhili Gong, Huayi Yang, Li Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23484-23493
cities: ["Chongqing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Visual_Representation_Learning_through_Causal_Intervention_for_Controllable_Image_Editing_CVPR_2025_paper.html

abstract:
A key challenge for controllable image editing is that visual attributes with semantic meanings are not always independent, resulting in spurious correlations in model training. However, most existing methods ignore such issues, leading to biased causal visual representation learning and unintended changes to unrelated regions or attributes in the edited images. To bridge this gap, we propose a diffusion-based causal visual representation learning framework called CIDiffuser to capture causal representations of visual attributes based on structural causal models to address the spurious correlation. Specifically, we first decompose the image representation into a high-level semantic representation for core attributes of the image and a low-level stochastic representation for other random or less structured aspects, with the former extracted by a semantic encoder and the latter derived via a stochastic encoder. We then introduce a causal effect learning module to capture the direct causal effect, that is, the difference of potential outcomes before and after intervening on the visual attributes.  In addition, a diffusion-based learning strategy is designed to optimize the representation learning process. Empirical evaluations on two benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, enabling highly controllable image editing by modifying learned visual representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Visual_Representation_Learning_through_Causal_Intervention_for_Controllable_Image_Editing_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Shanshan and Li, Haoxuan and Zheng, Chunyuan and Wang, Lei and Liao, Guorui and Gong, Zhili and Yang, Huayi and Liu, Li},
    title     = {Visual Representation Learning through Causal Intervention for Controllable Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23484-23493}
}

raw_affiliation:
[{"affiliation": "School of Big Data & Software Engineering, Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "Center for Data Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Mathematical Sciences, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Big Data & Software Engineering, Chongqing University", "Center for Data Science, Peking University", "School of Mathematical Sciences, Peking University"]


authors: Bingda Tang, Boyang Zheng, Sayak Paul, Saining Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28586-28595
affiliations: ["New York University", "Hugging Face"]
cities: ["New York", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Exploring_the_Deep_Fusion_of_Large_Language_Models_and_Diffusion_CVPR_2025_paper.html

title:
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis

abstract:
This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image synthesis---specifically, the deep fusion of large language models (LLMs) with diffusion transformers (DiTs) for multimodal generation. Previous studies mainly focused on overall system performance rather than detailed comparisons with alternative methods, and key design details and training recipes were often left undisclosed. These gaps create uncertainty about the real potential of this approach. To fill these gaps, we conduct an empirical study on text-to-image generation, performing controlled comparisons with established baselines, analyzing important design choices, and providing a clear, reproducible recipe for training at scale. We hope this work offers meaningful data points and practical guidelines for future research in multimodal generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Exploring_the_Deep_Fusion_of_Large_Language_Models_and_Diffusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Bingda and Zheng, Boyang and Paul, Sayak and Xie, Saining},
    title     = {Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28586-28595}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Hugging Face", "city": "New York", "country": "USA"}]


title: A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation
authors: Andrew Z. Wang, Songwei Ge, Tero Karras, Ming-Yu Liu, Yogesh Balaji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28575-28585
affiliations: ["University of Washington", "University of Maryland", "NVIDIA"]
cities: ["Seattle", "College Park", "Santa Clara"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_A_Comprehensive_Study_of_Decoder-Only_LLMs_for_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Both text-to-image generation and large language models (LLMs) have made significant advancements. However, many text-to-image models still employ the somewhat outdated T5 and CLIP as their text encoders. In this work, we investigate the effectiveness of using modern decoder-only LLMs as text encoders for text-to-image diffusion models. We build a standardized training and evaluation pipeline that allows us to isolate and evaluate the effect of different text embeddings. We train a total of 27 text-to-image models with 12 different text encoders to analyze the critical aspects of LLMs that could impact text-to-image generation, including the approaches to extract embeddings, different LLMs variants, and model sizes. Our experiments reveal that the de facto way of using last-layer embeddings as conditioning leads to inferior performance. Instead, we explore embeddings from various layers and find that using layer-normalized averaging across all layers significantly improves alignment with complex prompts. Most LLMs with this conditioning outperform the baseline T5 model, showing enhanced performance in advanced visio-linguistic reasoning skills.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_A_Comprehensive_Study_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Andrew Z. and Ge, Songwei and Karras, Tero and Liu, Ming-Yu and Balaji, Yogesh},
    title     = {A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28575-28585}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18411-18423
affiliations: ["Ant Group", "HKUST", "CUHK", "Shanghai Jiao Tong Univeristy"]
cities: ["Hangzhou", "Hong Kong", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Exploring_Sparse_MoE_in_GANs_for_Text-conditioned_Image_Synthesis_CVPR_2025_paper.html

authors:
Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng, Yinghao Xu, Zifan Shi, Yifei Zhang, Qifeng Chen, Yujun Shen

abstract:
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling out of grace with the task of text-conditioned image synthesis. Sparsely activated mixture-of-experts (MoE) has recently been demonstrated as a valid solution to training large-scale models with limited resources. Inspired by this, we present Aurora, a GAN-based text-to-image generator that employs a collection of experts to learn feature processing, together with a sparse router to adaptively select the most suitable expert for each feature point. We adopt a two-stage training strategy, which first learns a base model at 64x64 resolution followed by an upsampler to produce 512x512 images. Trained with only public data, our approach encouragingly closes the performance gap between GANs and industry-level diffusion models, maintaining a fast inference speed. We release the code and checkpoints \href https://github.com/zhujiapeng/Aurora  here  to facilitate the community for further development.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Exploring_Sparse_MoE_in_GANs_for_Text-conditioned_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Exploring_Sparse_MoE_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Jiapeng and Yang, Ceyuan and Zheng, Kecheng and Xu, Yinghao and Shi, Zifan and Zhang, Yifei and Chen, Qifeng and Shen, Yujun},
    title     = {Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18411-18423}
}

raw_affiliation:
[{"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Jiao Tong Univeristy", "city": "Shanghai", "country": "China"}]


title: Deformable Radial Kernel Splatting
authors: Yi-Hua Huang, Ming-Xian Lin, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21513-21523
affiliations: ["The University of Hong Kong", "V AST"]
cities: ["Hong Kong", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Deformable_Radial_Kernel_Splatting_CVPR_2025_paper.html

abstract:
Recently, Gaussian splatting has emerged as a robust technique for representing 3D scenes, enabling real-time rasterization and high-fidelity rendering. However, Gaussians' inherent radial symmetry and smoothness constraints limit their ability to represent complex shapes, often requiring thousands of primitives to approximate detailed geometry. We introduce Deformable Radial Kernel (DRK), which extends Gaussian splatting into a more general and flexible framework. Through learnable radial bases with adjustable angles and scales, DRK efficiently models diverse shape primitives while enabling precise control over edge sharpness and boundary curvature. iven DRK's planar nature, we further develop accurate ray-primitive intersection computation for depth sorting and introduce efficient kernel culling strategies for improved rasterization efficiency. Extensive experiments demonstrate that DRK outperforms existing methods in both representation efficiency and rendering quality, achieving state-of-the-art performance while dramatically reducing primitive count.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Deformable_Radial_Kernel_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Deformable_Radial_Kernel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yi-Hua and Lin, Ming-Xian and Sun, Yang-Tian and Yang, Ziyi and Lyu, Xiaoyang and Cao, Yan-Pei and Qi, Xiaojuan},
    title     = {Deformable Radial Kernel Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21513-21523}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "V AST", "city": "Unknown", "country": "Unknown"}]


title: GOAL: Global-local Object Alignment Learning
authors: Hyungyu Choi, Young Kyun Jang, Chanho Eom
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4070-4079
affiliations: ["Chung-Ang University", "Meta AI"]
cities: ["Seoul", "Menlo Park"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_GOAL_Global-local_Object_Alignment_Learning_CVPR_2025_paper.html

abstract:
Vision-language models like CLIP have shown impressive capabilities in aligning images and text, but they often struggle with lengthy and detailed text descriptions because of their training focus on short and concise captions. We present GOAL (Global-local Object Alignment Learning), a novel fine-tuning method that enhances CLIP's ability to handle lengthy text by leveraging both global and local semantic alignments between image and lengthy text. Our approach consists of two key components: Local Image-Sentence Matching (LISM), which identifies corresponding pairs between image segments and descriptive sentences, and Token Similarity-based Learning (TSL), which efficiently propagates local element attention through these matched pairs. Evaluating GOAL on three new benchmarks for image-lengthy text retrieval, we demonstrate significant improvements over baseline CLIP fine-tuning, establishing a simple yet effective approach for adapting CLIP to detailed textual descriptions. Through extensive experiments, we show that our method's focus on local semantic alignment alongside global context leads to more nuanced and representative embeddings, particularly beneficial for tasks requiring fine-grained understanding of lengthy text descriptions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_GOAL_Global-local_Object_Alignment_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_GOAL_Global-local_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Hyungyu and Jang, Young Kyun and Eom, Chanho},
    title     = {GOAL: Global-local Object Alignment Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4070-4079}
}

raw_affiliation:
[{"affiliation": "Chung-Ang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}]


title: Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
authors: Zhen Qu, Xian Tao, Xinyi Gong, ShiChen Qu, Qiyu Chen, Zhengtao Zhang, Xingang Wang, Guiguang Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30398-30408
cities: ["Beijing", "Beijing", "Hangzhou", "Unknown", "Luoyang", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_Bayesian_Prompt_Flow_Learning_for_Zero-Shot_Anomaly_Detection_CVPR_2025_paper.html

abstract:
Recently, vision-language models (e.g. CLIP) have demonstrated remarkable performance in zero-shot anomaly detection (ZSAD). By leveraging auxiliary data during training, these models can directly perform cross-category anomaly detection on target datasets, such as detecting defects on industrial product surfaces or identifying tumors in organ tissues. Existing approaches typically construct text prompts through either manual design or the optimization of learnable prompt vectors. However, these methods face several challenges: 1) handcrafted prompts require extensive expert knowledge and trial-and-error; 2) single-form learnable prompts struggle to capture complex anomaly semantics; and 3) an unconstrained prompt space limits generalization to unseen categories. To address these issues, we propose Bayesian Prompt Flow Learning (Bayes-PFL), which models the prompt space as a learnable probability distribution from a Bayesian perspective. Specifically, a prompt flow module is designed to learn both image-specific and image-agnostic distributions, which are jointly utilized to regularize the text prompt space and improve the model's generalization on unseen categories. These learned distributions are then sampled to generate diverse text prompts, effectively covering the prompt space. Additionally, a residual cross-model attention (RCA) module is introduced to better align dynamic text embeddings with fine-grained image features. Extensive experiments on 15 industrial and medical datasets demonstrate our method's superior performance. The code is available at https://github.com/xiaozhen228/Bayes-PFL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_Bayesian_Prompt_Flow_Learning_for_Zero-Shot_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_Bayesian_Prompt_Flow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Zhen and Tao, Xian and Gong, Xinyi and Qu, ShiChen and Chen, Qiyu and Zhang, Zhengtao and Wang, Xingang and Ding, Guiguang},
    title     = {Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30398-30408}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "HDU", "city": "Hangzhou", "country": "China"}, {"affiliation": "Casivision", "city": "Unknown", "country": "China"}, {"affiliation": "Luoyang Institute for Robot and Intelligent Equipment", "city": "Luoyang", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "HDU", "Casivision", "Luoyang Institute for Robot and Intelligent Equipment", "Tsinghua University"]


title: Post-pre-training for Modality Alignment in Vision-Language Foundation Models
authors: Shin'ya Yamaguchi, Dewei Feng, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4256-4266
affiliations: ["NTT", "Kyoto University", "MIT", "NTT", "NTT", "NTT"]
cities: ["Tokyo", "Kyoto", "Cambridge", "Tokyo", "Tokyo", "Tokyo"]
countries: ["Japan", "Japan", "USA", "Japan", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yamaguchi_Post-pre-training_for_Modality_Alignment_in_Vision-Language_Foundation_Models_CVPR_2025_paper.html

abstract:
Contrastive language image pre-training (CLIP) is an essential component of building modern vision-language foundation models. While CLIP demonstrates remarkable zero-shot performance on downstream tasks, the multi-modal feature spaces still suffer from a modality gap, which is a gap between image and text feature clusters and limits downstream task performance. Although existing works attempt to address the modality gap by modifying pre-training or fine-tuning, they struggle with heavy training costs with large datasets or degradations of zero-shot performance. This paper presents CLIP-Refine, a post-pre-training method for CLIP models at a phase between pre-training and fine-tuning. CLIP-Refine aims to align the feature space with 1 epoch training on small image-text datasets without zero-shot performance degradations. To this end, we introduce two techniques: random feature alignment (RaFA) and hybrid contrastive-distillation (HyCD). RaFA aligns the image and text features to follow a shared prior distribution by minimizing the distance to random reference vectors sampled from the prior. HyCD updates the model with hybrid soft labels generated by combining ground-truth image-text pair labels and outputs from the pre-trained CLIP model. This contributes to achieving both maintaining the past knowledge and learning new knowledge to align features. Our extensive experiments with multiple classification and retrieval tasks show that CLIP-Refine succeeds in mitigating the modality gap and improving the zero-shot performance. Code is available at https://github.com/yshinya6/clip-refine.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yamaguchi_Post-pre-training_for_Modality_Alignment_in_Vision-Language_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yamaguchi_Post-pre-training_for_Modality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yamaguchi_2025_CVPR,
    author    = {Yamaguchi, Shin'ya and Feng, Dewei and Kanai, Sekitoshi and Adachi, Kazuki and Chijiwa, Daiki},
    title     = {Post-pre-training for Modality Alignment in Vision-Language Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4256-4266}
}

raw_affiliation:
[{"affiliation": "NTT", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Kyoto University", "city": "Kyoto", "country": "Japan"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "NTT", "city": "Tokyo", "country": "Japan"}, {"affiliation": "NTT", "city": "Tokyo", "country": "Japan"}, {"affiliation": "NTT", "city": "Tokyo", "country": "Japan"}]


title: Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention
authors: Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13970-13979
affiliations: ["Forschungszentrum J\u00a8ulich, RWTH Aachen University"]
cities: ["J\u00fclich"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ahmed_Efficient_Event-Based_Object_Detection_A_Hybrid_Neural_Network_with_Spatial_CVPR_2025_paper.html

abstract:
Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for robust object detection.  While Spiking Neural Networks (SNNs) on neuromorphic hardware are often considered for energy efficient and low latency event-based data processing, they often fall short of Artificial Neural Networks (ANNs) in accuracy and flexibility. Here, we introduce Attention-based Hybrid SNN-ANN backbones for event-based object detection to leverage the strengths of both SNN and ANN architectures. A novel Attention-based SNN-ANN bridge module proposed to captures sparse spatial and temporal relations from the SNN layer and converts them into dense feature maps for the ANN part of the backbone. Additionally, we present a variant that integrates DWConvLSTMs to the ANN blocks to capture slower dynamics. This multi-timescale network combines fast SNN processing for short timesteps with long-term dense RNN processing, effectively capturing both fast and slow dynamics.Experimental results demonstrate that our proposed method surpasses SNN-based approaches by significant margins, with results comparable to existing ANN and RNN-based methods. Unlike ANN-only networks, the hybrid setup allow us to implement the SNN blocks on digital neuromorphic hardware to investigate the feasibility of our approach.Extensive ablation studies and implementation on neuromorphic hardware confirm the effectiveness of our proposed modules and architectural choices.Our hybrid SNN-ANN architectures pave the way for ANN-like performance at a drastically reduced parameter, latency, and power budget.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_Efficient_Event-Based_Object_Detection_A_Hybrid_Neural_Network_with_Spatial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ahmed_Efficient_Event-Based_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ahmed_2025_CVPR,
    author    = {Ahmed, Soikat Hasan and Finkbeiner, Jan and Neftci, Emre},
    title     = {Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13970-13979}
}

raw_affiliation:
[{"affiliation": "Forschungszentrum J\u00a8ulich, RWTH Aachen University", "city": "J\u00fclich", "country": "Germany"}]


title: SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces
authors: Sumit Chaturvedi, Mengwei Ren, Yannick Hold-Geoffroy, Jingyuan Liu, Julie Dorsey, Zhixin Shu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 369-379
affiliations: ["Yale University", "Adobe"]
cities: ["New Haven", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chaturvedi_SynthLight_Portrait_Relighting_with_Diffusion_Model_by_Learning_to_Re-render_CVPR_2025_paper.html

abstract:
We introduce SynthLight, a diffusion model for portrait relighting. We frame image relighting as a re-rendering problem, where pixels are transformed in response to changes in environmental lighting. Using a physically-based rendering engine, we create  a dataset to simulate this lighting-conditioned transformation with 3D head assets under varying lighting. We propose training and inference strategies to bridge the gap between the synthetic and real image domains: (1) multi-task training leveraging real human portraits without lighting labels; (2) an inference time diffusion sampling scheme based on classifier-free guidance leveraging the input portrait to better preserve details. Our method generalizes to diverse real portraits and produces realistic illumination effects, including specular highlights and cast shadows, while preserving identity. Our quantitative experiments on light stage data demonstrate results comparable to state-of-the-art relighting methods. Our qualitative results on in-the-wild images showcase rich and unprecedented illumination effects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chaturvedi_SynthLight_Portrait_Relighting_with_Diffusion_Model_by_Learning_to_Re-render_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chaturvedi_SynthLight_Portrait_Relighting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chaturvedi_2025_CVPR,
    author    = {Chaturvedi, Sumit and Ren, Mengwei and Hold-Geoffroy, Yannick and Liu, Jingyuan and Dorsey, Julie and Shu, Zhixin},
    title     = {SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {369-379}
}

raw_affiliation:
[{"affiliation": "Yale University", "city": "New Haven", "country": "USA"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


title: Pseudo Visible Feature Fine-Grained Fusion for Thermal Object Detection
authors: Ting Li, Mao Ye, Tianwen Wu, Nianxin Li, Shuaifeng Li, Song Tang, Luping Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6710-6719
cities: ["Chengdu", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Pseudo_Visible_Feature_Fine-Grained_Fusion_for_Thermal_Object_Detection_CVPR_2025_paper.html

abstract:
Thermal object detection is a critical task in various fields, such as surveillance and autonomous driving. Current state-of-the-art (SOTA) models always leverage a prior Thermal-To-Visible (T2V) translation model to obtain visible spectrum information, followed by a cross-modality aggregation module to fuse information from both modalities. However, this fusion approach does not fully exploit the complementary visible spectrum information beneficial for thermal detection. To address this issue, we propose a novel cross-modal fusion method called Pseudo Visible Feature Fine-Grained Fusion (PFGF). Specifically, a graph is constructed with nodes generated from multi-level thermal features and pseudo-visual latent features produced by the T2V model. Each level of features corresponds to a subgraph. An Inter-Mamba block is proposed to perform cross-modality fusion between nodes at the lowest level; while a Cascade Knowledge Integration (CKI) strategy is used to fuse low-level fused information to high-level subgraphs in a cascade manner. After several iterations of graph node updating, each subgraph outputs an aggregated feature to the detection head respectively. Unlike previous cross-modal fusion methods, our approach explicitly models high-level relationships between cross-modal data, effectively fusing different granularity information. Experimental results demonstrate that our method achieves SOTA detection performance. Code is available at https://github.com/liting1018/PFGF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Pseudo_Visible_Feature_Fine-Grained_Fusion_for_Thermal_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Pseudo_Visible_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ting and Ye, Mao and Wu, Tianwen and Li, Nianxin and Li, Shuaifeng and Tang, Song and Ji, Luping},
    title     = {Pseudo Visible Feature Fine-Grained Fusion for Thermal Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6710-6719}
}

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

affiliations:
["University of Electronic Science and Technology of China", "University of Shanghai for Science and Technology"]


title: HUNet: Homotopy Unfolding Network for Image Compressive Sensing
authors: Feiyang Shen, Hongping Gan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12799-12808
affiliations: ["School of Software, Northwestern Polytechnical University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_HUNet_Homotopy_Unfolding_Network_for_Image_Compressive_Sensing_CVPR_2025_paper.html

abstract:
Deep Unfolding Networks (DUNs) have risen to prominence due to their interpretability and superior performance for image Compressive Sensing (CS). However, existing DUNs still face significant issues, such as the insufficient representation capability of single-scale image information during the iterative reconstruction phase and loss of feature information, which fundamentally limit the further enhancement of image CS performance. In this paper, we propose Homotopy Unfolding Network (HUNet) for image CS, which enables phase-by-phase reconstruction of images along homotopy path. Specifically, each iteration step of the traditional homotopy algorithm is mapped to a Multi-scale Homotopy Iterative Module (MHIM), which includes U-shaped stacked window-based Transformer blocks capable of efficient feature extraction. Within the MHIM, we design the deep homotopy continuation strategy to ensure the interpretability of the homotopy algorithm and facilitate feature learning. Additionally, we introduce a dual-path feature fusion module to mitigate the loss of high-dimensional feature information during the transmission between iterative phases, thereby maximizing the preservation of details in the reconstructed image. Extensive experiments indicate that HUNet achieves superior image reconstruction results compared to existing state-of-the-art methods. The source code is available at https://github.com/ICSResearch/HUNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_HUNet_Homotopy_Unfolding_Network_for_Image_Compressive_Sensing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_HUNet_Homotopy_Unfolding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Feiyang and Gan, Hongping},
    title     = {HUNet: Homotopy Unfolding Network for Image Compressive Sensing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12799-12808}
}

raw_affiliation:
[{"affiliation": "School of Software, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]


title: HalLoc: Token-level Localization of Hallucinations for Vision Language Models
authors: Eunkyu Park, Minyeong Kim, Gunhee Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29893-29903
raw_affiliation: [{"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Seoul National University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_HalLoc_Token-level_Localization_of_Hallucinations_for_Vision_Language_Models_CVPR_2025_paper.html

abstract:
Hallucinations pose a significant challenge to the reliability of large vision-language models, making their detection essential for ensuring accuracy in critical applications. Current detection methods often rely on computationally intensive models, leading to high latency and resource demands. Their definitive outcomes also fail to account for real-world scenarios where the line between hallucinated and truthful information is unclear. To address these issues, we propose HalLoc, a dataset designed for efficient, probabilistic hallucination detection. It features 150K token-level annotated samples, including hallucination types, across Visual Question Answering (VQA), instruction-following, and image captioning tasks. This dataset facilitates the development of models that detect hallucinations with graded confidence, enabling more informed user interactions. Additionally, we introduce a baseline model trained on HalLoc, offering low-overhead, concurrent hallucination detection during generation. The model can be seamlessly integrated into existing VLMs, improving reliability while preserving efficiency. The prospect of a robust plug-and-play hallucination detection module opens new avenues for enhancing the trustworthiness of vision-language models in real-world applications. The HalLoc dataset and code are publicly available at: https://github.com/dbsltm/cvpr25_halloc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_HalLoc_Token-level_Localization_of_Hallucinations_for_Vision_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_HalLoc_Token-level_Localization_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Eunkyu and Kim, Minyeong and Kim, Gunhee},
    title     = {HalLoc: Token-level Localization of Hallucinations for Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29893-29903}
}


title: DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis
authors: Yuming Gu, Phong Tran, Yujian Zheng, Hongyi Xu, Heyuan Li, Adilbek Karmanov, Hao Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26263-26273
cities: ["Los Angeles", "Abu Dhabi", "Beijing", "Shenzhen", "Los Angeles"]
countries: ["USA", "UAE", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_DiffPortrait360_Consistent_Portrait_Diffusion_for_360_View_Synthesis_CVPR_2025_paper.html

abstract:
Generating high-quality 360-degree views of human heads from single-view images is essential for enabling accessible immersive telepresence applications and scalable personalized content creation.While cutting-edge methods for full head generation are limited to modeling realistic human heads, the latest diffusion-based approaches for style-omniscient head synthesis can produce only frontal views and struggle with view consistency, preventing their conversion into true 3D models for rendering from arbitrary angles.We introduce a novel approach that generates fully consistent 360-degree head views, accommodating human, stylized, and anthropomorphic forms, including accessories like glasses and hats. Our method builds on the DiffPortrait3D framework, incorporating a custom ControlNet for back-of-head detail generation and a dual appearance module to ensure global front-back consistency. By training on continuous view sequences and integrating a back reference image, our approach achieves robust, locally continuous view synthesis. Our model can be used to produce high-quality neural radiance fields (NeRFs) for real-time, free-viewpoint rendering, outperforming state-of-the-art methods in object synthesis and 360-degree head generation for very challenging input portraits.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_DiffPortrait360_Consistent_Portrait_Diffusion_for_360_View_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_DiffPortrait360_Consistent_Portrait_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Yuming and Tran, Phong and Zheng, Yujian and Xu, Hongyi and Li, Heyuan and Karmanov, Adilbek and Li, Hao},
    title     = {DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26263-26273}
}

raw_affiliation:
[{"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "ByteDance Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pinscreen Inc.", "city": "Los Angeles", "country": "USA"}]

affiliations:
["University of Southern California", "MBZUAI", "ByteDance Inc.", "The Chinese University of Hong Kong, Shenzhen", "Pinscreen Inc."]


title: SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30514-30523
cities: ["Xi'an", "Beijing", "Hong Kong", "Beijing", "Harbin"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_SURGEON_Memory-Adaptive_Fully_Test-Time_Adaptation_via_Dynamic_Activation_Sparsity_CVPR_2025_paper.html

authors:
Ke Ma, Jiaqi Tang, Bin Guo, Fan Dang, Sicong Liu, Zhui Zhu, Lei Wu, Cheng Fang, Ying-Cong Chen, Zhiwen Yu, Yunhao Liu

abstract:
Despite the growing integration of deep models into mobile terminals, the accuracy of these models declines significantly due to various deployment interferences. Test-time adaptation (TTA) has emerged to improve the performance of deep models by adapting them to unlabeled target data online. Yet, the significant memory cost, particularly in resource-constrained terminals, impedes the effective deployment of most backward-propagation-based TTA methods. To tackle memory constraints, we introduce SURGEON, a method that substantially reduces memory cost while preserving comparable accuracy improvements during fully test-time adaptation (FTTA) without relying on specific network architectures or modifications to the original training procedure. Specifically, we propose a novel dynamic activation sparsity strategy that directly prunes activations at layer-specific dynamic ratios during adaptation, allowing for flexible control of learning ability and memory cost in a data-sensitive manner. Among this, two metrics, Gradient Importance and Layer Activation Memory, are considered to determine the layer-wise pruning ratios, reflecting accuracy contribution and memory efficiency, respectively. Experimentally, our method surpasses the baselines by not only reducing memory usage but also achieving superior accuracy, delivering SOTA performance across diverse datasets, architectures, and tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_SURGEON_Memory-Adaptive_Fully_Test-Time_Adaptation_via_Dynamic_Activation_Sparsity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_SURGEON_Memory-Adaptive_Fully_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Ke and Tang, Jiaqi and Guo, Bin and Dang, Fan and Liu, Sicong and Zhu, Zhui and Wu, Lei and Fang, Cheng and Chen, Ying-Cong and Yu, Zhiwen and Liu, Yunhao},
    title     = {SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30514-30523}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Harbin Engineering University", "city": "Harbin", "country": "China"}]

affiliations:
["Northwestern Polytechnical University", "Tsinghua University", "The Hong Kong University of Science and Technology", "Beijing Jiaotong University", "Harbin Engineering University"]


title: NVILA: Efficient Frontier Visual Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4122-4134
affiliations: ["NVIDIA", "MIT", "UC Berkeley", "UC San Diego", "University of Washington", "Tsinghua University"]
cities: ["Santa Clara", "Cambridge", "Berkeley", "San Diego", "Seattle", "Beijing"]
countries: ["USA", "USA", "USA", "USA", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_NVILA_Efficient_Frontier_Visual_Language_Models_CVPR_2025_paper.html

authors:
Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang, Yuming Lou, Shang Yang, Haocheng Xi, Shiyi Cao, Yuxian Gu, Dacheng Li, Xiuyu Li, Haotian Tang, Yunhao Fang, Yukang Chen, Cheng-Yu Hsieh, De-An Huang, An-Chieh Cheng, Jinyi Hu, Sifei Liu, Ranjay Krishna, Pavlo Molchanov, Jan Kautz, Hongxu Yin, Song Han, Yao Lu

abstract:
Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy. Building on top of VILA, we improve its model architecture by first scaling up the spatial and temporal resolutions, and then compressing visual tokens. This "scale-then-compress" approach enables NVILA to efficiently process high-resolution images and long videos. We also conduct a systematic investigation to enhance the efficiency of NVILA throughout its entire lifecycle, from training to deployment. NVILA matches or surpasses the accuracy of many leading open and proprietary VLMs across a wide range of image and video benchmarks. At the same time, it reduces training costs by 1.9-5.1X, prefilling latency by 1.6-2.2X, and decoding latency by 1.2-2.8X.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_NVILA_Efficient_Frontier_Visual_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_NVILA_Efficient_Frontier_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhijian and Zhu, Ligeng and Shi, Baifeng and Zhang, Zhuoyang and Lou, Yuming and Yang, Shang and Xi, Haocheng and Cao, Shiyi and Gu, Yuxian and Li, Dacheng and Li, Xiuyu and Tang, Haotian and Fang, Yunhao and Chen, Yukang and Hsieh, Cheng-Yu and Huang, De-An and Cheng, An-Chieh and Hu, Jinyi and Liu, Sifei and Krishna, Ranjay and Molchanov, Pavlo and Kautz, Jan and Yin, Hongxu and Han, Song and Lu, Yao},
    title     = {NVILA: Efficient Frontier Visual Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4122-4134}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting
authors: Dongliang Luo, Hanshen Zhu, Ziyang Zhang, Dingkang Liang, Xudong Xie, Yuliang Liu, Xiang Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9329-9338
affiliations: ["Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_SemiETS_Integrating_Spatial_and_Content_Consistencies_for_Semi-Supervised_End-to-end_Text_CVPR_2025_paper.html

abstract:
Most previous scene text spotting methods rely on high-quality manual annotations to achieve promising performance. To reduce their expensive costs, we study semi-supervised text spotting (SSTS) to exploit useful information from unlabeled images. However, directly applying existing semi-supervised methods of general scenes to SSTS will face new challenges: 1) inconsistent pseudo labels between detection and recognition tasks, and 2) sub-optimal supervisions caused by inconsistency between teacher/student. Thus, we propose a new Semi-supervised framework for End-to-end Text Spotting, namely SemiETS that leverages the complementarity of text detection and recognition. Specifically, it gradually generates reliable hierarchical pseudo labels for each task, thereby reducing noisy labels. Meanwhile, it extracts important information in locations and transcriptions from bidirectional flows to improve consistency. Extensive experiments on three datasets under various settings demonstrate the effectiveness of SemiETS on arbitrary-shaped text. For example, it outperforms previous state-of-the-art SSL methods by a large margin on end-to-end spotting (+8.7%, +5.6%, and +2.6% H-mean under 0.5%, 1%, and 2% labeled data settings on Total-Text, respectively). More importantly, it still improves upon a strongly supervised text spotter trained with plenty of labeled data by 2.0%. Compelling domain adaptation ability shows practical potential. Moreover, our method demonstrates consistent improvement on different text spotters. Moreover, our method demonstrates consistent improvement on different text spotters. Code will be available at https://github.com/DrLuo/SemiETS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_SemiETS_Integrating_Spatial_and_Content_Consistencies_for_Semi-Supervised_End-to-end_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_SemiETS_Integrating_Spatial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Dongliang and Zhu, Hanshen and Zhang, Ziyang and Liang, Dingkang and Xie, Xudong and Liu, Yuliang and Bai, Xiang},
    title     = {SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9329-9338}
}


title: See Further When Clear: Curriculum Consistency Model
authors: Yunpeng Liu, Boxiao Liu, Yi Zhang, Xingzhong Hou, Guanglu Song, Yu Liu, Haihang You
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18103-18112
cities: ["Beijing", "Beijing", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_See_Further_When_Clear_Curriculum_Consistency_Model_CVPR_2025_paper.html

abstract:
Significant advances have been made in the sampling efficiency of diffusion and flow matching models, driven by Consistency Distillation (CD), which trains a student model to mimic the output of a teacher model at a later timestep. However, we found that the knowledge discrepancy between student and teacher varies significantly across different timesteps, leading to suboptimal performance in CD.To address this issue, we propose the Curriculum Consistency Model (CCM), which stabilizes and balances the knowledge discrepancy across timesteps. Specifically, we regard the distillation process at each timestep as a curriculum and introduce a metric based on the Peak Signal-to-Noise Ratio (PSNR) to quantify the knowledge discrepancy of this curriculum, then ensure that the curriculum maintains consistent knowledge discrepancy across different timesteps by having the teacher model iterate more steps when the noise intensity is low.Our method achieves competitive single-step sampling Frechet Inception Distance (FID) scores of 1.64 on CIFAR-10 and 2.18 on ImageNet 64x64.Moreover, we have extended our method to large-scale text-to-image models and confirmed that it generalizes well to both diffusion models (Stable Diffusion XL) and flow matching models (Stable Diffusion 3). The generated samples demonstrate improved image-text alignment and semantic structure since CCM enlarges the distillation step at large timesteps and reduces the accumulated error.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_See_Further_When_Clear_Curriculum_Consistency_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_See_Further_When_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yunpeng and Liu, Boxiao and Zhang, Yi and Hou, Xingzhong and Song, Guanglu and Liu, Yu and You, Haihang},
    title     = {See Further When Clear: Curriculum Consistency Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18103-18112}
}

raw_affiliation:
[{"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Technology, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}]

affiliations:
["Institute of Computing Technology, Chinese Academy of Sciences", "School of Computer Science and Technology, University of Chinese Academy of Sciences", "SenseTime Research"]


title: From Slow Bidirectional to Fast Autoregressive Video Diffusion Models
authors: Tianwei Yin, Qiang Zhang, Richard Zhang, William T. Freeman, Fredo Durand, Eli Shechtman, Xun Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22963-22974
affiliations: ["MIT", "Adobe"]
cities: ["Cambridge", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_From_Slow_Bidirectional_to_Fast_Autoregressive_Video_Diffusion_Models_CVPR_2025_paper.html

abstract:
Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. To further reduce latency, we extend distribution matching distillation (DMD) to videos, distilling 50-step diffusion model into a 4-step generator. To enable stable and high-quality distillation, we introduce a student initialization scheme based on teacher's ODE trajectories, as well as an asymmetric distillation strategy that supervises a causal student model with a bidirectional teacher. This approach effectively mitigates error accumulation in autoregressive generation, allowing long-duration video synthesis despite training on short clips. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models. It enables fast streaming generation of high-quality videos at 9.4 FPS on a single GPU thanks to KV caching. Our approach also enables streaming video-to-video translation, image-to-video, and dynamic prompting in a zero-shot manner. We release our code and pretrained models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_From_Slow_Bidirectional_to_Fast_Autoregressive_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_From_Slow_Bidirectional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Tianwei and Zhang, Qiang and Zhang, Richard and Freeman, William T. and Durand, Fredo and Shechtman, Eli and Huang, Xun},
    title     = {From Slow Bidirectional to Fast Autoregressive Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22963-22974}
}

raw_affiliation:
[{"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


authors: Libo Zhu, Jianze Li, Haotong Qin, Wenbo Li, Yulun Zhang, Yong Guo, Xiaokang Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12778-12788
cities: ["Shanghai", "Zurich", "Hong Kong", "Saarbr\u00fccken"]
countries: ["China", "Switzerland", "China", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_PassionSR_Post-Training_Quantization_with_Adaptive_Scale_in_One-Step_Diffusion_based_CVPR_2025_paper.html

title:
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution

abstract:
Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced to one, they require high computational costs and storage requirements, making it difficult for deployment on hardware devices. To address these issues, we propose a novel post-training quantization approach with adaptive scale in one-step diffusion (OSD) image SR, PassionSR. First, we simplify OSD model to two core components, UNet and Variational Autoencoder (VAE) by removing the CLIPEncoder. Secondly, we propose Learnable Boundary Quantizer (LBQ) and Learnable Equivalent Transformation (LET) to optimize the quantization process and manipulate activation distributions for better quantization. Finally, we design a Distributed Quantization Calibration (DQC) strategy that stabilizes the training of quantized parameters for rapid convergence. Comprehensive experiments demonstrate that PassionSR with 8-bit and 6-bit obtains comparable visual results with full-precision model. Moreover, our PassionSR achieves significant advantages over recent leading low-bit quantization methods for image SR. Our code will be released

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_PassionSR_Post-Training_Quantization_with_Adaptive_Scale_in_One-Step_Diffusion_based_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_PassionSR_Post-Training_Quantization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Libo and Li, Jianze and Qin, Haotong and Li, Wenbo and Zhang, Yulun and Guo, Yong and Yang, Xiaokang},
    title     = {PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12778-12788}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "ETH Z\burch", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}]

affiliations:
["Shanghai Jiao Tong University", "ETH Z\burch", "Chinese University of Hong Kong", "Max Planck Institute for Informatics"]


title: RainyGS: Efficient Rain Synthesis with Physically-Based Gaussian Splatting
authors: Qiyu Dai, Xingyu Ni, Qianfan Shen, Wenzheng Chen, Baoquan Chen, Mengyu Chu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16153-16162
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_RainyGS_Efficient_Rain_Synthesis_with_Physically-Based_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
We consider the problem of adding dynamic rain effects to in-the-wild scenes in a physically correct manner. Recent advances in scene modeling have made significant progress, with NeRF and 3DGS techniques emerging as powerful tools for reconstructing complex scenes. However, while effective for novel view synthesis, these methods typically struggle with challenging scene editing tasks, such as physics-based rain simulation. In contrast, traditional physics-based simulations can generate realistic rain effects, such as raindrops and splashes, but they often rely on skilled artists to carefully set up high-fidelity scenes. This process lacks flexibility and scalability, limiting its applicability to broader, open-world environments. In this work, we introduce RainyGS, a novel approach that leverages the strengths of both physics-based modeling and 3DGS to generate photorealistic, dynamic rain effects in open-world scenes with physical accuracy. At the core of our method is the integration of physically-based raindrop and shallow water simulation techniques within the fast 3DGS rendering framework, enabling realistic and efficient simulations of raindrop behavior, splashes, and reflections. Our method supports synthesizing rain effects at over 30 fps, offering users flexible control over rain intensity--from light drizzles to heavy downpours. We demonstrate that RainyGS performs effectively for both real-world outdoor scenes and large-scale driving scenarios, delivering more photorealistic and physically accurate rain effects compared to state-of-the-art methods.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_RainyGS_Efficient_Rain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Qiyu and Ni, Xingyu and Shen, Qianfan and Chen, Wenzheng and Chen, Baoquan and Chu, Mengyu},
    title     = {RainyGS: Efficient Rain Synthesis with Physically-Based Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16153-16162}
}

raw_affiliation:
[{"affiliation": "School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of EECS, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Multimedia Information Processing, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Intelligence Science and Technology, Peking University", "School of Computer Science, Peking University", "School of EECS, Peking University", "Wangxuan Institute of Computer Technology, Peking University", "State Key Laboratory of Multimedia Information Processing, Peking University", "State Key Laboratory of General Artificial Intelligence, Peking University"]


title: Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image Synthesis
authors: Boming Miao, Chunxiao Li, Xiaoxiao Wang, Andi Zhang, Rui Sun, Zizhe Wang, Yao Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23575-23584
cities: ["Beijing", "Beijing", "Manchester", "Shenzhen", "Beijing"]
countries: ["China", "China", "UK", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Miao_Noise_Diffusion_for_Enhancing_Semantic_Faithfulness_in_Text-to-Image_Synthesis_CVPR_2025_paper.html

abstract:
Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts. Optimizing the initial noisy latent offers a more efficient alternative to modifying model architectures or prompt engineering for improving semantic alignment. A latest approach, InitNo, refines the initial noisy latent by leveraging attention maps; however, these maps capture only limited information, and the effectiveness of InitNo is highly dependent on the initial starting point, as it tends to converge on a local optimum near this point. To this end, this paper proposes leveraging the language comprehension capabilities of large vision-language models (LVLMs) to guide the optimization of the initial noisy latent, and introduces the Noise Diffusion process, which updates the noisy latent to generate semantically faithful images while preserving distribution consistency. Furthermore, we provide a theoretical analysis of the condition under which the update improves semantic faithfulness. Experimental results demonstrate the effectiveness and adaptability of our framework, consistently enhancing semantic alignment across various diffusion models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Miao_Noise_Diffusion_for_Enhancing_Semantic_Faithfulness_in_Text-to-Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Miao_Noise_Diffusion_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Miao_2025_CVPR,
    author    = {Miao, Boming and Li, Chunxiao and Wang, Xiaoxiao and Zhang, Andi and Sun, Rui and Wang, Zizhe and Zhu, Yao},
    title     = {Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23575-23584}
}

raw_affiliation:
[{"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Manchester", "city": "Manchester", "country": "UK"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Normal University", "University of Chinese Academy of Sciences", "University of Manchester", "The Chinese University of Hong Kong, Shenzhen", "Tsinghua University"]


authors: Wenyuan Zhang, Yixiao Yang, Han Huang, Liang Han, Kanle Shi, Yu-Shen Liu, Zhizhong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21642-21653
cities: ["Beijing", "Beijing", "Detroit"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MonoInstance_Enhancing_Monocular_Priors_via_Multi-view_Instance_Alignment_for_Neural_CVPR_2025_paper.html

title:
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction

abstract:
Monocular depth priors have been widely adopted by neural rendering in multi-view based tasks such as 3D reconstruction and novel view synthesis. However, due to the inconsistent prediction on each view, how to more effectively leverage monocular cues in a multi-view context remains a challenge. Current methods treat the entire estimated depth map indiscriminately, and use it as ground truth supervision, while ignoring the inherent inaccuracy and cross-view inconsistency in monocular priors. To resolve these issues, we propose MonoInstance, a general approach that explores the uncertainty of monocular depths to provide enhanced geometric priors for neural rendering and reconstruction. Our key insight lies in aligning each segmented instance depths from multiple views within a common 3D space, thereby casting the uncertainty estimation of monocular depths into a density measure within noisy point clouds. For high-uncertainty areas where depth priors are unreliable, we further introduce a constraint term that encourages the projected instances to align with corresponding instance masks on nearby views. MonoInstance is a versatile strategy which can be seamlessly integrated into various multi-view neural rendering frameworks. Our experimental results demonstrate that MonoInstance significantly improves the performance in both reconstruction and novel view synthesis under various benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MonoInstance_Enhancing_Monocular_Priors_via_Multi-view_Instance_Alignment_for_Neural_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MonoInstance_Enhancing_Monocular_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Wenyuan and Yang, Yixiao and Huang, Han and Han, Liang and Shi, Kanle and Liu, Yu-Shen and Han, Zhizhong},
    title     = {MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21642-21653}
}

raw_affiliation:
[{"affiliation": "School of Software, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science, W ayne State University", "city": "Detroit", "country": "USA"}]

affiliations:
["School of Software, Tsinghua University", "Kuaishou Technology", "Department of Computer Science, W ayne State University"]


title: Three-view Focal Length Recovery From Homographies
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11505-11514
cities: ["Prague", "Bratislava", "Nanjing", "Shanghai"]
countries: ["Czech Republic", "Slovakia", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ding_Three-view_Focal_Length_Recovery_From_Homographies_CVPR_2025_paper.html

authors:
Yaqing Ding, Viktor Kocur, Zuzana Berger Haladova, Qianliang Wu, Shen Cai, Jian Yang, Zuzana Kukelova

abstract:
In this paper, we propose a novel approach for recovering focal lengths from three-view homographies. By examining the consistency of normal vectors between two homographies, we derive new explicit constraints between the focal lengths and homographies using an elimination technique. We demonstrate that three-view homographies provide two additional constraints, enabling the recovery of one or two focal lengths. We discuss four possible cases, including three cameras having an unknown equal focal length, three cameras having two different unknown focal lengths, three cameras where one focal length is known, and the other two cameras have equal or different unknown focal lengths. All the problems can be converted into solving polynomials in one or two unknowns, which can be efficiently solved using Sturm sequence or hidden variable technique. Evaluation using both synthetic and real data shows that the proposed solvers are both faster and more accurate than methods relying on existing two-view solvers. The code and data are available on https://github.com/kocurvik/hf.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_Three-view_Focal_Length_Recovery_From_Homographies_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_Three-view_Focal_Length_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ding_2025_CVPR,
    author    = {Ding, Yaqing and Kocur, Viktor and Haladova, Zuzana Berger and Wu, Qianliang and Cai, Shen and Yang, Jian and Kukelova, Zuzana},
    title     = {Three-view Focal Length Recovery From Homographies},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11505-11514}
}

raw_affiliation:
[{"affiliation": "Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava", "city": "Bratislava", "country": "Slovakia"}, {"affiliation": "PCA Lab, Nanjing University of Science and Technology, Nanjing, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Visual and Geometric Perception Lab, Donghua University", "city": "Shanghai", "country": "China"}]

affiliations:
["Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague", "Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava", "PCA Lab, Nanjing University of Science and Technology, Nanjing, China", "Visual and Geometric Perception Lab, Donghua University"]


title: NoPain: No-box Point Cloud Attack via Optimal Transport Singular Boundary
authors: Zezeng Li, Xiaoyu Du, Na Lei, Liming Chen, Weimin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3492-3502
affiliations: ["School of Software, Dalian University of Technology", "\u00c9cole Centrale de Lyon"]
cities: ["Dalian", "Lyon"]
countries: ["China", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_NoPain_No-box_Point_Cloud_Attack_via_Optimal_Transport_Singular_Boundary_CVPR_2025_paper.html

abstract:
Adversarial attacks exploit the vulnerability of deep models against adversarial samples. Existing point cloud attackers are tailored to specific models, iteratively optimizing perturbations based on gradients in either a white-box or black-box setting. Despite their promising attack performance, they often struggle to produce transferable adversarial samples due to overfitting the specific parameters of surrogate models. To overcome this issue, we shift our focus to the data distribution itself and introduce a novel approach named NoPain, which employs optimal transport (OT) to identify the inherent singular boundaries of the data manifold for cross-network point cloud attacks. Specifically, we first calculate the OT mapping from noise to the target feature space, then identify singular boundaries by locating non-differentiable positions. Finally, we sample along singular boundaries to generate adversarial point clouds. Once the singular boundaries are determined, NoPain can efficiently produce adversarial samples without the need of iterative updates or guidance from the surrogate classifiers. Extensive experiments demonstrate that the proposed end-to-end method outperforms baseline approaches in terms of both transferability and efficiency, while also maintaining notable advantages even against defense strategies. Code and model are available at https://github.com/cognaclee/nopain.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_NoPain_No-box_Point_Cloud_Attack_via_Optimal_Transport_Singular_Boundary_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_NoPain_No-box_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zezeng and Du, Xiaoyu and Lei, Na and Chen, Liming and Wang, Weimin},
    title     = {NoPain: No-box Point Cloud Attack via Optimal Transport Singular Boundary},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3492-3502}
}

raw_affiliation:
[{"affiliation": "School of Software, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "\u00c9cole Centrale de Lyon", "city": "Lyon", "country": "France"}]


title: RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models
authors: Haoran Hao, Jiaming Han, Changsheng Li, Yu-Feng Li, Xiangyu Yue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14538-14548
cities: ["Hong Kong", "Nanjing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_RAP_Retrieval-Augmented_Personalization_for_Multimodal_Large_Language_Models_CVPR_2025_paper.html

abstract:
The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the **R**etrieval **A**ugmented **P**ersonalization (RAP) framework for MLLMs' personalization. Starting from a general MLLM, we turn it into a personalized assistant in three steps. (a) Remember: We design a key-value database to store user-related information, *e.g.*, user's name, avatar and other attributes. (b) Retrieve: When the user initiates a conversation, RAP will retrieve relevant information from the database using a multimodal retriever. (c) Generate: The input query and retrieved concepts' information are fed into MLLMs to generate personalized, knowledge-augmented responses. Unlike previous methods, RAP allows real-time concept editing via updating the external database. To further improve generation quality and alignment with user-specific information, we design a pipeline for data collection and create a specialized dataset for personalized training of MLLMs. Based on the dataset, we train a series of MLLMs as personalized multimodal assistants. By pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual concepts without additional finetuning. Our models demonstrate outstanding flexibility and generation quality across a variety of tasks, such as personalized image captioning, question answering and visual recognition. The code, data and models are available at https://hoar012.github.io/RAP-Project/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_RAP_Retrieval-Augmented_Personalization_for_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_RAP_Retrieval-Augmented_Personalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hao_2025_CVPR,
    author    = {Hao, Haoran and Han, Jiaming and Li, Changsheng and Li, Yu-Feng and Yue, Xiangyu},
    title     = {RAP: Retrieval-Augmented Personalization for Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14538-14548}
}

raw_affiliation:
[{"affiliation": "MMLab, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "National Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["MMLab, The Chinese University of Hong Kong", "National Key Laboratory for Novel Software Technology, Nanjing University", "Beijing Institute of Technology"]


title: FADA: Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation
authors: Tianyun Zhong, Chao Liang, Jianwen Jiang, Gaojie Lin, Jiaqi Yang, Zhou Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3101-3110
affiliations: ["Zhejiang University", "Bytedance Intelligent Creation"]
cities: ["Hangzhou", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_FADA_Fast_Diffusion_Avatar_Synthesis_with_Mixed-Supervised_Multi-CFG_Distillation_CVPR_2025_paper.html

abstract:
Diffusion-based audio-driven talking avatar methods have recently gained attention for their high-fidelity, vivid, and expressive results. However, their slow inference speed limits practical applications. Despite the development of various distillation techniques for diffusion models, we found that naive diffusion distillation methods do not yield satisfactory results. Distilled models exhibit reduced robustness with open-set input images and a decreased correlation between audio and video compared to teacher models, undermining the advantages of diffusion models. To address this, we propose FADA (Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation). We first designed a mixed-supervised loss to leverage data of varying quality and enhance the overall model capability as well as robustness. Additionally, we propose a multi-CFG distillation with learnable tokens to utilize the correlation between audio and reference image conditions, reducing the threefold inference runs caused by multi-CFG with acceptable quality degradation. Extensive experiments across multiple datasets show that FADA generates vivid videos comparable to recent diffusion model-based methods while achieving an NFE speedup of 4.17-12.5 times. Demos are available at our webpage https://fadavatar.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_FADA_Fast_Diffusion_Avatar_Synthesis_with_Mixed-Supervised_Multi-CFG_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_FADA_Fast_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Tianyun and Liang, Chao and Jiang, Jianwen and Lin, Gaojie and Yang, Jiaqi and Zhao, Zhou},
    title     = {FADA: Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3101-3110}
}

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


title: CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26057-26068
affiliations: ["Google DeepMind", "Columbia University", "UC San Diego"]
cities: ["Mountain View", "New York", "San Diego"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_CAT4D_Create_Anything_in_4D_with_Multi-View_Video_Diffusion_Models_CVPR_2025_paper.html

authors:
Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick, Changxi Zheng, Jonathan T. Barron, Aleksander Holynski

abstract:
We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transform a single monocular video into a multi-view video, enabling robust 4D reconstruction via optimization of a deformable 3D Gaussian representation.  We demonstrate competitive performance on novel view synthesis and dynamic scene reconstruction benchmarks, and highlight the creative capabilities for 4D scene generation from real or generated videos. See our project page for results and interactive demos: cat-4d.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_CAT4D_Create_Anything_in_4D_with_Multi-View_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_CAT4D_Create_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Rundi and Gao, Ruiqi and Poole, Ben and Trevithick, Alex and Zheng, Changxi and Barron, Jonathan T. and Holynski, Aleksander},
    title     = {CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26057-26068}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}]


authors: Guanglu Dong, Xiangyu Liao, Mingyang Li, Guihuan Guo, Chao Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28176-28187
affiliations: ["College of Electronics and Information Engineering, Sichuan University, China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Exploring_Semantic_Feature_Discrimination_for_Perceptual_Image_Super-Resolution_and_Opinion-Unaware_CVPR_2025_paper.html

title:
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment

abstract:
Generative Adversarial Networks (GANs) have been widely applied to image super-resolution (SR) to enhance the perceptual quality. However, most existing GAN-based SR methods typically perform coarse-grained discrimination directly on images and ignore the semantic information of images, making it challenging for the super resolution networks (SRN) to learn fine-grained and semantic-related texture details. To alleviate this issue, we propose a semantic feature discrimination method, SFD, for perceptual SR. Specifically, we first design a feature discriminator (Feat-D), to discriminate the pixel-wise middle semantic features from CLIP, aligning the feature distributions of SR images with that of high-quality images. Additionally, we propose a text-guided discrimination method (TG-D) by introducing learnable prompt pairs (LPP) in an adversarial manner to perform discrimination on the more abstract output feature of CLIP, further enhancing the discriminative ability of our method. With both Feat-D and TG-D, our SFD can effectively distinguish between the semantic feature distributions of low-quality and high-quality images, encouraging SRN to generate more realistic and semantic-relevant textures. Furthermore, based on the trained Feat-D and LPP, we propose a novel opinion-unaware no-reference image quality assessment (OU NR-IQA) method, SFD-IQA, greatly improving OU NR-IQA performance without any additional targeted training. Extensive experiments on classical SISR, real-world SISR, and OU NR-IQA tasks, demonstrate the effectiveness of our proposed methods. Code is available at https://github.com/GuangluDong0728/SFD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Exploring_Semantic_Feature_Discrimination_for_Perceptual_Image_Super-Resolution_and_Opinion-Unaware_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Exploring_Semantic_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Guanglu and Liao, Xiangyu and Li, Mingyang and Guo, Guihuan and Ren, Chao},
    title     = {Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28176-28187}
}

raw_affiliation:
[{"affiliation": "College of Electronics and Information Engineering, Sichuan University, China", "city": "Chengdu", "country": "China"}]


title: Distilling Long-tailed Datasets
authors: Zhenghao Zhao, Haoxuan Wang, Yuzhang Shang, Kai Wang, Yan Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30609-30618
cities: ["Chicago", "Chicago", "Singapore"]
countries: ["USA", "USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Distilling_Long-tailed_Datasets_CVPR_2025_paper.html

abstract:
Dataset distillation (DD) aims to synthesize a small information-rich dataset from a large dataset for efficient neural network training. However, existing dataset distillation methods struggle with long-tailed datasets, which are prevalent in real-world scenarios. By investigating the reasons behind this unexpected result, we identified two main causes: 1) The distillation process on imbalanced datasets develops biased gradients, leading to the synthesis of similarly imbalanced distilled datasets. 2) The experts trained on such datasets perform suboptimally on tail classes, resulting in misguided distillation supervision and poor-quality soft-label initialization. To address these issues, we first propose Distribution-agnostic Matching to avoid directly matching the biased expert trajectories. It reduces the distance between the student and the biased expert trajectories and prevents the tail class bias from being distilled to the synthetic dataset. Moreover, we improve the distillation guidance with Expert Decoupling, which jointly matches the decoupled backbone and classifier to improve the tail class performance and initialize reliable soft labels. This work pioneers the field of long-tailed dataset distillation (LTDD), marking the first effective effort to distill long-tailed datasets. Our code will be made public at https://github.com/ichbill/LTDD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Distilling_Long-tailed_Datasets_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Distilling_Long-tailed_Datasets_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Zhenghao and Wang, Haoxuan and Shang, Yuzhang and Wang, Kai and Yan, Yan},
    title     = {Distilling Long-tailed Datasets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30609-30618}
}

raw_affiliation:
[{"affiliation": "University of Illinois Chicago", "city": "Chicago", "country": "USA"}, {"affiliation": "Illinois Institute of Technology", "city": "Chicago", "country": "USA"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["University of Illinois Chicago", "Illinois Institute of Technology", "National University of Singapore"]


title: Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders
authors: Fiona Ryan, Ajay Bati, Sangmin Lee, Daniel Bolya, Judy Hoffman, James M. Rehg
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28874-28884
cities: ["Atlanta", "Suwon", "Urbana"]
countries: ["USA", "South Korea", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ryan_Gaze-LLE_Gaze_Target_Estimation_via_Large-Scale_Learned_Encoders_CVPR_2025_paper.html

abstract:
We address the problem of gaze target estimation, which aims to predict where a person is looking in a scene. Predicting a person's gaze target requires reasoning both about the person's appearance and the contents of the scene. Prior works have developed increasingly complex, hand-crafted pipelines for gaze target estimation that carefully fuse features from separate scene encoders, head encoders, and auxiliary models for signals like depth and pose. Motivated by the success of general-purpose feature extractors on a variety of visual tasks, we propose Gaze-LLE, a novel transformer framework that streamlines gaze target estimation by leveraging features from a frozen DINOv2 encoder. We extract a single feature representation for the scene, and apply a person-specific positional prompt to decode gaze with a lightweight module. We demonstrate state-of-the-art performance across several gaze benchmarks and provide extensive analysis to validate our design choices. Our code and models are available at: http://github.com/fkryan/gazelle.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ryan_Gaze-LLE_Gaze_Target_Estimation_via_Large-Scale_Learned_Encoders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ryan_Gaze-LLE_Gaze_Target_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ryan_2025_CVPR,
    author    = {Ryan, Fiona and Bati, Ajay and Lee, Sangmin and Bolya, Daniel and Hoffman, Judy and Rehg, James M.},
    title     = {Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28874-28884}
}

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

affiliations:
["Georgia Institute of Technology", "Sungkyunkwan University", "University of Illinois Urbana-Champaign"]


title: Distilling Spectral Graph for Object-Context Aware Open-Vocabulary Semantic Segmentation
authors: Chanyoung Kim, Dayun Ju, Woojung Han, Ming-Hsuan Yang, Seong Jae Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15033-15042
affiliations: ["Yonsei University", "University of California, Merced"]
cities: ["Seoul", "Merced"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Distilling_Spectral_Graph_for_Object-Context_Aware_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-Vocabulary Semantic Segmentation (OVSS) has advanced with recent vision-language models (VLMs), enabling segmentation beyond predefined categories through various learning schemes. Notably, training-free methods offer scalable, easily deployable solutions for handling unseen data, a key goal of OVSS. Yet, a critical issue persists: lack of object-level context consideration when segmenting complex objects in the challenging environment of OVSS based on arbitrary query prompts. This oversight limits models' ability to group semantically consistent elements within object and map them precisely to user-defined arbitrary classes. In this work, we introduce a novel approach that overcomes this limitation by incorporating object-level contextual knowledge within images. Specifically, our model enhances intra-object consistency by distilling spectral-driven features from vision foundation models into the attention mechanism of the visual encoder, enabling semantically coherent components to form a single object mask. Additionally, we refine the text embeddings with zero-shot object presence likelihood to ensure accurate alignment with the specific objects represented in the images. By leveraging object-level contextual knowledge, our proposed approach achieves state-of-the-art performance with strong generalizability across diverse datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Distilling_Spectral_Graph_for_Object-Context_Aware_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Distilling_Spectral_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Chanyoung and Ju, Dayun and Han, Woojung and Yang, Ming-Hsuan and Hwang, Seong Jae},
    title     = {Distilling Spectral Graph for Object-Context Aware Open-Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15033-15042}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}]


title: Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models
authors: Yuhao Cui, Xinxing Zu, Wenhua Zhang, Zhongzhou Zhao, Jinyang Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29733-29743
affiliations: ["Alibaba Cloud Computing", "Dalian University of Technology", "Tongyi Lab"]
cities: ["Hangzhou", "Dalian", "China"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Incorporating_Dense_Knowledge_Alignment_into_Unified_Multimodal_Representation_Models_CVPR_2025_paper.html

abstract:
Leveraging Large Language Models (LLMs) for text representation has achieved significant success, but the exploration of using Multimodal LLMs (MLLMs) for multimodal representation remains limited. Previous MLLM-based representation studies have primarily focused on unifying the embedding space while neglecting the importance of multimodal alignment. As a result, their cross-modal retrieval performance falls markedly behind that of the CLIP series models. To address this, in our work, we 1) construct DeKon5M, a contrastive learning dataset enriched with dense multimodal knowledge, which efficiently enhances multimodal alignment capabilities in representation tasks. 2) design a framework for training unified representation on MLLMs. Building upon this unified representation framework and the dense knowledge dataset DeKon5M, we developed the dense knowledge representation model DeKR on Qwen2VL. Through extensive quantitative and qualitative experiments, our results demonstrate that DeKR not only aligns text, image, video, and text-image combinations within a unified embedding space but also achieves cross-modal retrieval performance comparable to SoTA CLIP series models. This fully validates the effectiveness of our approach and provides new insights for multimodal representation research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_Incorporating_Dense_Knowledge_Alignment_into_Unified_Multimodal_Representation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cui_Incorporating_Dense_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Yuhao and Zu, Xinxing and Zhang, Wenhua and Zhao, Zhongzhou and Gao, Jinyang},
    title     = {Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29733-29743}
}

raw_affiliation:
[{"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Tongyi Lab", "city": "China", "country": "China"}]


title: Geometry Field Splatting with Gaussian Surfels
authors: Kaiwen Jiang, Venkataram Sivaram, Cheng Peng, Ravi Ramamoorthi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5752-5762
affiliations: ["University of California, San Diego", "Johns Hopkins University"]
cities: ["San Diego", "Baltimore"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Geometry_Field_Splatting_with_Gaussian_Surfels_CVPR_2025_paper.html

abstract:
Geometric reconstruction of opaque surfaces from images is a longstanding challenge in computer vision, with renewed interest from volumetric view synthesis algorithms using radiance fields.  We leverage the geometry field proposed in recent work for stochastic opaque surfaces, which can then be converted to volume densities.  We adapt Gaussian kernels or surfels to splat the geometry field rather than the volume, enabling precise reconstruction of opaque solids.  Our first contribution is to derive an efficient and almost exact differentiable rendering algorithm for geometry fields parameterized by Gaussian surfels, while removing current approximations involving Taylor series and no self-attenuation. Next, we address the discontinuous loss landscape when surfels cluster near geometry, showing how to guarantee that the rendered color is a continuous function of the colors of the kernels, irrespective of ordering. Finally, we use latent representations with spherical harmonics encoded reflection vectors rather than spherical harmonics encoded colors to better address specular surfaces.  We demonstrate significant improvement in the quality of reconstructed 3D surfaces on widely-used datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Geometry_Field_Splatting_with_Gaussian_Surfels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Geometry_Field_Splatting_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Kaiwen and Sivaram, Venkataram and Peng, Cheng and Ramamoorthi, Ravi},
    title     = {Geometry Field Splatting with Gaussian Surfels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5752-5762}
}

raw_affiliation:
[{"affiliation": "University of California, San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


title: Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos
authors: Linyi Jin, Richard Tucker, Zhengqi Li, David Fouhey, Noah Snavely, Aleksander Holynski
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10497-10509
affiliations: ["Google DeepMind", "University of Michigan", "New York University"]
cities: ["Mountain View", "Ann Arbor", "New York"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_Stereo4D_Learning_How_Things_Move_in_3D_from_Internet_Stereo_CVPR_2025_paper.html

abstract:
Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale supervised training has enabled rapid progress, directly supervising methods for recovering 3D motion remains challenging due to the fundamental difficulty of obtaining ground truth annotations. We present a system for mining high-quality 4D reconstructions from internet stereoscopic, wide-angle videos. Our system fuses and filters the outputs of camera pose estimation, stereo depth estimation, and temporal tracking methods into high-quality dynamic 3D reconstructions. We use this method to generate large-scale data in the form of world-consistent, pseudo-metric 3D point clouds with long-term motion trajectories. We demonstrate the utility of this data by training a variant of DUSt3r to predict structure and 3D motion from real-world image pairs, showing that training on our reconstructed data enables generalization to diverse real-world scenes. Project page and data at https://stereo4d.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_Stereo4D_Learning_How_Things_Move_in_3D_from_Internet_Stereo_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_Stereo4D_Learning_How_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Linyi and Tucker, Richard and Li, Zhengqi and Fouhey, David and Snavely, Noah and Holynski, Aleksander},
    title     = {Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10497-10509}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: PS-EIP: Robust Photometric Stereo Based on Event Interval Profile
authors: Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6241-6251
affiliations: ["University of Tsukuba", "Optech Innovation, LLC.", "National Institute of Informatics (NII)"]
cities: ["Tsukuba", "Unknown", "Tokyo"]
countries: ["Japan", "Unknown", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.html

abstract:
Recently, the energy-efficient photometric stereo method using an event camera has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a time-series profile of event intervals. By exploiting the continuity of the profile and introducing an outlier detection method based on profile shape, our approach enhances robustness against outliers from shadows and specular reflections. Experiments using real event data from 3D-printed objects demonstrate that PS-EIP significantly improves robustness to outliers compared to EventPS's deep-learning variant, EventPS-FCN, without relying on deep learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kitazawa_PS-EIP_Robust_Photometric_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kitazawa_2025_CVPR,
    author    = {Kitazawa, Kazuma and Aoto, Takahito and Ikehata, Satoshi and Takatani, Tsuyoshi},
    title     = {PS-EIP: Robust Photometric Stereo Based on Event Interval Profile},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6241-6251}
}

raw_affiliation:
[{"affiliation": "University of Tsukuba", "city": "Tsukuba", "country": "Japan"}, {"affiliation": "Optech Innovation, LLC.", "city": "Unknown", "country": "Unknown"}, {"affiliation": "National Institute of Informatics (NII)", "city": "Tokyo", "country": "Japan"}]


title: GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors
authors: An Li, Zhe Zhu, Mingqiang Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1308-1318
affiliations: ["Nanjing University of Aeronautics and Astronautics"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GenPC_Zero-shot_Point_Cloud_Completion_via_3D_Generative_Priors_CVPR_2025_paper.html

abstract:
Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, real-world scans. To overcome this limitation, we introduce a zero-shot completion framework, termed GenPC, designed to reconstruct high-quality real-world scans by leveraging explicit 3D generative priors.Our key insight is that recent feed-forward 3D generative models, trained on extensive internet-scale data, have demonstrated the ability to perform 3D generation from single-view images in a zero-shot setting. To harness this for completion, we first develop a Depth Prompting module that links partial point clouds with image-to-3D generative models by leveraging depth images as a stepping stone. To retain the original partial structure in the final results, we designed the Geometric Preserving Fusion module that aligns the generated shape with input by adaptively adjusting its pose and scale.Extensive experiments on widely used benchmarks validate the superiority and generalizability of our approach, bringing us a step closer to robust real-world scan completion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GenPC_Zero-shot_Point_Cloud_Completion_via_3D_Generative_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GenPC_Zero-shot_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, An and Zhu, Zhe and Wei, Mingqiang},
    title     = {GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1308-1318}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}]


title: FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation
authors: Kefan Chen, Chaerin Min, Linguang Zhang, Shreyas Hampali, Cem Keskin, Srinath Sridhar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17448-17460
affiliations: ["Brown University", "Meta Reality Labs"]
cities: ["Providence", "Unknown"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FoundHand_Large-Scale_Domain-Specific_Learning_for_Controllable_Hand_Image_Generation_CVPR_2025_paper.html

abstract:
Despite remarkable progress in image generation models, generating realistic hands remains a persistent challenge due to their complex articulation, varying viewpoints, and frequent occlusions. We present FoundHand, a large-scale domain-specific diffusion model for synthesizing single and dual hand images. To train our model, we introduce FoundHand-10M, a large-scale hand dataset with 2D keypoints and segmentation mask annotations. Our insight is to use 2D hand keypoints as a universal representation that encodes both hand articulation and camera viewpoint. FoundHand learns from image pairs to capture physically plausible hand articulations, natively enables precise control through 2D keypoints, and supports appearance control. Our model exhibits core capabilities that include the ability to repose hands, transfer hand appearance, and even synthesize novel views. This leads to zero-shot capabilities for fixing malformed hands in previously generated images, or synthesizing hand video sequences. We present extensive experiments and evaluations that demonstrate state-of-the-art performance of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_FoundHand_Large-Scale_Domain-Specific_Learning_for_Controllable_Hand_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_FoundHand_Large-Scale_Domain-Specific_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Kefan and Min, Chaerin and Zhang, Linguang and Hampali, Shreyas and Keskin, Cem and Sridhar, Srinath},
    title     = {FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17448-17460}
}

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


title: InterDyn: Controllable Interactive Dynamics with Video Diffusion Models
authors: Rick Akkerman, Haiwen Feng, Michael J. Black, Dimitrios Tzionas, Victoria Fernández Abrevaya
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12467-12479
affiliations: ["Max Planck Institute for Intelligent Systems", "University of Amsterdam"]
cities: ["T\u00fcbingen", "Amsterdam"]
countries: ["Germany", "the Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Akkerman_InterDyn_Controllable_Interactive_Dynamics_with_Video_Diffusion_Models_CVPR_2025_paper.html

abstract:
Predicting the dynamics of interacting objects is essential for both humans and intelligent systems. However, existing approaches are limited to simplified, toy settings and lack generalizability to complex, real-world environments. Recent advances in generative models have enabled the prediction of state transitions based on interventions, but focus on generating a single future state which neglects the continuous dynamics resulting from the interaction. To address this gap, we propose InterDyn, a novel framework that generates videos of interactive dynamics given an initial frame and a control signal encoding the motion of a driving object or actor. Our key insight is that large video generation models can act as both neural renderers and implicit physics "simulators", having learned interactive dynamics from large-scale video data. To effectively harness this capability, we introduce an interactive control mechanism that conditions the video generation process on the motion of the driving entity. Qualitative results demonstrate that InterDyn generates plausible, temporally consistent videos of complex object interactions while generalizing to unseen objects. Quantitative evaluations show that InterDyn outperforms baselines that focus on static state transitions. This work highlights the potential of leveraging video generative models as implicit physics engines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Akkerman_InterDyn_Controllable_Interactive_Dynamics_with_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Akkerman_InterDyn_Controllable_Interactive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Akkerman_2025_CVPR,
    author    = {Akkerman, Rick and Feng, Haiwen and Black, Michael J. and Tzionas, Dimitrios and Abrevaya, Victoria Fern\'andez},
    title     = {InterDyn: Controllable Interactive Dynamics with Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12467-12479}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "the Netherlands"}]


authors: Shenghao Fu, Qize Yang, Qijie Mo, Junkai Yan, Xihan Wei, Jingke Meng, Xiaohua Xie, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14987-14997
cities: ["Guangzhou", "Shenzhen", "Hangzhou", "Beijing", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_LLMDet_Learning_Strong_Open-Vocabulary_Object_Detectors_under_the_Supervision_of_CVPR_2025_paper.html

title:
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

abstract:
Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset will be available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_LLMDet_Learning_Strong_Open-Vocabulary_Object_Detectors_under_the_Supervision_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_LLMDet_Learning_Strong_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Shenghao and Yang, Qize and Mo, Qijie and Yan, Junkai and Wei, Xihan and Meng, Jingke and Xie, Xiaohua and Zheng, Wei-Shi},
    title     = {LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14987-14997}
}

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

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


title: Boost Your Human Image Generation Model via Direct Preference Optimization
authors: Sanghyeon Na, Yonggyu Kim, Hyunjoon Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23551-23562
raw_affiliation: [{"affiliation": "Kakao", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Kakao"]
cities: ["Seoul"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Na_Boost_Your_Human_Image_Generation_Model_via_Direct_Preference_Optimization_CVPR_2025_paper.html

abstract:
Human image generation is a key focus in image synthesis due to its broad applications, but even slight inaccuracies in anatomy, pose, or details can compromise realism. To address these challenges, we explore Direct Preference Optimization (DPO), which trains models to generate preferred (winning) images while diverging from non-preferred (losing) ones. However, conventional DPO methods use generated images as winning images, limiting realism. To overcome this limitation, we propose an enhanced DPO approach that incorporates high-quality real images as winning images, encouraging outputs to resemble real images rather than generated ones. However, implementing this concept is not a trivial task. Therefore, our approach, HG-DPO (Human image Generation through DPO), employs a novel curriculum learning framework that gradually improves the output of the model toward greater realism, making training more feasible. Furthermore, HG-DPO effectively adapts to personalized text-to-image tasks, generating high-quality and identity-specific images, which highlights the practical value of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Na_Boost_Your_Human_Image_Generation_Model_via_Direct_Preference_Optimization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Na_Boost_Your_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Na_2025_CVPR,
    author    = {Na, Sanghyeon and Kim, Yonggyu and Lee, Hyunjoon},
    title     = {Boost Your Human Image Generation Model via Direct Preference Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23551-23562}
}


title: Learning to Highlight Audio by Watching Movies
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23925-23935
affiliations: ["University of Rochester", "University of Maryland, College Park", "Meta Reality Labs Research"]
cities: ["Rochester", "College Park", "Menlo Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Learning_to_Highlight_Audio_by_Watching_Movies_CVPR_2025_paper.html

authors:
Chao Huang, Ruohan Gao, J. M. F. Tsang, Jan Kurcius, Cagdas Bilen, Chenliang Xu, Anurag Kumar, Sanjeel Parekh

abstract:
Recent years have seen a significant increase in video content creation and consumption. Crafting engaging content requires the careful curation of both visual and audio elements. While visual cue curation, through techniques like optimal viewpoint selection or post-editing, has been central to media production, its natural counterpart, audio, has not undergone equivalent advancements. This often results in a disconnect between visual and acoustic saliency. To bridge this gap, we introduce a novel task: visually-guided acoustic highlighting, which aims to transform audio to deliver appropriate highlighting effects guided by the accompanying video, ultimately creating a more harmonious audio-visual experience. We propose a flexible, transformer-based multimodal framework to solve this task. To train our model, we also introduce a new dataset--the muddy mix dataset, leveraging the meticulous audio and video crafting found in movies, which provides a form of free supervision. We develop a pseudo-data generation process to simulate poorly mixed audio, mimicking real-world scenarios through a three-step process---separation, adjustment, and remixing. Our approach consistently outperforms several baselines in both quantitative and subjective evaluation. We also systematically study the impact of different types of contextual guidance and difficulty levels of the dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Learning_to_Highlight_Audio_by_Watching_Movies_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Learning_to_Highlight_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Chao and Gao, Ruohan and Tsang, J. M. F. and Kurcius, Jan and Bilen, Cagdas and Xu, Chenliang and Kumar, Anurag and Parekh, Sanjeel},
    title     = {Learning to Highlight Audio by Watching Movies},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23925-23935}
}

raw_affiliation:
[{"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}, {"affiliation": "Meta Reality Labs Research", "city": "Menlo Park", "country": "USA"}]


title: Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling
authors: Guillem Capellera, Antonio Rubio, Luis Ferraz, Antonio Agudo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22476-22486
affiliations: ["Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC", "Kognia Sports Intelligence"]
cities: ["Barcelona", "Unknown"]
countries: ["Spain", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Capellera_Unified_Uncertainty-Aware_Diffusion_for_Multi-Agent_Trajectory_Modeling_CVPR_2025_paper.html

abstract:
Multi-agent trajectory modeling has primarily focused on forecasting future states, often overlooking broader tasks like trajectory completion, which are crucial for real-world applications such as correcting tracking data. Existing methods also generally predict agents' states without offering any state-wise measure of uncertainty. Moreover, popular multi-modal sampling methods lack any error probability estimates for each generated scene under the same prior observations, making it difficult to rank the predictions during inference time. We introduce U2Diff, a unified diffusion model designed to handle trajectory completion while providing state-wise uncertainty estimates jointly. This uncertainty estimation is achieved by augmenting the simple denoising loss with the negative log-likelihood of the predicted noise and propagating latent space uncertainty to the real state space. Additionally, we incorporate a Rank Neural Network in post-processing to enable error probability estimation for each generated mode, demonstrating a strong correlation with the error relative to ground truth. Our method outperforms the state-of-the-art solutions in trajectory completion and forecasting across four challenging sports datasets (NBA, Basketball-U, Football-U, Soccer-U), highlighting the effectiveness of uncertainty and error probability estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Capellera_Unified_Uncertainty-Aware_Diffusion_for_Multi-Agent_Trajectory_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Capellera_Unified_Uncertainty-Aware_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Capellera_2025_CVPR,
    author    = {Capellera, Guillem and Rubio, Antonio and Ferraz, Luis and Agudo, Antonio},
    title     = {Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22476-22486}
}

raw_affiliation:
[{"affiliation": "Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Kognia Sports Intelligence", "city": "Unknown", "country": "Unknown"}]


title: WeGen: A Unified Model for Interactive Multimodal Generation as We Chat
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23679-23689
cities: ["Hefei", "Shanghai", "Shenzhen", "Beijing", "Unknown"]
countries: ["China", "China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_WeGen_A_Unified_Model_for_Interactive_Multimodal_Generation_as_We_CVPR_2025_paper.html

authors:
Zhipeng Huang, Shaobin Zhuang, Canmiao Fu, Binxin Yang, Ying Zhang, Chong Sun, Zhizheng Zhang, Yali Wang, Chen Li, Zheng-Jun Zha

abstract:
Existing multimodal generative models fall short as qualified design copilots, as they often struggle to generate imaginative outputs once instructions are less detailed or lack the ability to maintain consistency with the provided references. In this work, we introduce WeGen, a model that unifies multimodal generation and understanding, and promotes their interplay in iterative generation. It can generate diverse results with high creativity for less detailed instructions. And it can progressively refine prior generation results or integrating specific contents from references following the instructions in its chat with users. During this process, it is capable of preserving consistency in the parts that the user is already satisfied with. To this end, we curate a large-scale dataset, extracted from Internet videos, containing rich object dynamics and auto-labeled dynamics descriptions by advanced foundation models to date. These two information are interleaved into a single sequence to enable WeGen to learn consistency-aware generation where the specified dynamics are generated while the consistency of unspecified content is preserved aligned with instructions.  Besides, we introduce a prompt self-rewriting mechanism to enhance generation diversity.  Extensive experiments demonstrate the effectiveness of unifying multimodal understanding and generation in WeGen and show it achieves state-of-the-art performance across various visual generation benchmarks. These also demonstrate the potential of WeGen as a user-friendly design copilot as desired.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_WeGen_A_Unified_Model_for_Interactive_Multimodal_Generation_as_We_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_WeGen_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhipeng and Zhuang, Shaobin and Fu, Canmiao and Yang, Binxin and Zhang, Ying and Sun, Chong and Zhang, Zhizheng and Wang, Yali and Li, Chen and Zha, Zheng-Jun},
    title     = {WeGen: A Unified Model for Interactive Multimodal Generation as We Chat},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23679-23689}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "WeChat Vision, Tencent Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Galbot", "city": "Unknown", "country": "Unknown"}]

affiliations:
["University of Science and Technology of China", "Shanghai Jiao Tong University", "WeChat Vision, Tencent Inc.", "Chinese Academy of Sciences", "Galbot"]


title: HRAvatar: High-Quality and Relightable Gaussian Head Avatar
authors: Dongbin Zhang, Yunfei Liu, Lijian Lin, Ye Zhu, Kangjie Chen, Minghan Qin, Yu Li, Haoqian Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26285-26296
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_HRAvatar_High-Quality_and_Relightable_Gaussian_Head_Avatar_CVPR_2025_paper.html

abstract:
Reconstructing animatable and high-quality 3D head avatars from monocular videos, especially with realistic relighting, is a valuable task. However, the limited information from single-view input, combined with the complex head poses and facial movements, makes this challenging. Previous methods achieve real-time performance by combining 3D Gaussian Splatting with a parametric head model, but the resulting head quality suffers from inaccurate face tracking and limited expressiveness of the deformation model. These methods also fail to produce realistic effects under novel lighting conditions. To address these issues, we propose HRAvatar, a 3DGS-based method that reconstructs high-fidelity, relightable 3D head avatars. HRAvatar reduces tracking errors through end-to-end optimization and better captures individual facial deformations using learnable blendshapes and learnable linear blend skinning. Additionally, it decomposes head appearance into several physical properties and incorporates physically-based shading to account for environmental lighting. Extensive experiments demonstrate that HRAvatar not only reconstructs superior-quality heads but also achieves realistic visual effects under varying lighting conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_HRAvatar_High-Quality_and_Relightable_Gaussian_Head_Avatar_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_HRAvatar_High-Quality_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Dongbin and Liu, Yunfei and Lin, Lijian and Zhu, Ye and Chen, Kangjie and Qin, Minghan and Li, Yu and Wang, Haoqian},
    title     = {HRAvatar: High-Quality and Relightable Gaussian Head Avatar},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26285-26296}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "International Digital Economy Academy (IDEA)", "city": "Shenzhen", "country": "China"}]

affiliations:
["Tsinghua Shenzhen International Graduate School, Tsinghua University", "International Digital Economy Academy (IDEA)"]


title: Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7685-7695
cities: ["Munich", "Munich", "Munich", "Stanford"]
countries: ["Germany", "Germany", "Germany", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yeganeh_Latent_Drifting_in_Diffusion_Models_for_Counterfactual_Medical_Image_Synthesis_CVPR_2025_paper.html

authors:
Yousef Yeganeh, Azade Farshad, Ioannis Charisiadis, Marta Hasny, Martin Hartenberger, Björn Ommer, Nassir Navab, Ehsan Adeli

abstract:
Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models; however, such large datasets are not always accessible in medical imaging due to cost and privacy issues, which contradicts one of the main applications of such models to produce synthetic samples where real data is scarce. Also, finetuning on pre-trained general models has been a challenge due to the distribution shift between the medical domain and the pre-trained models. Here, we propose Latent Drift (LD) for diffusion models that can be adopted for any fine-tuning method to mitigate the issues faced by the distribution shift or employed in inference time as a condition. Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation, which is crucial to investigate how parameters such as gender, age, and adding or removing diseases in a patient would alter the medical images. We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation. Our results demonstrate significant performance gains in various scenarios when combined with different fine-tuning schemes. The source code of this work is available on GitHub.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yeganeh_Latent_Drifting_in_Diffusion_Models_for_Counterfactual_Medical_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yeganeh_Latent_Drifting_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yeganeh_2025_CVPR,
    author    = {Yeganeh, Yousef and Farshad, Azade and Charisiadis, Ioannis and Hasny, Marta and Hartenberger, Martin and Ommer, Bj\"orn and Navab, Nassir and Adeli, Ehsan},
    title     = {Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7685-7695}
}

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

affiliations:
["Technical University of Munich", "Munich Center for Machine Learning", "CompVis @ LMU Munich", "Stanford University"]


authors: Yichen Xiao, Shuai Wang, Dehao Zhang, Wenjie Wei, Yimeng Shan, Xiaoli Liu, Yulin Jiang, Malu Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5444-5454
affiliations: ["University of Electronic Science and Technology of China", "Liaoning Technical University"]
cities: ["Chengdu", "Fuxin"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Rethinking_Spiking_Self-Attention_Mechanism_Implementing_a-XNOR_Similarity_Calculation_in_Spiking_CVPR_2025_paper.html

title:
Rethinking Spiking Self-Attention Mechanism: Implementing a-XNOR Similarity Calculation in Spiking Transformers

abstract:
Transformers significantly raise the performance limits across various tasks, spurring research into integrating them into spiking neural networks. However, a notable performance gap remains between existing spiking Transformers and their artificial neural network counterparts. Here, we first analyze the cause of this gap and attribute it to the dot product's ineffectiveness in measuring similarity between spiking queries and keys, due to numerous non-spiking events. To address this, we propose a novel a-XNOR similarity measure tailored for spike trains. It redefines the correlation between non-spike pairs as a specific value a, effectively overcoming the limitations of dot-product similarity. Furthermore, considering the sparse nature of spike trains where spikes carry more information than non-spikes, the a-XNOR similarity correspondingly highlights the distinct importance of spikes over non-spikes. Extensive experiments demonstrate that a-XNOR similarity significantly improves performance across different spiking Transformer architectures on various static and neuromorphic datasets, further revealing the potential of spiking Transformers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Rethinking_Spiking_Self-Attention_Mechanism_Implementing_a-XNOR_Similarity_Calculation_in_Spiking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Rethinking_Spiking_Self-Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Yichen and Wang, Shuai and Zhang, Dehao and Wei, Wenjie and Shan, Yimeng and Liu, Xiaoli and Jiang, Yulin and Zhang, Malu},
    title     = {Rethinking Spiking Self-Attention Mechanism: Implementing a-XNOR Similarity Calculation in Spiking Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5444-5454}
}

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


title: MagicQuill: An Intelligent Interactive Image Editing System
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13072-13082
affiliations: ["HKUST", "Ant Group", "ZJU", "HKU"]
cities: ["Hong Kong", "Hangzhou", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MagicQuill_An_Intelligent_Interactive_Image_Editing_System_CVPR_2025_paper.html

authors:
Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, Yujun Shen

abstract:
As a highly practical application, image editing encounters a variety of user demands and thus prioritizes excellent ease of use. In this paper, we unveil MagicQuill, an integrated image editing system designed to support users in swiftly actualizing their creativity. Our system starts with a streamlined yet functionally robust interface, enabling users to articulate their ideas (e.g., inserting elements, erasing objects, altering color, etc.) with just a few strokes. These interactions are then monitored by a multimodal large language model (MLLM) to anticipate user intentions in real time, bypassing the need for prompt entry. Finally, we apply the powerful diffusion prior, enhanced by a carefully learned two-branch plug-in module, to process the editing request with precise control. Please visit https://magic-quill.github.io to try out our system.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MagicQuill_An_Intelligent_Interactive_Image_Editing_System_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MagicQuill_An_Intelligent_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zichen and Yu, Yue and Ouyang, Hao and Wang, Qiuyu and Cheng, Ka Leong and Wang, Wen and Liu, Zhiheng and Chen, Qifeng and Shen, Yujun},
    title     = {MagicQuill: An Intelligent Interactive Image Editing System},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13072-13082}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "ZJU", "city": "Hangzhou", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}]


title: HeMoRa: Unsupervised Heuristic Consensus Sampling for Robust Point Cloud Registration
authors: Shaocheng Yan, Yiming Wang, Kaiyan Zhao, Pengcheng Shi, Zhenjun Zhao, Yongjun Zhang, Jiayuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1363-1373
cities: ["Wuhan", "Macau", "Wuhan", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_HeMoRa_Unsupervised_Heuristic_Consensus_Sampling_for_Robust_Point_Cloud_Registration_CVPR_2025_paper.html

abstract:
Heuristic information for consensus set sampling is essential for correspondence-based point cloud registration, but existing approaches typically rely on supervised learning or expert-driven parameter tuning. In this work, we propose HeMoRa, a new unsupervised framework that trains a Heuristic information Generator (HeGen) to estimate sampling probabilities for correspondences using a Multi-order Reward Aggregator (MoRa) loss. The core of MoRa is to train HeGen through extensive trials and feedback, enabling unsupervised learning. While this process can be implemented using policy optimization, directly applying the policy gradient to optimize HeGen presents challenges such as sensitivity to noise and low reward efficiency. To address these issues, we propose a Maximal Reward Propagation (MRP) mechanism that enhances the training process by prioritizing noise-free signals and improving reward utilization. Experimental results show that equipped with HeMoRa, the consensus set sampler achieves improvements in both robustness and accuracy. For example, on the 3DMatch dataset with FCGF feature, the registration recall of our unsupervised methods (Ours+SM and Ours+SC^2) even outperforms the state-of-the-art supervised method VBreg. Our code is available at \href https://github.com/Laka-3DV/HeMoRa  \texttt HeMoRa  .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_HeMoRa_Unsupervised_Heuristic_Consensus_Sampling_for_Robust_Point_Cloud_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_HeMoRa_Unsupervised_Heuristic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Shaocheng and Wang, Yiming and Zhao, Kaiyan and Shi, Pengcheng and Zhao, Zhenjun and Zhang, Yongjun and Li, Jiayuan},
    title     = {HeMoRa: Unsupervised Heuristic Consensus Sampling for Robust Point Cloud Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1363-1373}
}

raw_affiliation:
[{"affiliation": "School of Remote Sensing and Information Engineering, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "State Key Laboratory of Internet of Things for Smart City, University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Mechanical and Automation Engineering, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Remote Sensing and Information Engineering, Wuhan University", "State Key Laboratory of Internet of Things for Smart City, University of Macau", "School of Computer Science, Wuhan University", "Mechanical and Automation Engineering, The Chinese University of Hong Kong"]


title: Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds
authors: Huitong Chen, Yu Wang, Yan Fan, Guosong Jiang, Qinghua Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10121-10130
cities: ["Tianjin", "Tianjin"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Reducing_Class-wise_Confusion_for_Incremental_Learning_with_Disentangled_Manifolds_CVPR_2025_paper.html

abstract:
Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use prototypes of classes during incremental updates. Despite simplicity and intuition, we find that such methods suffer from inadequate representation capability and unsatisfied feature overlap. These two factors cause class-wise confusion and limited performance. In this paper, we develop a Confusion-REduced AuTo-Encoder classifier (CREATE) for CIL. Specifically, our method employs a lightweight auto-encoder module to learn compact manifold for each class in the latent subspace, constraining samples to be well reconstructed only on the semantically correct auto-encoder. Thus, the representation stability and capability of class distributions are enhanced, alleviating the potential class-wise confusion problem. To further distinguish the overlapped features, we propose a confusion-aware latent space separation loss that ensures samples are closely distributed in their corresponding low-dimensional manifold while keeping away from the distributions of drifted features from other classes. Our method demonstrates stronger representational capacity and discrimination ability by learning disentangled manifolds and reduces class confusion. Extensive experiments on multiple datasets and settings show that CREATE outperforms other state-of-the-art methods up to 5.41%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Reducing_Class-wise_Confusion_for_Incremental_Learning_with_Disentangled_Manifolds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Reducing_Class-wise_Confusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Huitong and Wang, Yu and Fan, Yan and Jiang, Guosong and Hu, Qinghua},
    title     = {Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10121-10130}
}

raw_affiliation:
[{"affiliation": "Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University, China", "city": "Tianjin", "country": "China"}, {"affiliation": "Haihe Laboratory of Information Technology Application Innovation (Haihe Lab of ITAI), Tianjin, China", "city": "Tianjin", "country": "China"}]

affiliations:
["Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University, China", "Haihe Laboratory of Information Technology Application Innovation (Haihe Lab of ITAI), Tianjin, China"]


title: Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19401-19413
cities: ["Beijing", "Zurich", "Saarbr\u00fccken", "Redmond", "Stanford"]
countries: ["China", "Switzerland", "Germany", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Open-Vocabulary_Functional_3D_Scene_Graphs_for_Real-World_Indoor_Spaces_CVPR_2025_paper.html

authors:
Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida Zhang, Xiangyang Ji, Marc Pollefeys, Francis Engelmann

abstract:
We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial relationships of objects, functional 3D scene graphs capture objects, interactive elements, and their functional relationships. Due to the lack of training data, we leverage foundation models, including visual language models (VLMs) and large language models (LLMs), to encode functional knowledge. We evaluate our approach on an extended SceneFun3D dataset and a newly collected dataset, FunGraph3D, both annotated with functional 3D scene graphs. Our method significantly outperforms adapted baselines, including Open3DSG and ConceptGraph, demonstrating its effectiveness in modeling complex scene functionalities. We also demonstrate downstream applications such as 3D question answering and robotic manipulation using functional 3D scene graphs. See our project page at https://openfungraph.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Open-Vocabulary_Functional_3D_Scene_Graphs_for_Real-World_Indoor_Spaces_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Open-Vocabulary_Functional_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chenyangguang and Delitzas, Alexandros and Wang, Fangjinhua and Zhang, Ruida and Ji, Xiangyang and Pollefeys, Marc and Engelmann, Francis},
    title     = {Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19401-19413}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "ETH Z\n\n\"urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "MPI for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]

affiliations:
["Tsinghua University", "ETH Z\n\n\"urich", "MPI for Informatics", "Microsoft", "Stanford University"]


title: Boosting Adversarial Transferability through Augmentation in Hypothesis Space
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19175-19185
cities: ["Xiamen", "Xiamen", "Xiamen", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Boosting_Adversarial_Transferability_through_Augmentation_in_Hypothesis_Space_CVPR_2025_paper.html

authors:
Yu Guo, Weiquan Liu, Qingshan Xu, Shijun Zheng, Shujun Huang, Yu Zang, Siqi Shen, Chenglu Wen, Cheng Wang

abstract:
Adversarial examples can mislead deep neural networks with subtle perturbations, causing them to make incorrect predictions. Notably, adversarial examples crafted for one model can also deceive other models, a phenomenon known as the transferability of adversarial examples. To improve transferability, existing studies have designed increasingly complex mechanisms, but the improvements achieved remain relatively limited and are often difficult to adapt to other modalities, further restricting the scalability of these methods. In this work, we observe a mirroring relationship between model generalization and adversarial example transferability. Motivated by this observation, we propose an augmentation-based attack, called OPS (Operator-Perturbation-based Stochastic optimization), which constructs a stochastic optimization problem by input transformation operators and random perturbations, and solves this problem to generate adversarial examples with better transferability. Extensive experiments on both images and 3D point clouds demonstrate that OPS significantly outperforms existing state-of-the-art methods in terms of both performance and cost, showcasing the universality and superiority of our approach. The code is available at https://github.com/the-full/OPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Boosting_Adversarial_Transferability_through_Augmentation_in_Hypothesis_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Boosting_Adversarial_Transferability_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yu and Liu, Weiquan and Xu, Qingshan and Zheng, Shijun and Huang, Shujun and Zang, Yu and Shen, Siqi and Wen, Chenglu and Wang, Cheng},
    title     = {Boosting Adversarial Transferability through Augmentation in Hypothesis Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19175-19185}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "College of Computer Engineering, Jimei University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, China", "College of Computer Engineering, Jimei University, China", "Nanyang Technological University, Singapore"]


title: AniMo: Species-Aware Model for Text-Driven Animal Motion Generation
authors: Xuan Wang, Kai Ruan, Xing Zhang, Gaoang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1929-1939
affiliations: ["Zhejiang University", "Renmin University of China", "New Hope Liuhe Co., Ltd."]
cities: ["Hangzhou", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AniMo_Species-Aware_Model_for_Text-Driven_Animal_Motion_Generation_CVPR_2025_paper.html

abstract:
Text-driven motion generation has made significant strides in recent years. However, most existing works focus on human motion, largely overlooking the rich and diverse behaviors of animals. Understanding and synthesizing animal motion have important applications in wildlife conservation, animal ecology, and biomechanics. Animal motion modeling presents unique challenges due to species diversity, varied morphological structures, and different behavioral patterns in response to similar textual descriptions. To address these challenges, we propose AniMo for text-driven animal motion generation. AniMo consists of two stages: motion tokenization and text-to-motion generation. In the motion tokenization stage, we encode motions using a joint-aware spatiotemporal encoder with species-aware feature modulation, enabling the model to adapt to diverse skeletal structures across species. In the text-to-motion generation stage, we employ masked modeling to jointly learn the mappings between textual descriptions and motion tokens. Additionally, we introduce AniMo4D, a large-scale dataset containing 78,149 motion sequences and 185,435 textual descriptions across 114 animal species. Experimental results show that AniMo achieves superior performance on both the AniMo4D and AnimalML3D datasets, effectively capturing diverse morphological structures and behavioral patterns across animal species.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AniMo_Species-Aware_Model_for_Text-Driven_Animal_Motion_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AniMo_Species-Aware_Model_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xuan and Ruan, Kai and Zhang, Xing and Wang, Gaoang},
    title     = {AniMo: Species-Aware Model for Text-Driven Animal Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1929-1939}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "New Hope Liuhe Co., Ltd.", "city": "Beijing", "country": "China"}]


title: EditAR: Unified Conditional Generation with Autoregressive Models
authors: Jiteng Mu, Nuno Vasconcelos, Xiaolong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7899-7909
affiliations: ["UC San Diego", "NVIDIA"]
cities: ["San Diego", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mu_EditAR_Unified_Conditional_Generation_with_Autoregressive_Models_CVPR_2025_paper.html

abstract:
Recent progress in controllable image generation and editing is largely driven by diffusion-based methods. Although diffusion models perform exceptionally well in specific tasks with tailored designs, establishing a unified model is still challenging. In contrast, autoregressive models inherently feature a unified tokenized representation, which simplifies the creation of a single foundational model for various tasks. In this work, we propose EditAR, a single unified autoregressive framework for a variety of conditional image generation tasks, e.g., image editing, depth-to-image, edge-to-image, segmentation-to-image. The model takes both images and instructions as inputs, and predicts the edited images tokens in a vanilla next-token paradigm. To enhance the text-to-image alignment, we further propose to distill the knowledge from foundation models into the autoregressive modeling process. We evaluate its effectiveness across diverse tasks on established benchmarks, showing competitive performance to various state-of-the-art task-specific methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mu_EditAR_Unified_Conditional_Generation_with_Autoregressive_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mu_EditAR_Unified_Conditional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mu_2025_CVPR,
    author    = {Mu, Jiteng and Vasconcelos, Nuno and Wang, Xiaolong},
    title     = {EditAR: Unified Conditional Generation with Autoregressive Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7899-7909}
}

raw_affiliation:
[{"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Instance-wise Supervision-level Optimization in Active Learning
authors: Shinnosuke Matsuo, Riku Togashi, Ryoma Bise, Seiichi Uchida, Masahiro Nomura
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4939-4947
affiliations: ["Kyushu University", "CyberAgent, Inc."]
cities: ["Fukuoka", "Tokyo"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Matsuo_Instance-wise_Supervision-level_Optimization_in_Active_Learning_CVPR_2025_paper.html

abstract:
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness can be further enhanced by incorporating weak supervision, which uses rough yet cost-effective annotations instead of exact (i.e., full) but expensive annotations. We introduce a novel AL framework, Instance-wise Supervision-Level Optimization (ISO), which not only selects the instances to annotate but also determines their optimal annotation level within a fixed annotation budget. Its optimization criterion leverages the value-to-cost ratio (VCR) of each instance while ensuring diversity among the selected instances. In classification experiments, ISO consistently outperforms traditional AL methods and surpasses a state-of-the-art AL approach that combines full and weak supervision, achieving higher accuracy at a lower overall cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Matsuo_Instance-wise_Supervision-level_Optimization_in_Active_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Matsuo_Instance-wise_Supervision-level_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Matsuo_2025_CVPR,
    author    = {Matsuo, Shinnosuke and Togashi, Riku and Bise, Ryoma and Uchida, Seiichi and Nomura, Masahiro},
    title     = {Instance-wise Supervision-level Optimization in Active Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4939-4947}
}

raw_affiliation:
[{"affiliation": "Kyushu University", "city": "Fukuoka", "country": "Japan"}, {"affiliation": "CyberAgent, Inc.", "city": "Tokyo", "country": "Japan"}]


authors: Hala Djeghim, Nathan Piasco, Moussab Bennehar, Luis Roldao, Dzmitry Tsishkou, Désiré Sidibé
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11854-11863
cities: ["Paris", "Evry"]
countries: ["France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Djeghim_ViiNeuS_Volumetric_Initialization_for_Implicit_Neural_Surface_Reconstruction_of_Urban_CVPR_2025_paper.html

title:
ViiNeuS: Volumetric Initialization for Implicit Neural Surface Reconstruction of Urban Scenes with Limited Image Overlap

abstract:
Neural implicit surface representation methods have recently shown impressive 3D reconstruction results. However, existing solutions struggle to reconstruct driving scenes due to their large size, highly complex nature and limited visual observation overlap.Hence, to achieve accurate reconstructions, additional supervision data such as LiDAR, strong geometric priors, and long training times are required.To tackle such limitations, we present ViiNeuS, a new hybrid implicit surface learning method that efficiently initializes the signed distance field to reconstruct large driving scenes from 2D street view images.ViiNeuS's hybrid architecture models two separate implicit fields: one representing the volumetric density of the scene, and another one representing the signed distance to the surface.To accurately reconstruct urban outdoor driving scenarios, we introduce a novel volume-rendering strategy that relies on self-supervised probabilistic density estimation to sample points near the surface and transition progressively from volumetric to surface representation. Our solution permits a proper and fast initialization of the signed distance field without relying on any geometric prior on the scene, compared to concurrent methods.By conducting extensive experiments on four outdoor driving datasets, we show that ViiNeuS can learn an accurate and detailed 3D surface scene representation in various driving scenarios while being two times faster to train compared to previous state-of-the-art solutions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Djeghim_ViiNeuS_Volumetric_Initialization_for_Implicit_Neural_Surface_Reconstruction_of_Urban_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Djeghim_ViiNeuS_Volumetric_Initialization_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Djeghim_2025_CVPR,
    author    = {Djeghim, Hala and Piasco, Nathan and Bennehar, Moussab and Roldao, Luis and Tsishkou, Dzmitry and Sidib\'e, D\'esir\'e},
    title     = {ViiNeuS: Volumetric Initialization for Implicit Neural Surface Reconstruction of Urban Scenes with Limited Image Overlap},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11854-11863}
}

raw_affiliation:
[{"affiliation": "Noah\u2019s Ark, Huawei Paris Research Center, France", "city": "Paris", "country": "France"}, {"affiliation": "IBISC, Univ. Evry Paris-Saclay , Evry, France", "city": "Evry", "country": "France"}]

affiliations:
["Noah\u2019s Ark, Huawei Paris Research Center, France", "IBISC, Univ. Evry Paris-Saclay , Evry, France"]


title: Model Diagnosis and Correction via Linguistic and Implicit Attribute Editing
authors: Xuanbai Chen, Xiang Xu, Zhihua Li, Tianchen Zhao, Pietro Perona, Qin Zhang, Yifan Xing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14281-14292
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/Chen_Model_Diagnosis_and_Correction_via_Linguistic_and_Implicit_Attribute_Editing_CVPR_2025_paper.html

abstract:
How can we troubleshoot a deep visual model, i.e., understand why it makes certain mistakes and further take action to correct its behavior? We design a Model Diagnosis and Correction system (MDC), an automated framework that analyzes the pattern of errors, proposes candidate causes of attributes, conducts hypothesis testing via attribute editing, and ultimately generates counterfactual training samples to improve the performance of the model. Unlike previous methods, in addition to the linguistic attributes, our method also incorporates the analysis for implicit causal attributes, those cannot to be accurately described by language. To achieve this, we propose an image editing module capable of leveraging both implicit and linguistic attributes to generate counterfactual images depicting error patterns and further experimentally validate causality relationships. Lastly, we enrich the training set with synthetic samples depicting verified causal attributes and retrain the model, further boosting accuracy and robustness. Extensive experiments on fine-grained classification and face security applications demonstrate the superiority of our approach in model diagnosis and correction. Specifically, we achieve an average relative improvement of 62.01% in HTER for face security application over state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Model_Diagnosis_and_Correction_via_Linguistic_and_Implicit_Attribute_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Model_Diagnosis_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xuanbai and Xu, Xiang and Li, Zhihua and Zhao, Tianchen and Perona, Pietro and Zhang, Qin and Xing, Yifan},
    title     = {Model Diagnosis and Correction via Linguistic and Implicit Attribute Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14281-14292}
}


title: BHViT: Binarized Hybrid Vision Transformer
authors: Tian Gao, Yu Zhang, Zhiyuan Zhang, Huajun Liu, Kaijie Yin, Chengzhong Xu, Hui Kong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3563-3572
cities: ["Nanjing", "Macau", "Shanghai", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_BHViT_Binarized_Hybrid_Vision_Transformer_CVPR_2025_paper.html

abstract:
Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision Transformers (ViTs) on edge devices. However, due to the structural differences between CNN and Transformer architectures, simply applying binary CNN strategies to the ViT models will lead to a significant performance drop. To tackle this challenge, we propose BHViT, a binarization-friendly hybrid ViT architecture, and its full binarization model with the guidance of three important observations. Initially, BHViT utilizes the local information interaction and hierarchical feature aggregation technique from coarse to fine levels to address redundant computations stemming from excessive tokens. Then, a novel module based on shift operations is proposed to enhance the performance of the binary Multi-Layer Perceptron (MLP) module without significantly increasing computational overhead. In addition, an innovative attention matrix binarization method based on quantization decomposition is proposed to evaluate the token's importance in the binarized attention matrix. Finally, we propose a regularization loss to address the inadequate optimization caused by the incompatibility between the weight oscillation in the binary layers and the Adam Optimizer. Extensive experimental results demonstrate that our proposed algorithm achieves SOTA performance among binary ViT methods. The source code is released at: https://github.com/IMRL/BHViT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_BHViT_Binarized_Hybrid_Vision_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_BHViT_Binarized_Hybrid_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Tian and Zhang, Yu and Zhang, Zhiyuan and Liu, Huajun and Yin, Kaijie and Xu, Chengzhong and Kong, Hui},
    title     = {BHViT: Binarized Hybrid Vision Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3563-3572}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Nanjing University of Science and Technology", "University of Macau", "Shanghai Jiaotong University", "Singapore Management University"]


title: UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation
authors: Himangi Mittal, Peiye Zhuang, Hsin-Ying Lee, Shubham Tulsiani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16208-16218
affiliations: ["Carnegie Mellon University", "Snap Inc."]
cities: ["Pittsburgh", "Santa Monica"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mittal_UniPhy_Learning_a_Unified_Constitutive_Model_for_Inverse_Physics_Simulation_CVPR_2025_paper.html

abstract:
We propose UniPhy, a common latent-conditioned neural constitutive model that can encode the physical properties of diverse materials. At inference UniPhy allows `inverse simulation' i.e. inferring material properties by optimizing the scene-specific latent to match the available observations via differentiable simulation. In contrast to existing methods that treat such inference as system identification, UniPhy does not rely on user-specified material information. Compared to prior neural constitutive modeling approaches which learn instance specific networks, the shared training across materials improves both, robustness and accuracy of the estimates. We train UniPhy using simulated trajectories across diverse geometries and materials -- elastic, plasticine, sand, and fluids (Newtonian & non-Newtonian). At inference, given an object with unknown material properties, UniPhy can infer the material properties via latent optimization to match the motion observations, and can then allow re-simulating the object under diverse scenarios. We compare UniPhy against prior inverse simulation methods, and show that the inference from UniPhy enables more accurate replay and re-simulation under novel conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mittal_UniPhy_Learning_a_Unified_Constitutive_Model_for_Inverse_Physics_Simulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mittal_UniPhy_Learning_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mittal_2025_CVPR,
    author    = {Mittal, Himangi and Zhuang, Peiye and Lee, Hsin-Ying and Tulsiani, Shubham},
    title     = {UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16208-16218}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "USA"}]


title: STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks
authors: Tianqing Zhang, Kairong Yu, Xian Zhong, Hongwei Wang, Qi Xu, Qiang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13959-13969
affiliations: ["Zhejiang University", "Dalian University of Technology", "Wuhan University of Technology"]
cities: ["Hangzhou", "Dalian", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_STAA-SNN_Spatial-Temporal_Attention_Aggregator_for_Spiking_Neural_Networks_CVPR_2025_paper.html

abstract:
Spiking Neural Networks (SNNs) have gained significant attention due to their biological plausibility and energy efficiency, making them promising alternatives to Artificial Neural Networks (ANNs). However, the performance gap between SNNs and ANNs remains a substantial challenge hindering the widespread adoption of SNNs. In this paper, we propose a Spatial-Temporal Attention Aggregator SNN (STAA-SNN) framework, which dynamically focuses on and captures both spatial and temporal dependencies. First, we introduce a spike-driven self-attention mechanism specifically designed for SNNs. Additionally, we pioneeringly incorporate position encoding to integrate latent temporal relationships into the incoming features. For spatial-temporal information aggregation, we employ step attention to selectively amplify relevant features to variant steps. Finally, we implement a time-step random dropout strategy to avoid local optima. The framework demonstrates exceptional performance across diverse datasets and exhibits strong generalization capabilities. Notably, STAA-SNN achieves state-of-the-art results on neuromorphic datasets CIFAR10-DVS of 82.10% and with performances of 97.14%, 82.05% and 70.40% on the static datasets CIFAR-10, CIFAR-100 and ImageNet, respectively. Furthermore, this model exhibits improved performance ranging from 0.33% to 2.80% with fewer time steps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_STAA-SNN_Spatial-Temporal_Attention_Aggregator_for_Spiking_Neural_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_STAA-SNN_Spatial-Temporal_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Tianqing and Yu, Kairong and Zhong, Xian and Wang, Hongwei and Xu, Qi and Zhang, Qiang},
    title     = {STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13959-13969}
}

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


title: Pathways on the Image Manifold: Image Editing via Video Generation
authors: Noam Rotstein, Gal Yona, Daniel Silver, Roy Velich, David Bensaid, Ron Kimmel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7857-7866
affiliations: ["Technion - Israel Institute of Technology"]
cities: ["Haifa"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rotstein_Pathways_on_the_Image_Manifold_Image_Editing_via_Video_Generation_CVPR_2025_paper.html

abstract:
Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image's key aspects. Our approach achieves state-of-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. Visit our project page: https://rotsteinnoam.github.io/Frame2Frame/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rotstein_Pathways_on_the_Image_Manifold_Image_Editing_via_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rotstein_Pathways_on_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rotstein_2025_CVPR,
    author    = {Rotstein, Noam and Yona, Gal and Silver, Daniel and Velich, Roy and Bensaid, David and Kimmel, Ron},
    title     = {Pathways on the Image Manifold: Image Editing via Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7857-7866}
}

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


title: DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering
authors: Yihao Wang, Marcus Klasson, Matias Turkulainen, Shuzhe Wang, Juho Kannala, Arno Solin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 722-732
affiliations: ["Technical University of Munich", "Aalto University", "University of Oulu"]
cities: ["Munich", "Espoo", "Oulu"]
countries: ["Germany", "Finland", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DeSplat_Decomposed_Gaussian_Splatting_for_Distractor-Free_Rendering_CVPR_2025_paper.html

abstract:
Gaussian splatting enables fast novel view synthesis in static 3D environments. However, reconstructing real-world environments remains challenging as distractors or occluders break the multi-view consistency assumption required for accurate 3D reconstruction. Most existing methods rely on external semantic information from pre-trained models, introducing additional computational overhead as pre-processing steps or during optimization. In this work, we propose a novel method, DeSplat, that directly separates distractors and static scene elements purely based on volume rendering of Gaussian primitives. We initialize Gaussians within each camera view for reconstructing the view-specific distractors to separately model the static 3D scene and distractors in the alpha compositing stages. DeSplat yields an explicit scene separation of static elements and distractors, achieving comparable results to prior distractor-free approaches without sacrificing rendering speed. We demonstrate DeSplat's effectiveness on three benchmark data sets for distractor-free novel view synthesis.  See the project website at https://aaltoml.github.io/desplat/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DeSplat_Decomposed_Gaussian_Splatting_for_Distractor-Free_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DeSplat_Decomposed_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yihao and Klasson, Marcus and Turkulainen, Matias and Wang, Shuzhe and Kannala, Juho and Solin, Arno},
    title     = {DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {722-732}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Aalto University", "city": "Espoo", "country": "Finland"}, {"affiliation": "University of Oulu", "city": "Oulu", "country": "Finland"}]


title: Knowledge Memorization and Rumination for Pre-trained Model-based Class-Incremental Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20523-20533
cities: ["Changsha", "Changsha", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Knowledge_Memorization_and_Rumination_for_Pre-trained_Model-based_Class-Incremental_Learning_CVPR_2025_paper.html

authors:
Zijian Gao, Wangwang Jia, Xingxing Zhang, Dulan Zhou, Kele Xu, Feng Dawei, Yong Dou, Xinjun Mao, Huaimin Wang

abstract:
Class-Incremental Learning (CIL) enables models to continuously learn new classes while mitigating catastrophic forgetting. Recently, Pre-Trained Models (PTMs) have greatly enhanced CIL performance, even when fine-tuning is limited to the first task. This advantage is particularly beneficial for CIL methods that freeze the feature extractor after first-task fine-tuning, such as analytic learning-based approaches using a least squares solution-based classification head to acquire knowledge recursively. In this work, we revisit the analytical learning approach combined with PTMs and identify its limitations in adapting to new classes, leading to sub-optimal performance. To address this, we propose the Momentum-based Analytical Learning (MoAL) approach. MoAL achieves robust knowledge memorization via an analytical classification head and improves adaptivity to new classes through momentum-based adapter weight interpolation, leading to forgetting outdated knowledge. Importantly, we introduce a knowledge rumination mechanism that leverages refined adaptivity, allowing the model to revisit and reinforce old knowledge, thereby improving performance on old classes. MoAL facilitates the acquisition of new knowledge and consolidates old knowledge, achieving a win-win outcome between plasticity and stability. Extensive experiments on various incremental settings show MoAL's state-of-the-art performance

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Knowledge_Memorization_and_Rumination_for_Pre-trained_Model-based_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Knowledge_Memorization_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Zijian and Jia, Wangwang and Zhang, Xingxing and Zhou, Dulan and Xu, Kele and Dawei, Feng and Dou, Yong and Mao, Xinjun and Wang, Huaimin},
    title     = {Knowledge Memorization and Rumination for Pre-trained Model-based Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20523-20533}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "State Key Laboratory of Complex & Critical Software Environment", "city": "Changsha", "country": "China"}, {"affiliation": "School of Computer Science, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["College of Computer Science and Technology, National University of Defense Technology", "State Key Laboratory of Complex & Critical Software Environment", "School of Computer Science, Tsinghua University"]


title: A Distractor-Aware Memory for Visual Object Tracking with SAM2
authors: Jovana Videnovic, Alan Lukezic, Matej Kristan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24255-24264
affiliations: ["Faculty of Computer and Information Science, University of Ljubljana, Slovenia"]
cities: ["Ljubljana"]
countries: ["Slovenia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Videnovic_A_Distractor-Aware_Memory_for_Visual_Object_Tracking_with_SAM2_CVPR_2025_paper.html

abstract:
Memory-based trackers are video object segmentation methods that form the target model by concatenating recently tracked frames into a memory buffer and localize the target by attending the current image to the buffered frames. While already achieving top performance on many benchmarks, it was the recent release of SAM2 that placed memory-based trackers into focus of the visual object tracking community. Nevertheless, modern trackers still struggle in the presence of distractors. We argue that a more sophisticated memory model is required, and propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness. The resulting tracker is denoted as DAM4SAM. We also propose a new distractor-distilled DiDi dataset to study the distractor problem better. DAM4SAM outperforms SAM2.1 and related SAM memory extensions on seven benchmarks and sets a solid new state-of-the-art on six of them.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Videnovic_A_Distractor-Aware_Memory_for_Visual_Object_Tracking_with_SAM2_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Videnovic_A_Distractor-Aware_Memory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Videnovic_2025_CVPR,
    author    = {Videnovic, Jovana and Lukezic, Alan and Kristan, Matej},
    title     = {A Distractor-Aware Memory for Visual Object Tracking with SAM2},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24255-24264}
}

raw_affiliation:
[{"affiliation": "Faculty of Computer and Information Science, University of Ljubljana, Slovenia", "city": "Ljubljana", "country": "Slovenia"}]


