# papers

title: Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images
authors: Yasamin Medghalchi, Moein Heidari, Clayton Allard, Leonid Sigal, Ilker Hacihaliloglu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28564-28574
affiliations: ["University of British Columbia", "Vector Institute for AI"]
cities: ["Vancouver", "Toronto"]
countries: ["Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Medghalchi_Prompt2Perturb_P2P_Text-Guided_Diffusion-Based_Adversarial_Attack_on_Breast_Ultrasound_Images_CVPR_2025_paper.html

abstract:
Deep neural networks (DNNs)  offer significant promise for improving breast cancer diagnosis in medical imaging. However, these models are highly susceptible to adversarial attacks--small, imperceptible changes that can mislead classifiers--raising critical concerns about their reliability and security. Traditional attack methods typically either require substantial extra data for malicious model pre-training, or involve a fixed norm perturbation budget, which does not align with human perception of these alterations. In medical imaging, however, this is often unfeasible due to the limited availability of datasets. Building on recent advancements in learnable prompts, we propose Prompt2Perturb (P2P), a novel language-guided semantic attack method capable of generating meaningful perturbations driven by text instructions. During the prompt learning phase, our approach leverages learnable prompts within the text encoder to create subtle, yet impactful, perturbations that remain imperceptible while guiding the model towards targeted outcomes.In contrast to current prompt learning-based approaches, our P2P stands out by directly updating text embeddings, avoiding the need for retraining diffusion models or using large pre-trained models which is typically infeasible in medical domain. Further, we leverage the finding that optimizing only the early reverse diffusion steps boosts efficiency while ensuring that the generated adversarial examples incorporate subtle low-frequency noise, thus preserving ultrasound image quality without introducing noticeable artifacts. We show that our method outperforms state-of-the-art attack techniques across three breast ultrasound datasets. Moreover, the generated images are both more natural in appearance and more effective compared to existing adversarial attacks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Medghalchi_Prompt2Perturb_P2P_Text-Guided_Diffusion-Based_Adversarial_Attack_on_Breast_Ultrasound_Images_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Medghalchi_2025_CVPR,
    author    = {Medghalchi, Yasamin and Heidari, Moein and Allard, Clayton and Sigal, Leonid and Hacihaliloglu, Ilker},
    title     = {Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28564-28574}
}

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


title: Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
authors: Qiang Wang, Xiang Song, Yuhang He, Jizhou Han, Chenhao Ding, Xinyuan Gao, Yihong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4839-4849
affiliations: ["Xi\u2019an Jiaotong University", "Shenzhen University of Advanced Technology"]
cities: ["Xi'an", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Boosting_Domain_Incremental_Learning_Selecting_the_Optimal_Parameters_is_All_CVPR_2025_paper.html

abstract:
Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by enabling continuous model adaptation, with Parameter-Isolation DIL (PIDIL) emerging as a promising paradigm to reduce knowledge conflicts. However, existing PIDIL methods struggle with parameter selection accuracy, especially as the number of domains and corresponding classes grows. To address this, we propose SOYO, a lightweight framework that improves domain selection in PIDIL. SOYO introduces a Gaussian Mixture Compressor (GMC) and Domain Feature Resampler (DFR) to store and balance prior domain data efficiently, while a Multi-level Domain Feature Fusion Network (MDFN) enhances domain feature extraction. Our framework supports multiple Parameter-Efficient Fine-Tuning (PEFT) methods and is validated across tasks such as image classification, object detection, and speech enhancement. Experimental results on six benchmarks demonstrate SOYO's consistent superiority over existing baselines, showcasing its robustness and adaptability in complex, evolving environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Boosting_Domain_Incremental_Learning_Selecting_the_Optimal_Parameters_is_All_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Boosting_Domain_Incremental_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qiang and Song, Xiang and He, Yuhang and Han, Jizhou and Ding, Chenhao and Gao, Xinyuan and Gong, Yihong},
    title     = {Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4839-4849}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}]


title: COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection
authors: Jinqi Xiao, Shen Sang, Tiancheng Zhi, Jing Liu, Qing Yan, Linjie Luo, Bo Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30116-30126
affiliations: ["ByteDance Inc.", "Rutgers University"]
cities: ["Beijing", "New Brunswick"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_COAP_Memory-Efficient_Training_with_Correlation-Aware_Gradient_Projection_CVPR_2025_paper.html

abstract:
Training large-scale neural networks in vision, and multimodal domains demands substantial memory resources, primarily due to the storage of optimizer states. While LoRA, a popular parameter-efficient method, reduces memory usage, it often suffers from suboptimal performance due to the constraints of low-rank updates. Low-rank gradient projection methods (e.g., GaLore, Flora) reduce optimizer memory by projecting gradients and moment estimates into low-rank spaces via singular value decomposition or random projection. However, they fail to account for inter-projection correlation, causing performance degradation, and their projection strategies often incur high computational costs. In this paper, we present COAP (Correlation-Aware Gradient Projection), a memory-efficient method that minimizes computational overhead while maintaining training performance. Evaluated across various vision, language, and multimodal tasks, COAP outperforms existing methods in both training speed and model performance. For LLaMA-1B, it reduces optimizer memory by 61% with only 2% additional time cost, achieving the same PPL as AdamW. With 8-bit quantization, COAP cuts optimizer memory by 81% and achieves 4x speedup over GaLore for LLaVA-v1.5-7B fine-tuning, while delivering higher accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_COAP_Memory-Efficient_Training_with_Correlation-Aware_Gradient_Projection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_COAP_Memory-Efficient_Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Jinqi and Sang, Shen and Zhi, Tiancheng and Liu, Jing and Yan, Qing and Luo, Linjie and Yuan, Bo},
    title     = {COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30116-30126}
}

raw_affiliation:
[{"affiliation": "ByteDance Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}]


title: Perceptual Inductive Bias Is What You Need Before Contrastive Learning
authors: Junru Zhao, Tianqin Li, Dunhan Jiang, Shenghao Wu, Alan Ramirez, Tai Sing Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9621-9630
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/Zhao_Perceptual_Inductive_Bias_Is_What_You_Need_Before_Contrastive_Learning_CVPR_2025_paper.html

abstract:
David Marr's seminal theory of human perception stipulates that visual processing is a multi-stage process, prioritizing the derivation of boundary and surface properties before forming semantic object representations. In contrast, contrastive representation learning frameworks typically bypass this explicit multi-stage approach, defining their objective as the direct learning of a semantic representation space for objects. While effective in general contexts, this approach sacrifices the inductive biases of vision, leading to slower convergence speed and learning shortcut resulting in texture bias. In this work, we demonstrate that leveraging Marr's multi-stage theory--by first constructing boundary and surface-level representations using perceptual constructs from early visual processing stages and subsequently training for object semantics--leads to 2x faster convergence on ResNet18, improved final representations on semantic segmentation, depth estimation, and object recognition, and enhanced robustness and out-of-distribution capability. Together, we propose a pretraining stage before the general contrastive representation pretraining to further enhance the final representation quality and reduce the overall convergence time via inductive bias from human vision systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Perceptual_Inductive_Bias_Is_What_You_Need_Before_Contrastive_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Perceptual_Inductive_Bias_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Junru and Li, Tianqin and Jiang, Dunhan and Wu, Shenghao and Ramirez, Alan and Lee, Tai Sing},
    title     = {Perceptual Inductive Bias Is What You Need Before Contrastive Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9621-9630}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9154-9164
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Xi'an", "Beijing", "Hong Kong", "Hong Kong", "Guangzhou"]
countries: ["China", "China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_FaceBench_A_Multi-View_Multi-Level_Facial_Attribute_VQA_Dataset_for_Benchmarking_CVPR_2025_paper.html

title:
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs

authors:
Xiaoqin Wang, Xusen Ma, Xianxu Hou, Meidan Ding, Yudong Li, Junliang Chen, Wenting Chen, Xiaoyang Peng, Linlin Shen

abstract:
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://github.com/CVI-SZU/FaceBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_FaceBench_A_Multi-View_Multi-Level_Facial_Attribute_VQA_Dataset_for_Benchmarking_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xiaoqin and Ma, Xusen and Hou, Xianxu and Ding, Meidan and Li, Yudong and Chen, Junliang and Chen, Wenting and Peng, Xiaoyang and Shen, Linlin},
    title     = {FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9154-9164}
}

raw_affiliation:
[{"affiliation": "Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "city": "Shenzhen", "country": "China"}, {"affiliation": "AIAC, Xi\u2019an Jiaotong-Liverpool University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University", "National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University", "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "AIAC, Xi\u2019an Jiaotong-Liverpool University", "Tsinghua University", "The Hong Kong Polytechnic University", "City University of Hong Kong", "Sun Yat-sen University"]


title: EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation
authors: Md Mostafijur Rahman, Radu Marculescu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10435-10444
raw_affiliation: [{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}]
affiliations: ["The University of Texas at Austin"]
cities: ["Austin"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rahman_EffiDec3D_An_Optimized_Decoder_for_High-Performance_and_Efficient_3D_Medical_CVPR_2025_paper.html

abstract:
Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments. The high #FLOPs and #Params in these networks stem largely from complex decoder designs with high-resolution layers and excessive channel counts. In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages, which sets the number of channels to the minimum needed for accurate feature representation. Additionally, EffiDec3D removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Similarly, for SwinUNETR and SwinUNETRv2 (which share an identical decoder), we observe reductions of 94.9% in #Params and 86.2% in #FLOPs. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation. Our implementation is available at https://github.com/SLDGroup/EffiDec3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rahman_EffiDec3D_An_Optimized_Decoder_for_High-Performance_and_Efficient_3D_Medical_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rahman_EffiDec3D_An_Optimized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rahman_2025_CVPR,
    author    = {Rahman, Md Mostafijur and Marculescu, Radu},
    title     = {EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10435-10444}
}


title: Exploring Historical Information for RGBE Visual Tracking with Mamba
authors: Chuanyu Sun, Jiqing Zhang, Yang Wang, Huilin Ge, Qianchen Xia, Baocai Yin, Xin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6500-6509
cities: ["Dalian", "Dalian", "Zhenjiang", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Exploring_Historical_Information_for_RGBE_Visual_Tracking_with_Mamba_CVPR_2025_paper.html

abstract:
Combining the advantages of conventional and event cameras for robust visual tracing has drawn extensive interest. However, existing tracking approaches heavily engage in complex cross-modal fusion modules, leading to higher computational complexity and training challenges. Besides, these methods generally ignore the effective integration of historical information, which is crucial to grasping the change in the target's appearance and motion trends. Given the recent advancements in Mamba's long-range modeling and linear complexity, we explore its potential in addressing the above issues in RGBE tracking tasks. Specifically, we first propose an efficient fusion module based on Mamba, which utilizes a simple gate-based interaction scheme to achieve effective modality-selective fusion. This module can be seamlessly integrated into the encoding layer of prevalent Transformer-based backbones. Moreover, we further present a novel historical decoder that leverages Mamba's advanced long sequence modeling to effectively capture the target appearance changes with autoregressive queries. Extensive experiments show that our proposed approach achieves state-of-the-art performance on multiple challenging short-term and long-term RGBE benchmarks.  Besides, the effectiveness of each key Mamba-based component of our approach is evidenced by our thorough ablation study.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Exploring_Historical_Information_for_RGBE_Visual_Tracking_with_Mamba_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Exploring_Historical_Information_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Chuanyu and Zhang, Jiqing and Wang, Yang and Ge, Huilin and Xia, Qianchen and Yin, Baocai and Yang, Xin},
    title     = {Exploring Historical Information for RGBE Visual Tracking with Mamba},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6500-6509}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Dalian Maritime University", "city": "Dalian", "country": "China"}, {"affiliation": "Jiangsu University of Science and Technology", "city": "Zhenjiang", "country": "China"}, {"affiliation": "The Future Laboratory,Tsinghua University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology", "Dalian Maritime University", "Jiangsu University of Science and Technology", "The Future Laboratory,Tsinghua University, Beijing, China", "Beijing University of Technology"]


title: Gyro-based Neural Single Image Deblurring
authors: Heemin Yang, Jaesung Rim, Seungyong Lee, Seung-Hwan Baek, Sunghyun Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23111-23120
raw_affiliation: [{"affiliation": "POSTECH CSE1 & GSAI2", "city": "Pohang", "country": "South Korea"}]
affiliations: ["POSTECH CSE1 & GSAI2"]
cities: ["Pohang"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Gyro-based_Neural_Single_Image_Deblurring_CVPR_2025_paper.html

abstract:
In this paper, we present GyroDeblurNet, a novel single-image deblurring method that utilizes a gyro sensor to resolve the ill-posedness of image deblurring. The gyro sensor provides valuable information about camera motion that can improve deblurring quality. However, exploiting real-world gyro data is challenging due to errors from various sources. To handle these errors, GyroDeblurNet is equipped with two novel neural network blocks: a gyro refinement block and a gyro deblurring block. The gyro refinement block refines the erroneous gyro data using the blur information from the input image. The gyro deblurring block removes blur from the input image using the refined gyro data and further compensates for gyro error by leveraging the blur information from the input image. For training a neural network with erroneous gyro data, we propose a training strategy based on the curriculum learning. We also introduce a novel gyro data embedding scheme to represent real-world intricate camera shakes. Finally, we present both synthetic and real-world datasets for training and evaluating gyro-based single image deblurring. Our experiments demonstrate that our approach achieves state-of-the-art deblurring quality by effectively utilizing erroneous gyro data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Gyro-based_Neural_Single_Image_Deblurring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Gyro-based_Neural_Single_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Heemin and Rim, Jaesung and Lee, Seungyong and Baek, Seung-Hwan and Cho, Sunghyun},
    title     = {Gyro-based Neural Single Image Deblurring},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23111-23120}
}


title: ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2891-2901
affiliations: ["NVIDIA", "Cornell University"]
cities: ["Santa Clara", "Ithaca"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_ArtiScene_Language-Driven_Artistic_3D_Scene_Generation_Through_Image_Intermediary_CVPR_2025_paper.html

authors:
Zeqi Gu, Yin Cui, Zhaoshuo Li, Fangyin Wei, Yunhao Ge, Jinwei Gu, Ming-Yu Liu, Abe Davis, Yifan Ding

abstract:
Designing 3D scenes is traditionally a challenging and laborious task that demands both artistic expertise and proficiency with complex software. Recent advances in text-to-3D generation have greatly simplified this process by letting users create scenes based on simple text descriptions. However, as these methods generally require extra training or in-context learning, their performance is often hindered by the limited availability of high-quality 3D data. In contrast, modern text-to-image models learned from web-scale images can generate scenes with diverse, reliable spatial layouts and consistent, visually appealing styles. Our key insight is that instead of learning directly from 3D scenes, we can leverage generated 2D images as an intermediary to guide 3D synthesis. In light of this, we introduce ArtiScene, a training-free automated pipeline for scene design that integrates the flexibility of free-form text-to-image generation with the diversity and reliability of 2D intermediary layouts. We generate the 2D intermediary image from a scene description, extract object shapes and appearances, create 3D models, and assemble them into the final scene with geometry, position, and pose extracted from the same image. Being generalizable to a wide range of scenes and styles, ArtiScene is shown to outperform state-of-the-art benchmarks by a large margin in layout and aesthetic quality by quantitative metrics. It also averages a 74.89% winning rate in extensive user studies and 95.07% in GPT evaluation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_ArtiScene_Language-Driven_Artistic_3D_Scene_Generation_Through_Image_Intermediary_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_ArtiScene_Language-Driven_Artistic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Zeqi and Cui, Yin and Li, Zhaoshuo and Wei, Fangyin and Ge, Yunhao and Gu, Jinwei and Liu, Ming-Yu and Davis, Abe and Ding, Yifan},
    title     = {ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2891-2901}
}

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


authors: Zifan Wang, Ziqing Chen, Junyu Chen, Jilong Wang, Yuxin Yang, Yunze Liu, Xueyi Liu, He Wang, Li Yi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17315-17325
cities: ["Beijing", "Beijing", "Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MobileH2R_Learning_Generalizable_Human_to_Mobile_Robot_Handover_Exclusively_from_CVPR_2025_paper.html

title:
MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data

abstract:
This paper introduces MobileH2R, a framework for learning generalizable vision-based human-to-mobile-robot (H2MR) handover skills. Unlike traditional fixed-base handovers, this task requires a mobile robot to reliably receive objects in a large workspace enabled by its mobility. Our key insight is that generalizable handover skills can be developed in simulators using high-quality synthetic data, without the need for real-world demonstrations. To achieve this, we propose a scalable pipeline for generating diverse synthetic full-body human motion data, an automated method for creating safe and imitation-friendly demonstrations, and an efficient 4D imitation learning method for distilling large-scale demonstrations into closed-loop policies with base-arm coordination. Experimental evaluations in both simulators and the real world show significant improvements (at least +15% success rate) over baseline methods in all cases. Experiments also validate that large-scale and diverse synthetic data greatly enhances robot learning, highlighting our scalable framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MobileH2R_Learning_Generalizable_Human_to_Mobile_Robot_Handover_Exclusively_from_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zifan and Chen, Ziqing and Chen, Junyu and Wang, Jilong and Yang, Yuxin and Liu, Yunze and Liu, Xueyi and Wang, He and Yi, Li},
    title     = {MobileH2R: Learning Generalizable Human to Mobile Robot Handover Exclusively from Scalable and Diverse Synthetic Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17315-17325}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Galbot", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["Tsinghua University", "Galbot", "Peking University", "Shanghai Artificial Intelligence Laboratory", "Shanghai Qi Zhi Institute"]


title: Improving Sound Source Localization with Joint Slot Attention on Image and Audio
authors: Inho Kim, Youngkil Song, Jicheol Park, Won Hwa Kim, Suha Kwak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3121-3130
affiliations: ["Dept. of CSE, POSTECH", "Graduate School of AI, POSTECH"]
cities: ["Pohang", "Pohang"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Improving_Sound_Source_Localization_with_Joint_Slot_Attention_on_Image_CVPR_2025_paper.html

abstract:
Sound source localization (SSL) is the task of locating the source of sound within an image. Due to the lack of localization labels, the de facto standard in SSL has been to represent an image and audio as a single embedding vector each, and use them to learn SSL via contrastive learning. To this end, previous work samples one of local image features as the image embedding and aggregates all local audio features to obtain the audio embedding, which is far from optimal due to the presence of noise and background irrelevant to the actual target in the input.We present a novel SSL method that addresses this chronic issue by joint slot attention on image and audio. To be specific, two slots competitively attend image and audio features to decompose them into target and off-target representations, and only target representations of image and audio are used for contrastive learning. Also, we introduce cross-modal attention matching to further align local features of image and audio. Our method achieved the best in almost all settings on three public benchmarks for SSL, and substantially outperformed all the prior work in cross-modal retrieval.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Improving_Sound_Source_Localization_with_Joint_Slot_Attention_on_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Improving_Sound_Source_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Inho and Song, Youngkil and Park, Jicheol and Kim, Won Hwa and Kwak, Suha},
    title     = {Improving Sound Source Localization with Joint Slot Attention on Image and Audio},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3121-3130}
}

raw_affiliation:
[{"affiliation": "Dept. of CSE, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Graduate School of AI, POSTECH", "city": "Pohang", "country": "South Korea"}]


authors: Marwane Hariat, Antoine Manzanera, David Filliat
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21868-21879
affiliations: ["U2IS, ENSTA, Institut Polytechnique de Paris"]
cities: ["Palaiseau"]
countries: ["France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hariat_Improved_Monocular_Depth_Prediction_Using_Distance_Transform_Over_Pre-semantic_Contours_CVPR_2025_paper.html

title:
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks

abstract:
Monocular depth estimation (MDE) with self-supervised training approaches struggles in low-texture areas, where photometric losses may lead to ambiguous depth predictions. To address this, we propose a novel technique that enhances spatial information by applying a distance transform over pre-semantic contours, augmenting discriminative power in  low texture regions. Our approach jointly estimates pre-semantic contours, depth and ego-motion. The pre-semantic contours are leveraged to produce new input images, with variance augmented by the distance transform in uniform areas. This approach results in more effective loss functions, enhancing the training process for depth and ego-motion.  We demonstrate theoretically that the distance transform is the optimal variance-augmenting technique in this context. Through extensive experiments on KITTI and Cityscapes, our model demonstrates robust performance, surpassing conventional self-supervised methods in MDE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hariat_Improved_Monocular_Depth_Prediction_Using_Distance_Transform_Over_Pre-semantic_Contours_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hariat_Improved_Monocular_Depth_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hariat_2025_CVPR,
    author    = {Hariat, Marwane and Manzanera, Antoine and Filliat, David},
    title     = {Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21868-21879}
}

raw_affiliation:
[{"affiliation": "U2IS, ENSTA, Institut Polytechnique de Paris", "city": "Palaiseau", "country": "France"}]


title: Feature-Preserving Mesh Decimation for Normal Integration
authors: Moritz Heep, Sven Behnke, Eduard Zell
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5783-5792
cities: ["Bonn", "Bonn", "Unknown"]
countries: ["Germany", "Germany", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heep_Feature-Preserving_Mesh_Decimation_for_Normal_Integration_CVPR_2025_paper.html

abstract:
Normal integration reconstructs 3D surfaces from normal maps obtained, e.g. by photometric stereo. These normal maps capture surface details down to the pixel level but require large computational resources for integration at high resolutions. In this work, we replace the dense pixel grid with a sparse anisotropic triangle mesh prior to normal integration. We adapt the triangle mesh to the local geometry in the case of complex surface structures and remove oversampling from flat featureless regions. For high-resolution images, the resulting compression reduces normal integration runtimes from hours to minutes while maintaining high surface accuracy. Our main contribution is the derivation of the well-known quadric error measure from mesh decimation for screen space applications and its combination with optimal Delaunay triangulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heep_Feature-Preserving_Mesh_Decimation_for_Normal_Integration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heep_Feature-Preserving_Mesh_Decimation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Heep_2025_CVPR,
    author    = {Heep, Moritz and Behnke, Sven and Zell, Eduard},
    title     = {Feature-Preserving Mesh Decimation for Normal Integration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5783-5792}
}

raw_affiliation:
[{"affiliation": "PhenoRob University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Autonomous Intelligent Systems University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}]

affiliations:
["PhenoRob University of Bonn", "Autonomous Intelligent Systems University of Bonn", "Independent Researcher"]


authors: Zeqing Wang, Qingyang Ma, Wentao Wan, Haojie Li, Keze Wang, Yonghong Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21226-21237
cities: ["Guangzhou", "Guangzhou", "Shenzhen", "Guangzhou", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Is_this_Generated_Person_Existed_in_Real-world_Fine-grained_Detecting_and_CVPR_2025_paper.html

title:
Is this Generated Person Existed in Real-world? Fine-grained Detecting and Calibrating Abnormal Human-body

abstract:
Recent improvements in visual synthesis have significantly enhanced the depiction of generated human photos, which are pivotal due to their wide applicability and demand. Nonetheless, the existing text-to-image or text-to-video models often generate low-quality human photos that might differ considerably from real-world body structures, referred to as "abnormal human bodies". Such abnormalities, typically deemed unacceptable, pose considerable challenges in the detection and repair of them within human photos. These challenges require precise abnormality recognition capabilities, which entail pinpointing both the location and the abnormality type. Intuitively, Visual Language Models (VLMs) that have obtained remarkable performance on various visual tasks are quite suitable for this task. However, their performance on abnormality detection in human photos is quite poor.Hence, it is quite important to highlight this task for the research community. In this paper, we first introduce a simple yet challenging task, i.e., Fine-grained Human-body Abnormality Detection (FHAD), and construct two high-quality datasets for evaluation. Then, we propose a meticulous framework, named HumanCalibrator, which identifies and repairs abnormalities in human body structures while preserving the other content. Experiments indicate that our HumanCalibrator achieves high accuracy in abnormality detection and accomplishes an increase in visual comparisons while preserving the other visual content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Is_this_Generated_Person_Existed_in_Real-world_Fine-grained_Detecting_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Is_this_Generated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zeqing and Ma, Qingyang and Wan, Wentao and Li, Haojie and Wang, Keze and Tian, Yonghong},
    title     = {Is this Generated Person Existed in Real-world? Fine-grained Detecting and Calibrating Abnormal Human-body},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21226-21237}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "PengCheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Key Laboratory of Big Data Analysis and Processing", "city": "Guangzhou", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Sun Yat-sen University", "South China University of Technology", "PengCheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing", "Peking University"]


title: PERSE: Personalized 3D Generative Avatars from A Single Portrait
authors: Hyunsoo Cha, Inhee Lee, Hanbyul Joo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15953-15962
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/Cha_PERSE_Personalized_3D_Generative_Avatars_from_A_Single_Portrait_CVPR_2025_paper.html

abstract:
We present PERSE, a method for building a personalized 3D generative avatar from a reference portrait. Our avatar enables facial attribute editing in a continuous and disentangled latent space to control each facial attribute, while preserving the individual's identity. To achieve this, our method begins by synthesizing large-scale synthetic 2D video datasets, where each video contains consistent changes in facial expression and viewpoint, along with variations in a specific facial attribute from the original input. We propose a novel pipeline to produce high-quality, photorealistic 2D videos with facial attribute editing. Leveraging this synthetic attribute dataset, we present a personalized avatar creation method based on 3D Gaussian Splatting, learning a continuous and disentangled latent space for intuitive facial attribute manipulation. To enforce smooth transitions in this latent space, we introduce a latent space regularization technique by using interpolated 2D faces as supervision. Compared to previous approaches, we demonstrate that PERSE generates high-quality avatars with interpolated attributes while preserving the identity of the reference individual.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cha_PERSE_Personalized_3D_Generative_Avatars_from_A_Single_Portrait_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cha_PERSE_Personalized_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cha_2025_CVPR,
    author    = {Cha, Hyunsoo and Lee, Inhee and Joo, Hanbyul},
    title     = {PERSE: Personalized 3D Generative Avatars from A Single Portrait},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15953-15962}
}


title: Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29580-29590
affiliations: ["Stanford University", "Tsinghua University", "MIT"]
cities: ["Stanford", "Beijing", "Cambridge"]
countries: ["USA", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Automated_Generation_of_Challenging_Multiple-Choice_Questions_for_Vision_Language_Model_CVPR_2025_paper.html

authors:
Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, Serena Yeung-Levy

abstract:
The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce AutoConverter, an agentic framework that automatically converts these open-ended questions into multiple-choice format, enabling objective evaluation while reducing the costly multiple-choice question creation process. Our experiments demonstrate that AutoConverter can generate correct and challenging multiple-choice questions, with VLMs demonstrating consistently similar or lower accuracy on these questions compared to human-created ones. Using AutoConverter, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format, totaling 9,018 questions. We comprehensively evaluate 33 state-of-the-art VLMs on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Automated_Generation_of_Challenging_Multiple-Choice_Questions_for_Vision_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Automated_Generation_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yuhui and Su, Yuchang and Liu, Yiming and Wang, Xiaohan and Burgess, James and Sui, Elaine and Wang, Chenyu and Aklilu, Josiah and Lozano, Alejandro and Wei, Anjiang and Schmidt, Ludwig and Yeung-Levy, Serena},
    title     = {Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29580-29590}
}

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


title: DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1745-1755
affiliations: ["The University of Hong Kong", "University of California, Berkeley"]
cities: ["Hong Kong", "Berkeley"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_DexHandDiff_Interaction-aware_Diffusion_Planning_for_Adaptive_Dexterous_Manipulation_CVPR_2025_paper.html

authors:
Zhixuan Liang, Yao Mu, Yixiao Wang, Tianxing Chen, Wenqi Shao, Wei Zhan, Masayoshi Tomizuka, Ping Luo, Mingyu Ding

abstract:
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks, they often produce unrealistic ghost states (e.g., the object automatically moves without hand contact) or lack adaptability when handling complex sequential interactions. In this work, we introduce DexHandDiff, an interaction-aware diffusion planning framework for adaptive dexterous manipulation. DexHandDiff models joint state-action dynamics through a dual-phase diffusion process which consists of pre-interaction contact alignment and post-contact goal-directed control, enabling goal-adaptive generalizable dexterous manipulation. Additionally, we incorporate dynamics model-based dual guidance and leverage large language models for automated guidance function generation, enhancing generalizability for physical interactions and facilitating diverse goal adaptation through language cues. Experiments on physical interaction tasks such as door opening, pen and block re-orientation, object relocation, and hammer striking demonstrate DexHandDiff's effectiveness on goals outside training distributions, achieving over twice the average success rate (59.2% vs. 29.5%) compared to existing methods. Our framework achieves an average of 70.7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_DexHandDiff_Interaction-aware_Diffusion_Planning_for_Adaptive_Dexterous_Manipulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_DexHandDiff_Interaction-aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Zhixuan and Mu, Yao and Wang, Yixiao and Chen, Tianxing and Shao, Wenqi and Zhan, Wei and Tomizuka, Masayoshi and Luo, Ping and Ding, Mingyu},
    title     = {DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1745-1755}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}]


title: VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness
authors: SeungJu Cha, Kwanyoung Lee, Ye-Chan Kim, Hyunwoo Oh, Dong-Jin Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8041-8050
raw_affiliation: [{"affiliation": "Hanyang University, South Korea", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Hanyang University, South Korea"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cha_VerbDiff_Text-Only_Diffusion_Models_with_Enhanced_Interaction_Awareness_CVPR_2025_paper.html

abstract:
Recent large-scale text-to-image diffusion models generate photorealistic images but often struggle to accurately depict interactions between humans and objects due to their limited ability to differentiate various interaction words.In this work, we propose VerbDiff to address the challenge of capturing nuanced interactions within text-to-image diffusion models. VerbDiff is a novel text-to-image generation model that weakens the bias between interaction words and objects, enhancing the understanding of interactions. Specifically, we disentangle various interaction words from frequency-based anchor words and leverage localized interaction regions from generated images to help the model better capture semantics in distinctive words without extra conditions. Our approach enables the model to accurately understand the intended interaction between humans and objects, producing high-quality images with accurate interactions aligned with specified verbs. Extensive experiments on the HICO-DET dataset demonstrate the effectiveness of our method compared to previous approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cha_VerbDiff_Text-Only_Diffusion_Models_with_Enhanced_Interaction_Awareness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cha_VerbDiff_Text-Only_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cha_2025_CVPR,
    author    = {Cha, SeungJu and Lee, Kwanyoung and Kim, Ye-Chan and Oh, Hyunwoo and Kim, Dong-Jin},
    title     = {VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8041-8050}
}


title: ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence
authors: Yuan Sun, Yongxiang Li, Zhenwen Ren, Guiduo Duan, Dezhong Peng, Peng Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30732-30741
cities: ["Chengdu", "Mianyang", "Chengdu", "Chengdu", "Chengdu"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_ROLL_Robust_Noisy_Pseudo-label_Learning_for_Multi-View_Clustering_with_Noisy_CVPR_2025_paper.html

abstract:
Multi-view clustering (MVC) aims to exploit complementary information from diverse views to enhance clustering performance. Since pseudo-labels can provide additional semantic information, many MVC methods have been proposed to guide unsupervised multi-view learning through pseudo-labels. These methods implicitly assume that the predicted pseudo-labels are predicted correctly. However, due to the challenges in training a flawless unsupervised model, this assumption can be easily violated, thereby leading to the Noisy Pseudo-label Problem (NPP). Moreover, these existing approaches typically rely on the assumption of perfect cross-view alignment. In practice, it is frequently compromised due to noise or sensor differences, thereby resulting in the Noisy Correspondence Problem (NCP). Based on the above observations, we reveal and study unsupervised multi-view learning under NPP and NCP. To this end, we propose Robust Noisy Pseudo-label Learning (ROLL) to prevent the overfitting problem caused by both NPP and NCP. Specifically, we first adopt traditional contrastive learning to warm up the model, thereby generating the pseudo-labels in a self-supervised manner. Afterward, we propose noise-tolerance pseudo-label learning to deal with the noise in the predicted pseudo-labels, thereby embracing the robustness against NPP. To further mitigate the overfitting problem, we present robust multi-view contrastive learning to mitigate the negative impact of NCP. Extensive experiments on five multi-view datasets demonstrate the superior clustering performance of our ROLL compared to 11 state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_ROLL_Robust_Noisy_Pseudo-label_Learning_for_Multi-View_Clustering_with_Noisy_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Yuan and Li, Yongxiang and Ren, Zhenwen and Duan, Guiduo and Peng, Dezhong and Hu, Peng},
    title     = {ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30732-30741}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Southwest University of Science and Technology", "city": "Mianyang", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd", "city": "Chengdu", "country": "China"}, {"affiliation": "Tianfu Jincheng Laboratory", "city": "Chengdu", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University", "School of Computer Science and Technology, Southwest University of Science and Technology", "School of Computer Science and Engineering, University of Electronic Science and Technology of China", "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd", "Tianfu Jincheng Laboratory"]


title: Towards In-the-wild 3D Plane Reconstruction from a Single Image
authors: Jiachen Liu, Rui Yu, Sili Chen, Sharon X. Huang, Hengkai Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27027-27037
affiliations: ["The Pennsylvania State University", "University of Louisville", "Bytedance"]
cities: ["State College", "Louisville", "Beijing"]
countries: ["USA", "USA", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Towards_In-the-wild_3D_Plane_Reconstruction_from_a_Single_Image_CVPR_2025_paper.html

abstract:
3D plane reconstruction from a single image is a crucial yet challenging topic in 3D computer vision. Previous state-of-the-art (SOTA) methods have focused on training their system on a single dataset from either indoor or outdoor domain, limiting their generalizability across diverse testing data. In this work, we introduce a novel framework dubbed ZeroPlane, a Transformer-based model targeting zero-shot 3D plane detection and reconstruction from a single image, over diverse domains and environments. To enable data-driving models on multiple domains, we have curated a large-scale (over 14 datasets and 560,000 images), high-resolution, densely-annotated planar benchmark from various indoor and outdoor scenes. To address the challenge of achieving desirable planar geometry on multi-dataset training, we propose to disentangle the representation of plane normal and offset, and employ an exemplar-guided, classification-then-regression paradigm to learn plane and offset respectively. Additionally, we employ advanced backbones as image encoder, and present an effective pixel-geometry-enhanced plane embedding module to further facilitate planar reconstruction. Extensive experiments across multiple zero-shot evaluation datasets have demonstrated that our approach significantly outperforms previous methods on both reconstruction accuracy and generalizability, especially over in-the-wild data. We will release all of the labeled data, code and models upon the acceptance of this paper. Our code and data are available at: https://github.com/jcliu0428/ZeroPlane.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Towards_In-the-wild_3D_Plane_Reconstruction_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Towards_In-the-wild_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiachen and Yu, Rui and Chen, Sili and Huang, Sharon X. and Guo, Hengkai},
    title     = {Towards In-the-wild 3D Plane Reconstruction from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27027-27037}
}

raw_affiliation:
[{"affiliation": "The Pennsylvania State University", "city": "State College", "country": "USA"}, {"affiliation": "University of Louisville", "city": "Louisville", "country": "USA"}, {"affiliation": "Bytedance", "city": "Beijing", "country": "China"}]


title: Memories of Forgotten Concepts
authors: Matan Rusanovsky, Shimon Malnick, Amir Jevnisek, Ohad Fried, Shai Avidan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2966-2975
affiliations: ["Tel Aviv University", "Reichman University"]
cities: ["Tel Aviv", "Herzliya"]
countries: ["Israel", "Israel"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rusanovsky_Memories_of_Forgotten_Concepts_CVPR_2025_paper.html

abstract:
Diffusion models dominate the space of text-to-image generation, yet they may produce undesirable outputs, including explicit content or private data. To mitigate this, concept ablation techniques have been explored to limit the generation of certain concepts.In this paper, we reveal that the erased concept information persists in the model and that erased concept images can be generated using the right latent. Utilizing inversion methods, we show that there exist latent seeds capable of generating high quality images of erased concepts.Moreover, we show that these latents have likelihoods that overlap with those of images outside the erased concept.We extend this to demonstrate that for every image from the erased concept set, we can generate many seeds that generate the erased concept.Given the vast space of latents capable of generating ablated concept images, our results suggest that fully erasing concept information may be intractable, highlighting possible vulnerabilities in current concept ablation techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rusanovsky_Memories_of_Forgotten_Concepts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rusanovsky_Memories_of_Forgotten_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rusanovsky_2025_CVPR,
    author    = {Rusanovsky, Matan and Malnick, Shimon and Jevnisek, Amir and Fried, Ohad and Avidan, Shai},
    title     = {Memories of Forgotten Concepts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2966-2975}
}

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


title: Dynamic Stereotype Theory Induced Micro-expression Recognition with Oriented Deformation
authors: Bohao Zhang, Xuejiao Wang, Changbo Wang, Gaoqi He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10701-10711
cities: ["Shanghai", "Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Dynamic_Stereotype_Theory_Induced_Micro-expression_Recognition_with_Oriented_Deformation_CVPR_2025_paper.html

abstract:
Micro-expression recognition (MER) aims to uncover genuine emotions and underlying psychological states. However, existing MER methods struggle with three main challenges. 1) Scarcity of micro-expression samples. 2) Difficulty in modeling nearly imperceptible facial movements. 3) Reliance on apex frame annotations. To address these issues, we propose a Self-supervised Oriented Deformation model for Apex-free Micro-expression Recognition (SODA4MER). Our approach enhances local deformation perception using muscle-group priors and amplifies subtle features through Dynamic Stereotype Theory (DST) based enhancement, while contrastive learning eliminates the need for manual apex annotations. Specifically, the Oriented deformation estimator of SODA4MER is first pre-trained in a self-supervised manner. Secondly, a Gated Temporal Variance Gaussian model (GTVG) is introduced to adaptively integrate facial muscle-group priors, enhancing local deformation perception and mitigating noise from head movements. Then, contrastive learning is employed to achieve apex detection by identifying the frame with the most significant local deformation. Finally, guided by DST, we introduced a feature enhancement strategy that models the temporal dynamics of local deformation in the activation and decay phases, leading to richer deformation features. Our rigorous experiments confirm the competitive performance and practical applicability of SODA4MER.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Dynamic_Stereotype_Theory_Induced_Micro-expression_Recognition_with_Oriented_Deformation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Dynamic_Stereotype_Theory_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Bohao and Wang, Xuejiao and Wang, Changbo and He, Gaoqi},
    title     = {Dynamic Stereotype Theory Induced Micro-expression Recognition with Oriented Deformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10701-10711}
}

raw_affiliation:
[{"affiliation": "Lab of Artificial Intelligence for Education, East China Normal University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Data Science and Engineering, East China Normal University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China", "city": "Shanghai", "country": "China"}]

affiliations:
["Lab of Artificial Intelligence for Education, East China Normal University, Shanghai, China", "School of Computer Science and Technology, East China Normal University, Shanghai, China", "School of Data Science and Engineering, East China Normal University, Shanghai, China", "Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China"]


title: PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
authors: Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu, Josiane Mothe, Radu Tudor Ionescu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28651-28661
cities: ["Bucharest", "Marseille", "Toulouse"]
countries: ["Romania", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Poesina_PQPP_A_Joint_Benchmark_for_Text-to-Image_Prompt_and_Query_Performance_CVPR_2025_paper.html

abstract:
Text-to-image generation has recently emerged as a viable alternative to text-to-image retrieval, driven by the visually impressive results of generative diffusion models. Although query performance prediction is an active research topic in information retrieval, to the best of our knowledge, there is no prior study that analyzes the difficulty of queries (referred to as prompts) in text-to-image generation, based on human judgments. To this end, we introduce the first dataset of prompts which are manually annotated in terms of image generation performance. Additionally, we extend these evaluations to text-to-image retrieval by collecting manual annotations that represent retrieval performance. We thus establish the first joint benchmark for prompt and query performance prediction (PQPP) across both tasks, comprising over 10K queries. Our benchmark enables (i) the comparative assessment of prompt/query difficulty in both image generation and image retrieval, and (ii) the evaluation of prompt/query performance predictors addressing both generation and retrieval. We evaluate several pre- and post-generation/retrieval performance predictors, thus providing competitive baselines for future research. Our benchmark and code are publicly available at https://github.com/Eduard6421/PQPP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Poesina_PQPP_A_Joint_Benchmark_for_Text-to-Image_Prompt_and_Query_Performance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Poesina_PQPP_A_Joint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Poesina_2025_CVPR,
    author    = {Poesina, Eduard and Costache, Adriana Valentina and Chifu, Adrian-Gabriel and Mothe, Josiane and Ionescu, Radu Tudor},
    title     = {PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28651-28661}
}

raw_affiliation:
[{"affiliation": "University of Bucharest", "city": "Bucharest", "country": "Romania"}, {"affiliation": "Aix-Marseille Universit\u00b4e", "city": "Marseille", "country": "France"}, {"affiliation": "Universit\u00b4e Toulouse Jean-Jaur`es", "city": "Toulouse", "country": "France"}]

affiliations:
["University of Bucharest", "Aix-Marseille Universit\u00b4e", "Universit\u00b4e Toulouse Jean-Jaur`es"]


authors: Mariamma Antony, Rajiv Porana, Sahil M Lathiya, Siva Teja Kakileti, Chiranjib Bhattacharyya
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25887-25896
affiliations: ["Indian Institute of Science", "Niramai Health Analytix"]
cities: ["Bangalore", "Bangalore"]
countries: ["India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Antony_CheXwhatsApp_A_Dataset_for_Exploring_Challenges_in_the_Diagnosis_of_CVPR_2025_paper.html

title:
CheXwhatsApp: A Dataset for Exploring Challenges in the Diagnosis of Chest X-rays through Mobile Devices

abstract:
Mobile health (mHealth) has emerged as a transformative solution to enhance healthcare accessibility and affordability, particularly in resource-constrained regions and low-to-middle-income countries.mHealth leverages mobile platforms to improve healthcare accessibility, addressing radiologist shortages in low-resource settings by enabling remote diagnosis and consultation through mobile devices. Mobile phones allow healthcare workers to transmit radiographic images, such as chest X-rays (CXR), to specialists or AI-driven models for interpretation. However, AI-based diagnosis using CXR images shared via apps like WhatsApp suffers from reduced predictability and explainability due to compression artifacts, and there is a lack of datasets to systematically study these challenges. To address this, we introduce CheXwhatsApp, a dataset of 141,804 paired original and WhatsApp-compressed CXR images. We present a benchmarking study which shows the dataset improves prediction stability and explainability of state-of-the-art models by up to 80%, while also enhancing localization performance. CheXwhatsApp is open-sourced to support advancements in mHealth applications for CXR analysis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Antony_CheXwhatsApp_A_Dataset_for_Exploring_Challenges_in_the_Diagnosis_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Antony_CheXwhatsApp_A_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Antony_2025_CVPR,
    author    = {Antony, Mariamma and Porana, Rajiv and Lathiya, Sahil M and Kakileti, Siva Teja and Bhattacharyya, Chiranjib},
    title     = {CheXwhatsApp: A Dataset for Exploring Challenges in the Diagnosis of Chest X-rays through Mobile Devices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25887-25896}
}

raw_affiliation:
[{"affiliation": "Indian Institute of Science", "city": "Bangalore", "country": "India"}, {"affiliation": "Niramai Health Analytix", "city": "Bangalore", "country": "India"}]


title: PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection
authors: Wei Li, Pin-Yu Chen, Sijia Liu, Ren Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10255-10264
affiliations: ["Illinois Institute of Technology", "IBM Research", "Michigan State University"]
cities: ["Chicago", "Yorktown Heights", "East Lansing"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_PSBD_Prediction_Shift_Uncertainty_Unlocks_Backdoor_Detection_CVPR_2025_paper.html

abstract:
Deep neural networks are susceptible to backdoor attacks, where adversaries manipulate model predictions by inserting malicious samples into the training data. Currently, there is still a significant challenge in identifying suspicious training data to unveil potential backdoor samples. In this paper, we propose a novel method, Prediction Shift Backdoor Detection (PSBD), leveraging an uncertainty-based approach requiring minimal unlabeled clean validation data. PSBD is motivated by an intriguing Prediction Shift (PS) phenomenon, where poisoned models' predictions on clean data often shift away from true labels towards certain other labels with dropout applied during inference, while backdoor samples exhibit less PS. We hypothesize PS results from the neuron bias effect, making neurons favor features of certain classes. PSBD identifies backdoor training samples by computing the Prediction Shift Uncertainty (PSU), the variance in probability values when dropout layers are toggled on and off during model inference. Extensive experiments have been conducted to verify the effectiveness and efficiency of PSBD, which achieves state-of-the-art results among mainstream detection methods. The code is available at https://github.com/WL-619/PSBD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_PSBD_Prediction_Shift_Uncertainty_Unlocks_Backdoor_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_PSBD_Prediction_Shift_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wei and Chen, Pin-Yu and Liu, Sijia and Wang, Ren},
    title     = {PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10255-10264}
}

raw_affiliation:
[{"affiliation": "Illinois Institute of Technology", "city": "Chicago", "country": "USA"}, {"affiliation": "IBM Research", "city": "Yorktown Heights", "country": "USA"}, {"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]


title: Degradation-Aware Feature Perturbation for All-in-One Image Restoration
authors: Xiangpeng Tian, Xiangyu Liao, Xiao Liu, Meng Li, Chao Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28165-28175
affiliations: ["College of Electronics and Information Engineering, Sichuan University, China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Degradation-Aware_Feature_Perturbation_for_All-in-One_Image_Restoration_CVPR_2025_paper.html

abstract:
All-in-one image restoration aims to recover clear images from various degradation types and levels with a unified model. Nonetheless, the significant variations among degradation types present challenges for training a universal model, often resulting in task interference, where the gradient update directions of different tasks may diverge due to shared parameters. To address this issue, motivated by the routing strategy, we propose DFPIR, a novel all-in-one image restorer that introduces Degradation-aware Feature Perturbations(DFP) to adjust the feature space to align with the unified parameter space. In this paper, the feature perturbations primarily include channel-wise perturbations and attention-wise perturbations. Specifically, channel-wise perturbations are implemented by shuffling the channels in high-dimensional space guided by degradation types, while attention-wise perturbations are achieved through selective masking in the attention space. To achieve these goals, we propose a Degradation-Guided Perturbation Block (DGPB) to implement these two functions, positioned between the encoding and decoding stages of the encoder-decoder architecture. Extensive experimental results demonstrate that DFPIR achieves state-of-the-art performance on several all-in-one image restoration tasks including image denoising, image dehazing, image deraining, motion deblurring, and low-light image enhancement. Our codes are available at https://github.com/TxpHome/DFPIR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Degradation-Aware_Feature_Perturbation_for_All-in-One_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Degradation-Aware_Feature_Perturbation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Xiangpeng and Liao, Xiangyu and Liu, Xiao and Li, Meng and Ren, Chao},
    title     = {Degradation-Aware Feature Perturbation for All-in-One Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28165-28175}
}

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


title: ACL: Activating Capability of Linear Attention for Image Restoration
authors: Yubin Gu, Yuan Meng, Jiayi Ji, Xiaoshuai Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17913-17923
cities: ["Xiamen", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_ACL_Activating_Capability_of_Linear_Attention_for_Image_Restoration_CVPR_2025_paper.html

abstract:
Image restoration (IR), a key area in computer vision, has entered a new era with deep learning. Recent research has shifted toward Selective State Space Models (Mamba) to overcome CNNs' limited receptive fields and Transformers' computational inefficiency. However, due to Mamba's inherent one-dimensional scanning limitations, recent approaches have introduced multi-directional scanning to bolster inter-sequence correlations. Despite these enhancements, these methods still struggle with managing local pixel correlations across various directions. Moreover, the recursive computation in Mamba's SSM leads to reduced efficiency. To resolve these issues, we exploit the mathematical congruences between linear attention and SSM within Mamba to propose a novel model, ACL, which leverages news designs to Activate the Capability of Linear attention for IR. ACL integrates linear attention blocks instead of SSM within Mamba, serving as the core component of encoders/decoders, and aims to preserve a global perspective while boosting computational efficiency. Furthermore, we have designed a simple yet robust local enhancement module with multi-scale dilated convolutions to extract both coarse and fine features to improve local detail recovery. Experimental results confirm that our ACL model excels in classical IR tasks such as de-raining and de-blurring, while maintaining relatively low parameter counts and FLOPs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_ACL_Activating_Capability_of_Linear_Attention_for_Image_Restoration_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Yubin and Meng, Yuan and Ji, Jiayi and Sun, Xiaoshuai},
    title     = {ACL: Activating Capability of Linear Attention for Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17913-17923}
}

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

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


title: GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
authors: Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape, Vishal M. Patel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28144-28154
raw_affiliation: [{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]
affiliations: ["Johns Hopkins University"]
cities: ["Baltimore"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rajagopalan_GenDeg_Diffusion-based_Degradation_Synthesis_for_Generalizable_All-In-One_Image_Restoration_CVPR_2025_paper.html

abstract:
Deep learning-based models for All-In-One image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This limitation arises primarily from insufficient diversity in degradation variations and scenes within existing datasets, resulting in inadequate representations of real-world scenarios. Additionally, capturing large-scale real-world paired data for degradations such as haze, low-light, and raindrops is often cumbersome and sometimes infeasible. In this paper, we leverage the generative capabilities of latent diffusion models to synthesize high-quality degraded images from their clean counterparts. Specifically, we introduce GenDeg, a degradation and intensity-aware conditional diffusion model, capable of producing diverse degradation patterns on clean images. Using GenDeg, we synthesize over 550k samples across six degradation types: haze, rain, snow, motion blur, low-light, and raindrops. These generated samples are integrated with existing datasets to form the GenDS dataset, comprising over 750k samples. Our experiments reveal that image restoration models trained on GenDS dataset exhibit significant improvements in out-of-distribution performance as compared to when trained solely on existing datasets. Furthermore, we provide comprehensive analyses on implications of diffusion model-based synthetic degradations for AIOR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rajagopalan_GenDeg_Diffusion-based_Degradation_Synthesis_for_Generalizable_All-In-One_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rajagopalan_GenDeg_Diffusion-based_Degradation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rajagopalan_2025_CVPR,
    author    = {Rajagopalan, Sudarshan and Nair, Nithin Gopalakrishnan and Paranjape, Jay N. and Patel, Vishal M.},
    title     = {GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28144-28154}
}


title: Phoenix: A Motion-based Self-Reflection Framework for Fine-grained Robotic Action Correction
authors: Wenke Xia, Ruoxuan Feng, Dong Wang, Di Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6981-6990
affiliations: ["Gaoling School of Artificial Intelligence, Renmin University of China", "Shanghai AI Laboratory"]
cities: ["Beijing", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Phoenix_A_Motion-based_Self-Reflection_Framework_for_Fine-grained_Robotic_Action_Correction_CVPR_2025_paper.html

abstract:
Building a generalizable self-correction system is crucial for robots to recover from failures. Despite advancements in Multimodal Large Language Models (MLLMs) that empower robots with semantic reflection ability for failure, translating semantic reflection into how to correct fine-grained robotic actions remains a significant challenge. To address this gap, we build the Phoenix framework, which leverages motion instruction as a bridge to connect high-level semantic reflection with low-level robotic action correction. In this motion-based self-reflection framework, we start with a dual-process motion adjustment mechanism with MLLMs to translate the semantic reflection into coarse-grained motion instruction adjustment. To leverage this motion instruction for guiding how to correct fine-grained robotic actions, a multi-task motion-conditioned diffusion policy is proposed to integrate visual observations for high-frequency robotic action correction. By combining these two models, we could shift the demand for generalization capability from the low-level manipulation policy to the MLLMs-driven motion adjustment model and facilitate precise, fine-grained robotic action correction. Utilizing this framework, we further develop a lifelong learning method to automatically improve the model's capability from interactions with dynamic environments. The experiments conducted in both the RoboMimic simulation and real-world scenarios prove the superior generalization and robustness of our framework across a variety of manipulation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Phoenix_A_Motion-based_Self-Reflection_Framework_for_Fine-grained_Robotic_Action_Correction_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Wenke and Feng, Ruoxuan and Wang, Dong and Hu, Di},
    title     = {Phoenix: A Motion-based Self-Reflection Framework for Fine-grained Robotic Action Correction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6981-6990}
}

raw_affiliation:
[{"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: The Power of Context: How Multimodality Improves Image Super-Resolution
authors: Kangfu Mei, Hossein Talebi, Mojtaba Ardakani, Vishal M. Patel, Peyman Milanfar, Mauricio Delbracio
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23141-23152
affiliations: ["Google", "Johns Hopkins University"]
cities: ["Mountain View", "Baltimore"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_The_Power_of_Context_How_Multimodality_Improves_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inputs. Existing methods often rely on limited image priors, leading to suboptimal results. We propose a novel approach that leverages the rich contextual information available in multiple modalities -- including depth, segmentation, edges, and text prompts -- to learn a powerful generative prior for SISR within a diffusion model framework. We introduce a flexible network architecture that effectively fuses multimodal information, accommodating an arbitrary number of input modalities without requiring significant modifications to the diffusion process. Crucially, we mitigate hallucinations, often introduced by text prompts, by using spatial information from other modalities to guide regional text-based conditioning. Each modality's guidance strength can also be controlled independently, allowing steering outputs toward different directions, such as increasing bokeh through depth or adjusting object prominence via segmentation. Extensive experiments demonstrate that our model surpasses state-of-the-art generative SISR methods, achieving superior visual quality and fidelity.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_The_Power_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Kangfu and Talebi, Hossein and Ardakani, Mojtaba and Patel, Vishal M. and Milanfar, Peyman and Delbracio, Mauricio},
    title     = {The Power of Context: How Multimodality Improves Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23141-23152}
}

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


title: MARBLE: Material Recomposition and Blending in CLIP-Space
authors: Ta Ying Cheng, Prafull Sharma, Mark Boss, Varun Jampani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13061-13071
affiliations: ["University of Oxford", "MIT CSAIL", "Stability AI", "Stability AI"]
cities: ["Oxford", "Cambridge", "San Francisco", "San Francisco"]
countries: ["UK", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_MARBLE_Material_Recomposition_and_Blending_in_CLIP-Space_CVPR_2025_paper.html

abstract:
Editing materials of objects in images based on exemplar images is an active area of research in computer vision and graphics. We propose MARBLE, a method for performing material blending and recomposing fine-grained material properties by finding material embeddings in CLIP-space and using that to control pre-trained text-to-image models. We improve exemplar-based material editing by finding a block in the denoising UNet responsible for material attribution. Given two material exemplar-images, we find directions in the CLIP-space for blending the materials. Further, we can achieve parametric control over fine-grained material attributes such as roughness, metallic, transparency, and glow using a shallow network to predict the direction for the desired material attribute change. We perform qualitative and quantitative analysis to demonstrate the efficacy of our proposed method. We also present the ability of our method to perform multiple edits in a single forward pass and applicability to painting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_MARBLE_Material_Recomposition_and_Blending_in_CLIP-Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_MARBLE_Material_Recomposition_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Ta Ying and Sharma, Prafull and Boss, Mark and Jampani, Varun},
    title     = {MARBLE: Material Recomposition and Blending in CLIP-Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13061-13071}
}

raw_affiliation:
[{"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "MIT CSAIL", "city": "Cambridge", "country": "USA"}, {"affiliation": "Stability AI", "city": "San Francisco", "country": "USA"}, {"affiliation": "Stability AI", "city": "San Francisco", "country": "USA"}]


title: Multirate Neural Image Compression with Adaptive Lattice Vector Quantization
authors: Hao Xu, Xiaolin Wu, Xi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7633-7642
affiliations: ["McMaster University", "Southwest Jiaotong University", "Nanyang Technological University"]
cities: ["Hamilton", "Chengdu", "Singapore"]
countries: ["Canada", "China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Multirate_Neural_Image_Compression_with_Adaptive_Lattice_Vector_Quantization_CVPR_2025_paper.html

abstract:
Recent research has explored integrating lattice vector quantization (LVQ) into learned image compression models. Due to its more efficient Voronoi covering of vector space than scalar quantization (SQ), LVQ achieves better rate-distortion (R-D) performance than SQ, while still retaining the low complexity advantage of SQ. However, existing LVQ-based methods have two shortcomings: 1) lack of a multirate coding mode, hence incapable to operate at different rates; 2) the use of a fixed lattice basis, hence nonadaptive to changing source distributions. To overcome these shortcomings, we propose a novel adaptive LVQ method, which is the first among LVQ-based methods to achieve both rate and domain adaptations. By scaling the lattice basis vector, our method can adjust the density of lattice points to achieve various bit rate targets, achieving superior R-D performance to current SQ-based variable rate models. Additionally, by using a learned invertible linear transformation between two different input domains, we can reshape the predefined lattice cell to better represent the target domain, further improving the R-D performance. To our knowledge, this paper represents the first attempt to propose a unified solution for rate adaptation and domain adaptation through quantizer design.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Multirate_Neural_Image_Compression_with_Adaptive_Lattice_Vector_Quantization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Multirate_Neural_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Hao and Wu, Xiaolin and Zhang, Xi},
    title     = {Multirate Neural Image Compression with Adaptive Lattice Vector Quantization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7633-7642}
}

raw_affiliation:
[{"affiliation": "McMaster University", "city": "Hamilton", "country": "Canada"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: EventFly: Event Camera Perception from Ground to the Sky
authors: Lingdong Kong, Dongyue Lu, Xiang Xu, Lai Xing Ng, Wei Tsang Ooi, Benoit R. Cottereau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1472-1484
cities: ["Singapore", "Singapore", "Nanjing", "Singapore", "Singapore", "Toulouse"]
countries: ["Singapore", "Singapore", "China", "Singapore", "Singapore", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_EventFly_Event_Camera_Perception_from_Ground_to_the_Sky_CVPR_2025_paper.html

abstract:
Cross-platform adaptation in event-based dense perception is crucial for deploying event cameras across diverse settings, such as vehicles, drones, and quadrupeds, each with unique motion dynamics, viewpoints, and class distributions. In this work, we introduce EventFly, a framework for robust cross-platform adaptation in event camera perception. Our approach comprises three key components: i) Event Activation Prior (EAP), which identifies high-activation regions in the target domain to minimize prediction entropy, fostering confident, domain-adaptive predictions; ii) EventBlend, a data-mixing strategy that integrates source and target event voxel grids based on EAP-driven similarity and density maps, enhancing feature alignment; and iii) EventMatch, a dual-discriminator technique that aligns features from source, target, and blended domains for better domain-invariant learning. To holistically assess cross-platform adaptation abilities, we introduce EXPo, a large-scale benchmark with diverse samples across vehicle, drone, and quadruped platforms. Extensive experiments validate our effectiveness, demonstrating substantial gains over popular adaptation methods. We hope this work can pave the way for more adaptive, high-performing event perception across diverse and complex environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_EventFly_Event_Camera_Perception_from_Ground_to_the_Sky_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_EventFly_Event_Camera_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kong_2025_CVPR,
    author    = {Kong, Lingdong and Lu, Dongyue and Xu, Xiang and Ng, Lai Xing and Ooi, Wei Tsang and Cottereau, Benoit R.},
    title     = {EventFly: Event Camera Perception from Ground to the Sky},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1472-1484}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CNRS@CREATE", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "Institute for Infocomm Research, A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "IPAL, CNRS IRL 2955, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "CerCo, CNRS UMR 5549, Universit\u00b4e Toulouse III", "city": "Toulouse", "country": "France"}]

affiliations:
["National University of Singapore", "CNRS@CREATE", "Nanjing University of Aeronautics and Astronautics", "Institute for Infocomm Research, A*STAR", "IPAL, CNRS IRL 2955, Singapore", "CerCo, CNRS UMR 5549, Universit\u00b4e Toulouse III"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25476-25486
cities: ["Guangzhou", "Seattle", "Beijing", "Hong Kong"]
countries: ["China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_Detect_Any_Mirrors_Boosting_Learning_Reliability_on_Large-Scale_Unlabeled_Data_CVPR_2025_paper.html

title:
Detect Any Mirrors: Boosting Learning Reliability on Large-Scale Unlabeled Data with an Iterative Data Engine

authors:
Zhaohu Xing, Lihao Liu, Yijun Yang, Hongqiu Wang, Tian Ye, Sixiang Chen, Wenxue Li, Guang Liu, Lei Zhu

abstract:
Mirror detection is a challenging task because a mirror's visual appearance varies depending on the reflected content. Due to limited annotated data, current methods failed to generalize well for detecting diverse mirror scenes. Semi-supervised learning with large-scale unlabeled data can improve generalization capabilities on mirror detection, but these methods often suffer from unreliable pseudo-labels due to distribution differences between labeled and unlabeled data, therefore affecting the learning process. To address this issue, we first collect a large-scale dataset of approximately 0.4 million mirror-related images from the internet, significantly expanding the data scale for mirror detection. To effectively exploit this unlabeled dataset, we propose the first semi-supervised framework (namely an iterative data engine) consisting of four steps: (1) mirror detection model training, (2) pseudo label prediction, (3) dual guidance scoring, and (4) selection of highly reliable pseudo labels. In each iteration of the data engine, we employ a geometric accuracy scoring approach to assess pseudo labels based on multiple segmentation metrics, and design a multi-modal agent-driven semantic scoring approach to enhance the semantic perception of pseudo labels. These two scoring approaches can effectively improve the reliability of pseudo labels by selecting unlabeled samples with higher scores. Our method demonstrates promising performance across three mirror detection tasks and exhibits strong generalization on unseen examples. Our code will be publicly available at \href https://github.com/ge-xing/DAM  https://github.com/ge-xing/DAM .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_Detect_Any_Mirrors_Boosting_Learning_Reliability_on_Large-Scale_Unlabeled_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_Detect_Any_Mirrors_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Zhaohu and Liu, Lihao and Yang, Yijun and Wang, Hongqiu and Ye, Tian and Chen, Sixiang and Li, Wenxue and Liu, Guang and Zhu, Lei},
    title     = {Detect Any Mirrors: Boosting Learning Reliability on Large-Scale Unlabeled Data with an Iterative Data Engine},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25476-25486}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}, {"affiliation": "Beijing Academy of Artificial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Hong Kong University of Science and Technology (Guangzhou)", "Amazon", "Beijing Academy of Artificial Intelligence", "The Hong Kong University of Science and Technology"]


title: CH3Depth: Efficient and Flexible Depth Foundation Model with Flow Matching
authors: Jiaqi Li, Yiran Wang, Jinghong Zheng, Junrui Zhang, Liao Shen, Tianqi Liu, Zhiguo Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7222-7232
affiliations: ["School of AIA, Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CH3Depth_Efficient_and_Flexible_Depth_Foundation_Model_with_Flow_Matching_CVPR_2025_paper.html

abstract:
Depth estimation is a fundamental task in 3D vision. An ideal depth estimation model is expected to embrace meticulous detail, temporal consistency, and high efficiency. Although existing foundation models can perform well in certain specific aspects, most of them fall short of fulfilling all the above requirements simultaneously. In this paper, we present CH_3Depth, an efficient and flexible model for depth estimation with flow matching to address this challenge. Specifically, 1) we reframe the optimization objective of flow matching as the Inversion by Direct Iteration (InDI) to improve accuracy. 2) To enhance efficiency, we propose non-uniform sampling to achieve better prediction with fewer sampling steps. 3) We design the Latent Temporal Stabilizer (LTS) to enhance temporal consistency by aggregating latent codes of adjacent frames, enabling our method to be lightweight and compatible for video depth estimation. CH_3Depth achieves state-of-the-art performance in zero-shot evaluations across multiple image and video datasets, excelling in prediction accuracy, efficiency, and temporal consistency, highlighting its potential as the next foundation model for depth estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CH3Depth_Efficient_and_Flexible_Depth_Foundation_Model_with_Flow_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CH3Depth_Efficient_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiaqi and Wang, Yiran and Zheng, Jinghong and Zhang, Junrui and Shen, Liao and Liu, Tianqi and Cao, Zhiguo},
    title     = {CH3Depth: Efficient and Flexible Depth Foundation Model with Flow Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7222-7232}
}

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


title: Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors
authors: Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy, Lourdes Agapito, Jerome Revaud
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1071-1081
affiliations: ["UCL", "Naver Labs Europe"]
cities: ["London", "Grenoble"]
countries: ["UK", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Pow3R_Empowering_Unconstrained_3D_Reconstruction_with_Camera_and_Scene_Priors_CVPR_2025_paper.html

abstract:
We present Pow3R, a novel large 3D vision regression model that is highly versatile in the input modalities it accepts. Unlike previous feed-forward models that lack any mechanism to exploit known camera or scene priors at test time, Pow3R incorporates any combination of auxiliary information such as intrinsics, relative pose, dense or sparse depth, alongside input images, within a single network. Building upon the recent DUSt3R paradigm, a transformer-based architecture that leverages powerful pre-training, our lightweight and versatile conditioning acts as additional guidance for the network to predict more accurate estimates when auxiliary information is available. During training we feed the model with random subsets of modalities at each iteration, which enables the model to operate under different levels of known priors at test time. This in turn opens up new capabilities, such as performing inference in native image resolution, or point-cloud completion. Our experiments on 3D reconstruction, depth completion, multi-view depth prediction, multi-view stereo, and multi-view pose estimation tasks yield state-of-the-art results and confirm the effectiveness of Pow3R at exploiting all available information. The project webpage is https://europe.naverlabs.com/pow3r.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Pow3R_Empowering_Unconstrained_3D_Reconstruction_with_Camera_and_Scene_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Pow3R_Empowering_Unconstrained_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Wonbong and Weinzaepfel, Philippe and Leroy, Vincent and Agapito, Lourdes and Revaud, Jerome},
    title     = {Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1071-1081}
}

raw_affiliation:
[{"affiliation": "UCL", "city": "London", "country": "UK"}, {"affiliation": "Naver Labs Europe", "city": "Grenoble", "country": "France"}]


title: Efficient Visual State Space Model for Image Deblurring
authors: Lingshun Kong, Jiangxin Dong, Jinhui Tang, Ming-Hsuan Yang, Jinshan Pan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12710-12719
cities: ["Nanjing", "Merced", "Mountain View"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_Efficient_Visual_State_Space_Model_for_Image_Deblurring_CVPR_2025_paper.html

abstract:
Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. While ViTs generally outperform CNNs by effectively capturing long-range dependencies and input-specific characteristics, their computational complexity increases quadratically with image resolution. This limitation hampers their practical application in high-resolution image restoration. In this paper, we propose a simple yet effective visual state space model (EVSSM) for image deblurring, leveraging the benefits of state space models (SSMs) to visual data. In contrast to existing methods that employ several fixed-direction scanning for feature extraction, which significantly increases the computational cost, we develop an efficient visual scan block that applies various geometric transformations before each SSM-based module, capturing useful non-local information and maintaining high efficiency. In addition, to more effectively capture and represent local information, we propose an efficient discriminative frequency domain-based feedforward network (EDFFN) which can effectively estimate useful frequency information for latent clear image restoration. Extensive experimental results show that the proposed EVSSM performs favorably against state-of-the-art methods on benchmark datasets and real-world images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_Efficient_Visual_State_Space_Model_for_Image_Deblurring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_Efficient_Visual_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kong_2025_CVPR,
    author    = {Kong, Lingshun and Dong, Jiangxin and Tang, Jinhui and Yang, Ming-Hsuan and Pan, Jinshan},
    title     = {Efficient Visual State Space Model for Image Deblurring},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12710-12719}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "University of California, Merced", "city": "Merced", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]

affiliations:
["Nanjing University of Science and Technology", "University of California, Merced", "Google DeepMind"]


title: 4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22001-22011
cities: ["Cambridge", "Beijing", "Stony Brook", "Providence", "Z\u00fcrich"]
countries: ["USA", "China", "USA", "USA", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_4D_LangSplat_4D_Language_Gaussian_Splatting_via_Multimodal_Large_Language_CVPR_2025_paper.html

authors:
Wanhua Li, Renping Zhou, Jiawei Zhou, Yingwei Song, Johannes Herter, Minghan Qin, Gao Huang, Hanspeter Pfister

abstract:
Learning 4D language fields to enable time-sensitive, open-ended language queries in dynamic scenes is essential for many real-world applications. While LangSplat successfully grounds CLIP features into 3D Gaussian representations, achieving precision and efficiency in 3D static scenes, it lacks the ability to handle dynamic 4D fields as CLIP, designed for static image-text tasks, cannot capture temporal dynamics in videos. Real-world environments are inherently dynamic, with object semantics evolving over time. Building a precise 4D language field necessitates obtaining pixel-aligned, object-wise video features, which current vision models struggle to achieve. To address these challenges, we propose 4D LangSplat, which learns 4D language fields to handle time-agnostic or time-sensitive open-vocabulary queries in dynamic scenes efficiently. 4D LangSplat bypasses learning the language field from vision features and instead learns directly from text generated from object-wise video captions via Multimodal Large Language Models (MLLMs). Specifically, we propose a multimodal object-wise video prompting method, consisting of visual and text prompts that guide MLLMs to generate detailed, temporally consistent, high-quality captions for objects throughout a video. These captions are encoded using a Large Language Model into high-quality sentence embeddings, which then serve as pixel-aligned, object-specific feature supervision, facilitating open-vocabulary text queries through shared embedding spaces. Recognizing that objects in 4D scenes exhibit smooth transitions across states, we further propose a status deformable network to model these continuous changes over time effectively. Our results across multiple benchmarks demonstrate that 4D LangSplat attains precise and efficient results for both time-sensitive and time-agnostic open-vocabulary queries.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_4D_LangSplat_4D_Language_Gaussian_Splatting_via_Multimodal_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_4D_LangSplat_4D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wanhua and Zhou, Renping and Zhou, Jiawei and Song, Yingwei and Herter, Johannes and Qin, Minghan and Huang, Gao and Pfister, Hanspeter},
    title     = {4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22001-22011}
}

raw_affiliation:
[{"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "ETH Z\u00a8urich", "city": "Z\u00fcrich", "country": "Switzerland"}]

affiliations:
["Harvard University", "Tsinghua University", "Stony Brook University", "Brown University", "ETH Z\u00a8urich"]


title: MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language Tracking
authors: Xinqi Liu, Li Zhou, Zikun Zhou, Jianqiu Chen, Zhenyu He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8731-8741
affiliations: ["Harbin Institute of Technology, Shenzhen", "Pengcheng Laboratory"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MambaVLT_Time-Evolving_Multimodal_State_Space_Model_for_Vision-Language_Tracking_CVPR_2025_paper.html

abstract:
The vision-language tracking task aims to perform object tracking based on various modality references. Existing Transformer-based vision-language tracking methods have made remarkable progress by leveraging the global modeling ability of self-attention. However, current approaches still face challenges in effectively exploiting the temporal information and dynamically updating reference features during tracking. Recently, the State Space Model (SSM), known as Mamba, has shown astonishing ability in efficient long-sequence modeling. Particularly, its state space evolving process demonstrates promising capabilities in memorizing multimodal temporal information with linear complexity. Witnessing its success, we propose a Mamba-based vision-language tracking model to exploit its state space evolving ability in temporal space for robust multimodal tracking, dubbed MambaVLT. In particular, our approach mainly integrates a time-evolving hybrid state space block and a selective locality enhancement block, to capture contextual information for multimodal modeling and adaptive reference feature update. Besides, we introduce a modality-selection module that dynamically adjusts the weighting between visual and language references, mitigating potential ambiguities from either reference type. Extensive experimental results show that our method performs favorably against state-of-the-art trackers across diverse benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MambaVLT_Time-Evolving_Multimodal_State_Space_Model_for_Vision-Language_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MambaVLT_Time-Evolving_Multimodal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xinqi and Zhou, Li and Zhou, Zikun and Chen, Jianqiu and He, Zhenyu},
    title     = {MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8731-8741}
}

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


title: Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels
authors: Pierre Vuillecard, Jean-Marc Odobez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13508-13518
cities: ["Martigny", "Lausanne"]
countries: ["Switzerland", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vuillecard_Enhancing_3D_Gaze_Estimation_in_the_Wild_using_Weak_Supervision_CVPR_2025_paper.html

abstract:
Accurate 3D gaze estimation in unconstrained real-world environments remains a significant challenge due to variations in appearance, head pose, occlusion, and the limited availability of in-the-wild 3D gaze datasets. To address these challenges, we introduce a novel Self-Training Weakly-Supervised Gaze Estimation framework (ST-SWGE). This two-stage learning framework leverages diverse 2D gaze datasets, such as gaze-following data, which offer rich variations in appearances, natural scenes, and gaze distributions, and proposes an approach to generate 3D pseudo-labels and enhance model generalization. Furthermore, traditional modality-specific models, designed separately for images or videos, limit the effective use of available training data. To overcome this, we propose the Gaze Transformer (GaT), a modality-agnostic architecture capable of simultaneously learning static and dynamic gaze information from both image and video datasets. By combining 3D video datasets with 2D gaze target labels from gaze following tasks, our approach achieves the following key contributions: (i) Significant state-of-the-art improvements in within-domain and cross-domain generalization on unconstrained benchmarks like Gaze360 and GFIE, with notable cross-modal gains in video gaze estimation; (ii) Superior cross-domain performance on datasets such as MPIIFaceGaze and Gaze360 compared to frontal face methods. Code and pre-trained models will be released to the community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vuillecard_Enhancing_3D_Gaze_Estimation_in_the_Wild_using_Weak_Supervision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vuillecard_Enhancing_3D_Gaze_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Vuillecard_2025_CVPR,
    author    = {Vuillecard, Pierre and Odobez, Jean-Marc},
    title     = {Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13508-13518}
}

raw_affiliation:
[{"affiliation": "Idiap Research Institute, Switzerland", "city": "Martigny", "country": "Switzerland"}, {"affiliation": "Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne, Switzerland", "city": "Lausanne", "country": "Switzerland"}]

affiliations:
["Idiap Research Institute, Switzerland", "Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne, Switzerland"]


authors: Zhiwei Jia, Yuesong Nan, Huixi Zhao, Gengdai Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12912-12922
raw_affiliation: [{"affiliation": "Zoom Communications", "city": "San Jose", "country": "USA"}]
affiliations: ["Zoom Communications"]
cities: ["San Jose"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_Reward_Fine-Tuning_Two-Step_Diffusion_Models_via_Learning_Differentiable_Latent-Space_Surrogate_CVPR_2025_paper.html

title:
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward

abstract:
Recent research has shown that fine-tuning diffusion models (DMs) with arbitrary rewards, including non-differentiable ones, is feasible with reinforcement learning (RL) techniques, enabling flexible model alignment. However, applying existing RL methods to step-distilled DMs is challenging for ultra-fast (<=2-step) image generation. Our analysis suggests several limitations of policy-based RL methods such as PPO or DPO toward this goal. Based on the insights, we propose fine-tuning DMs with learned differentiable surrogate rewards. Our method, named LaSRO, learns surrogate reward models in the latent space of SDXL to convert arbitrary rewards into differentiable ones for effective reward gradient guidance. LaSRO leverages pre-trained latent DMs for reward modeling and tailors reward optimization for <=2-step image generation with efficient off-policy exploration. LaSRO is effective and stable for improving ultra-fast image generation with different reward objectives, outperforming popular RL methods including DDPO and Diffusion-DPO. We further show LaSRO's connection to value-based RL, providing theoretical insights. See our webpage at https://sites.google.com/view/lasro.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_Reward_Fine-Tuning_Two-Step_Diffusion_Models_via_Learning_Differentiable_Latent-Space_Surrogate_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_Reward_Fine-Tuning_Two-Step_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Zhiwei and Nan, Yuesong and Zhao, Huixi and Liu, Gengdai},
    title     = {Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12912-12922}
}


title: Detecting Out-of-Distribution Through the Lens of Neural Collapse
authors: Litian Liu, Yao Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15424-15433
affiliations: ["MIT", "UC Santa Barbara"]
cities: ["Cambridge", "Santa Barbara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Detecting_Out-of-Distribution_Through_the_Lens_of_Neural_Collapse_CVPR_2025_paper.html

abstract:
Out-of-Distribution (OOD) detection is critical for safe deployment; however, existing detectors often struggle to generalize across datasets of varying scales and model architectures, and some can incur high computational costs in real-world applications. Inspired by the phenomenon of Neural Collapse, we propose a versatile and efficient OOD detection method. Specifically, we re-characterize prior observations that in-distribution (ID) samples form clusters, demonstrating that, with appropriate centering, these clusters align closely with model weight vectors. Additionally, we reveal that ID features tend to expand into a simplex Equiangular Tight Frame, explaining the common observation that ID features are situated farther from the origin than OOD features. Incorporating both insights from Neural Collapse, our OOD detector leverages feature proximity to weight vectors and complements this approach by using feature norms to effectively filter out OOD samples. Extensive experiments on off-the-shelf models demonstrate the robustness of our OOD detector across diverse scenarios, mitigating generalization discrepancies and enhancing overall performance, with inference latency comparable to that of the basic softmax-confidence detector. Code is available at: https://github.com/litianliu/NCI-OOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Detecting_Out-of-Distribution_Through_the_Lens_of_Neural_Collapse_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Detecting_Out-of-Distribution_Through_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Litian and Qin, Yao},
    title     = {Detecting Out-of-Distribution Through the Lens of Neural Collapse},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15424-15433}
}

raw_affiliation:
[{"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "UC Santa Barbara", "city": "Santa Barbara", "country": "USA"}]


title: Adaptive Parameter Selection for Tuning Vision-Language Models
authors: Yi Zhang, Yi-Xuan Deng, Meng-Hao Guo, Shi-Min Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4280-4290
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Adaptive_Parameter_Selection_for_Tuning_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) like CLIP have been widely used in various specific tasks.Parameter-efficient fine-tuning (PEFT) methods, such as prompt and adapter tuning,have become key techniques for adapting these models to specific domains.However, existing approaches rely on prior knowledgeto manually identify the locations requiring fine-tuning.Adaptively selecting which parameters in VLMs should be tuned remains unexplored. In this paper, we propose CLIP with Adaptive Selective Tuning (CLIP-AST), which can be used to automatically select critical parameters in VLMs for fine-tuning for specific tasks.It opportunely leveragesthe adaptive learning rate in the optimizer and improves model performance without extra parameter overhead. We conduct extensive experiments on 13 benchmarks, such as ImageNet, Food101, Flowers102, etc,with different settings, including few-shot learning, base-to-novel class generalization, and out-of-distribution. The results show that CLIP-AST consistently outperforms the original CLIP model as well as its variantsand achieves state-of-the-art (SOTA) performance in all cases. For example, with the 16-shot learning, CLIP-AST surpasses GraphAdapter and PromptSRC by 3.56% and 2.20% in average accuracy on 11 datasets, respectively.Code will be publicly available.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Adaptive_Parameter_Selection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yi and Deng, Yi-Xuan and Guo, Meng-Hao and Hu, Shi-Min},
    title     = {Adaptive Parameter Selection for Tuning Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4280-4290}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist, Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhili College, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University", "BNRist, Department of Computer Science and Technology, Tsinghua University", "Zhili College, Tsinghua University"]


title: MotionMap: Representing Multimodality in Human Pose Forecasting
authors: Reyhaneh Hosseininejad, Megh Shukla, Saeed Saadatnejad, Mathieu Salzmann, Alexandre Alahi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22680-22689
cities: ["Lausanne", "Lausanne"]
countries: ["Switzerland", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hosseininejad_MotionMap_Representing_Multimodality_in_Human_Pose_Forecasting_CVPR_2025_paper.html

abstract:
Human pose forecasting is inherently multimodal since multiple future motions exist for an observed pose sequence. However, learning this multimodality is challenging since the task is ill-posed. To address this issue, we propose an alternative paradigm to make the task well-posed. Additionally, while state-of-the-art methods predict multimodality, this is attained through a large volume of predictions obtained by oversampling. However, such an approach glosses over key questions: (1) Can we capture multimodality by efficiently sampling a smaller number of predictions? (2) Subsequently, which of the predicted futures is more likely for an observed pose sequence? We address these questions with MotionMap, a simple yet effective heatmap based representation for multimodality. We extend heatmaps to represent a spatial distribution over the space of all possible motions, where different local maxima correspond to different forecasts for a given observation. Not only can MotionMap capture a variable number of modes per observation, but it also provides confidence measures for different modes. Further, MotionMap captures rare modes that are non-trivial to evaluate yet critical for robustness. Finally, MotionMap allows us to introduce the notion of uncertainty and controllability over the forecasted pose sequence. We support our claims through multiple qualitative and quantitative experiments using popular 3D human pose datasets: Human3.6M and AMASS, highlighting the strengths and limitations of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hosseininejad_MotionMap_Representing_Multimodality_in_Human_Pose_Forecasting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hosseininejad_MotionMap_Representing_Multimodality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hosseininejad_2025_CVPR,
    author    = {Hosseininejad, Reyhaneh and Shukla, Megh and Saadatnejad, Saeed and Salzmann, Mathieu and Alahi, Alexandre},
    title     = {MotionMap: Representing Multimodality in Human Pose Forecasting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22680-22689}
}

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

affiliations:
["\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "Swiss Data Science Centre (SDSC)"]


authors: Hanrui Zhao, Niuniu Qi, Mengxin Ren, Banglong Liu, Shuming Shi, Zhengfeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10275-10284
affiliations: ["Shanghai Key Lab of Trustworthy Computing of East China Normal University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Learning-enabled_Polynomial_Lyapunov_Function_Synthesis_via_High-Accuracy_Counterexample-Guided_Framework_CVPR_2025_paper.html

title:
Learning-enabled Polynomial Lyapunov Function Synthesis via High-Accuracy Counterexample-Guided Framework

abstract:
Polynomial Lyapunov function \mathcal V (x) provides mathematically rigorous that converts stability analysis into efficiently solvable optimization problem. Traditional numerical methods rely on user-defined templates, while emerging neural \mathcal V (x) offer flexibility but exhibit poor generalization yield from naive  Square  polynomial networks. In this paper, we propose a novel learning-enabled polynomial \mathcal V (x) synthesis approach, where a data-driven machine learning process guided by target-based sampling to fit candidate \mathcal V (x) which naturally compatible with the sum-of-squares (SOS) soundness verification. The framework is structured as an iterative loop between a  Learner  and a  Verifier , where the  Learner  trains expressive polynomial \mathcal V (x) network via polynomial expansions, while the  Verifier  encodes learned candidates with SOS constraints to identify a real \mathcal V (x) by solving LMI feasibility test problems. The entire procedure is driven by a high-accuracy counterexample guidance technique to further enhance efficiency. Experimental results demonstrate that our approach outperforms both SMT-based polynomial neural Lyapunov function synthesis and traditional SOS method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Learning-enabled_Polynomial_Lyapunov_Function_Synthesis_via_High-Accuracy_Counterexample-Guided_Framework_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Hanrui and Qi, Niuniu and Ren, Mengxin and Liu, Banglong and Shi, Shuming and Yang, Zhengfeng},
    title     = {Learning-enabled Polynomial Lyapunov Function Synthesis via High-Accuracy Counterexample-Guided Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10275-10284}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Trustworthy Computing of East China Normal University", "city": "Shanghai", "country": "China"}]


title: Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21317-21327
affiliations: ["Chongqing University", "KAUST", "Skoltech", "AIRI"]
cities: ["Chongqing", "Thuwal", "Moscow", "Moscow"]
countries: ["China", "Saudi Arabia", "Russia", "Russia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Factored-NeuS_Reconstructing_Surfaces_Illumination_and_Materials_of_Possibly_Glossy_Objects_CVPR_2025_paper.html

authors:
Yue Fan, Ningjing Fan, Ivan Skorokhodov, Oleg Voynov, Savva Ignatyev, Evgeny Burnaev, Peter Wonka, Yiqun Wang

abstract:
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. In the first stage, we reconstruct the scene radiance and signed distance function (SDF) with a novel regularization strategy for specular reflections. We propose to explain a pixel color using both surface and volume rendering jointly, which allows for handling complex view-dependent lighting effects for surface reconstruction. In the second stage, we distill light visibility and indirect illumination from the learned SDF and radiance field using learnable mapping functions. Finally, we design a method for estimating the ratio of incoming direct light reflected in a specular manner and use it to reconstruct the materials and direct illumination. Experimental results demonstrate that the proposed method outperforms the current state-of-the-art in recovering surfaces, materials, and lighting without relying on any additional data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Factored-NeuS_Reconstructing_Surfaces_Illumination_and_Materials_of_Possibly_Glossy_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Factored-NeuS_Reconstructing_Surfaces_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Yue and Fan, Ningjing and Skorokhodov, Ivan and Voynov, Oleg and Ignatyev, Savva and Burnaev, Evgeny and Wonka, Peter and Wang, Yiqun},
    title     = {Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21317-21327}
}

raw_affiliation:
[{"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "Skoltech", "city": "Moscow", "country": "Russia"}, {"affiliation": "AIRI", "city": "Moscow", "country": "Russia"}]


authors: Yangming Zhang, Wenqi Jia, Wei Niu, Miao Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26673-26682
cities: ["Arlington", "Athens"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_GaussianSpa_An_Optimizing-Sparsifying_Simplification_Framework_for_Compact_and_High-Quality_3D_CVPR_2025_paper.html

title:
GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting

abstract:
3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions to model scene geometry. However, 3DGS suffers from substantial memory requirements to store the large amount of Gaussians, hindering its efficiency and practicality. To address this challenge, we introduce GaussianSpa, an optimization-based simplification framework for compact and high-quality 3DGS. Specifically, we formulate the simplification objective as a constrained optimization problem associated with the 3DGS training. Correspondingly, we propose an efficient "optimizing-sparsifying" solution for the formulated problem, alternately solving two independent sub-problems and gradually imposing substantial sparsity onto the Gaussians in the 3DGS training process. We conduct quantitative and qualitative evaluations on various datasets, demonstrating the superiority of GaussianSpa over existing state-of-the-art approaches. Notably, GaussianSpa achieves an average PSNR improvement of 0.9 dB on the real-world Deep Blending dataset with 10X fewer Gaussians compared to the vanilla 3DGS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_GaussianSpa_An_Optimizing-Sparsifying_Simplification_Framework_for_Compact_and_High-Quality_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_GaussianSpa_An_Optimizing-Sparsifying_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yangming and Jia, Wenqi and Niu, Wei and Yin, Miao},
    title     = {GaussianSpa: An ''Optimizing-Sparsifying'' Simplification Framework for Compact and High-Quality 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26673-26682}
}

raw_affiliation:
[{"affiliation": "Dept. of Computer Science, University of Texas at Arlington", "city": "Arlington", "country": "USA"}, {"affiliation": "School of Computing, University of Georgia", "city": "Athens", "country": "USA"}]

affiliations:
["Dept. of Computer Science, University of Texas at Arlington", "School of Computing, University of Georgia"]


title: Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
authors: Jiang Wu, Rui Li, Yu Zhu, Rong Guo, Jinqiu Sun, Yanning Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11307-11316
raw_affiliation: [{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]
affiliations: ["Northwestern Polytechnical University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Sparse2DGS_Geometry-Prioritized_Gaussian_Splatting_for_Surface_Reconstruction_from_Sparse_Views_CVPR_2025_paper.html

abstract:
We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Motion points for initialization. While learning-based Multi-view Stereo (MVS) provides dense 3D points, directly combining it with Gaussian Splatting leads to suboptimal results due to the ill-posed nature of sparse-view geometric optimization. We propose Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction. Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under ill-posed conditions. Sparse2DGS outperforms existing methods by notable margins, with 1.13 Chamfer Distance error compared to 2DGS (2.81) on the DTU dataset using 3 views. Meanwhile, our method is 2x faster than NeRF-based fine-tuning approach. Code is available at https://github.com/Wuuu3511/Sparse2DGS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Sparse2DGS_Geometry-Prioritized_Gaussian_Splatting_for_Surface_Reconstruction_from_Sparse_Views_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Sparse2DGS_Geometry-Prioritized_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jiang and Li, Rui and Zhu, Yu and Guo, Rong and Sun, Jinqiu and Zhang, Yanning},
    title     = {Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11307-11316}
}


title: VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation
authors: Saksham Singh Kushwaha, Yapeng Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13529-13539
raw_affiliation: [{"affiliation": "The University of Texas at Dallas", "city": "Dallas", "country": "USA"}]
affiliations: ["The University of Texas at Dallas"]
cities: ["Dallas"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kushwaha_VinTAGe_Joint_Video_and_Text_Conditioning_for_Holistic_Audio_Generation_CVPR_2025_paper.html

abstract:
Recent advances in audio generation have focused on text-to-audio (T2A) and video-to-audio (V2A) tasks. However, T2A or V2A methods cannot generate holistic sounds (onscreen and off-screen). This is because T2A cannot generate sounds aligning with onscreen objects, while V2A cannot generate semantically complete (offscreen sounds missing). In this work, we address the task of holistic audio generation: given a video and a text prompt, we aim to generate both onscreen and offscreen sounds that are temporally synchronized with the video and semantically aligned with text and video. Previous approaches for joint text and video-to-audio generation often suffer from modality bias, favoring one modality over the other. To overcome this limitation, we introduce VinTAGe, a flow-based transformer model that jointly considers text and video to guide audio generation. Our framework comprises two key components: a Visual-Text Encoder and a Joint VT-SiT model. To reduce modality bias and improve generation quality, we employ pretrained uni-modal text-to-audio and video-to-audio generation models for additional guidance. Due to the lack of appropriate benchmarks, we also introduce VinTAGe-Bench, a dataset of 636 video-text-audio pairs containing both onscreen and offscreen sounds. Our comprehensive experiments on VinTAGe-Bench demonstrate that joint text and visual interaction is necessary for holistic audio generation. Furthermore, VinTAGe achieves state-of-the-art results on the VGGSound benchmark.We will release our pretrained models and the VinTAGe-Bench dataset to facilitate future research in this exciting field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kushwaha_VinTAGe_Joint_Video_and_Text_Conditioning_for_Holistic_Audio_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kushwaha_VinTAGe_Joint_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kushwaha_2025_CVPR,
    author    = {Kushwaha, Saksham Singh and Tian, Yapeng},
    title     = {VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13529-13539}
}


title: Efficient Decoupled Feature 3D Gaussian Splatting via Hierarchical Compression
authors: Zhenqi Dai, Ting Liu, Yanning Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11156-11166
cities: ["Xi'an", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Efficient_Decoupled_Feature_3D_Gaussian_Splatting_via_Hierarchical_Compression_CVPR_2025_paper.html

abstract:
Efficient 3D scene representation has become a key challenge with the rise of 3D Gaussian Splatting (3DGS), particularly when incorporating semantic information into the scene representation. Existing 3DGS-based methods embed both color and high-dimensional semantic features into a single field,  leading to significant storage and computational overhead. To mitigate this, we propose Decoupled Feature 3D Gaussian Splatting (DF-3DGS), a novel method that decouples the color and semantic fields, thereby reducing the number of 3D Gaussians required for semantic representation. We then introduce a hierarchical compression strategy that first employs our novel quantization approach with dynamic codebook evolution to reduce data size, followed by a scene-specific autoencoder for further compression of the semantic feature dimensions. This multi-stage approach results in a compact representation that enhances both storage efficiency and reconstruction speed. Experimental results demonstrate that DF-3DGS outperforms previous 3DGS-based methods, achieving faster training and rendering times while requiring less storage, without sacrificing performance--in fact, it improves performance in the novel view semantic segmentation task. Specifically, DF-3DGS achieves remarkable improvements over Feature 3DGS, reducing training time by 10xand storage by 20x, while improving the mIoU of novel view semantic segmentation by 4%. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Efficient_Decoupled_Feature_3D_Gaussian_Splatting_via_Hierarchical_Compression_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Zhenqi and Liu, Ting and Zhang, Yanning},
    title     = {Efficient Decoupled Feature 3D Gaussian Splatting via Hierarchical Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11156-11166}
}

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

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


title: CountLLM: Towards Generalizable Repetitive Action Counting via Large Language Model
authors: Ziyu Yao, Xuxin Cheng, Zhiqi Huang, Lei Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19143-19153
affiliations: ["Peking University", "University of Washington", "University of Copenhagen"]
cities: ["Beijing", "Seattle", "Copenhagen"]
countries: ["China", "USA", "Denmark"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_CountLLM_Towards_Generalizable_Repetitive_Action_Counting_via_Large_Language_Model_CVPR_2025_paper.html

abstract:
Repetitive action counting, which aims to count periodic movements in a video, is valuable for video analysis applications such as fitness monitoring. However, existing methods largely rely on regression networks with limited representational capacity, which hampers their ability to accurately capture variable periodic patterns. Additionally, their supervised learning on narrow, limited training sets leads to overfitting and restricts their ability to generalize across diverse scenarios. To address these challenges, we propose CountLLM, the first large language model (LLM)-based framework that takes video data and periodic text prompts as inputs and outputs the desired counting value. CountLLM leverages the rich clues from explicit textual instructions and the powerful representational capabilities of pre-trained LLMs for repetitive action counting. To effectively guide CountLLM, we develop a periodicity-based structured template for instructions that describes the properties of periodicity and implements a standardized answer format to ensure consistency. Additionally, we propose a progressive multimodal training paradigm to enhance the periodicity-awareness of the LLM. Empirical evaluations on widely recognized benchmarks demonstrate CountLLM's superior performance and generalization, particularly in handling novel and out-of-domain actions that deviate significantly from the training data, offering a promising avenue for repetitive action counting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_CountLLM_Towards_Generalizable_Repetitive_Action_Counting_via_Large_Language_Model_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Ziyu and Cheng, Xuxin and Huang, Zhiqi and Li, Lei},
    title     = {CountLLM: Towards Generalizable Repetitive Action Counting via Large Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19143-19153}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Copenhagen", "city": "Copenhagen", "country": "Denmark"}]


title: Navigating the Unseen: Zero-shot Scene Graph Generation via Capsule-Based Equivariant Features
authors: Wenhuan Huang, Yi JI, Guiqian Zhu, Li Ying, Chunping Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29448-29457
affiliations: ["School of Computer Science and Technology, Soochow University"]
cities: ["Suzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Navigating_the_Unseen_Zero-shot_Scene_Graph_Generation_via_Capsule-Based_Equivariant_CVPR_2025_paper.html

abstract:
In scene graph generation (SGG), the accurate prediction of unseen triples is essential for its effectiveness in downstream vision-language tasks. We hypothesize that the predicates of unseen triples can be viewed as transformations of seen predicates in feature space, and the essence of the zero-shot task is to bridge the gap caused by this transformation. Traditional models, however, have difficulty addressing this challenge, which we attribute to their inability to model the predicates equivariant. To overcome this limitation, we introduce a novel framework based on capsule networks (CAPSGG). We propose a Three-Stream Pipeline that generates modality-specific representations for predicates, while building low-level predicate capsules of these modalities. Then these capsules are aggregated into high-level predicate capsules using a Routing Capsule Layer. In addition, we introduce GroupLoss to aggregate capsules with the same predicate label into groups. This replaces the global loss with the intra-group loss, effectively balancing the learning of predicate invariance and equivariant features, while mitigating the impact of the severe long-tail distribution of the predicate categories. Our extensive experiments demonstrate the notable superiority of our approach over state-of-the-art methods, with zero-shot indicators outperforming up to 132.26% on SGCls task than the T-CAR [21]. Our code will be available upon publication.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Navigating_the_Unseen_Zero-shot_Scene_Graph_Generation_via_Capsule-Based_Equivariant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Navigating_the_Unseen_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Wenhuan and JI, Yi and Zhu, Guiqian and Ying, Li and Liu, Chunping},
    title     = {Navigating the Unseen: Zero-shot Scene Graph Generation via Capsule-Based Equivariant Features},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29448-29457}
}

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


title: VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24657-24668
affiliations: ["HKU", "SCUT", "SJTU", "PKU", "UW", "Allen AI"]
cities: ["Hong Kong", "Guangzhou", "Shanghai", "Beijing", "Seattle", "Seattle"]
countries: ["China", "China", "China", "China", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VL-RewardBench_A_Challenging_Benchmark_for_Vision-Language_Generative_Reward_Models_CVPR_2025_paper.html

authors:
Lei Li, Yuancheng Wei, Zhihui Xie, Xuqing Yang, Yifan Song, Peiyi Wang, Chenxin An, Tianyu Liu, Sujian Li, Bill Yuchen Lin, Lingpeng Kong, Qi Liu

abstract:
Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current assessment methods primarily rely on AI-annotated preference labels from traditional VL tasks, which can introduce biases and often fail to effectively challenge state-of-the-art models. To address these limitations, we introduce VL-RewardBench, a comprehensive benchmark spanning general multimodal queries, visual hallucination detection, and complex reasoning tasks. Through our AI-assisted annotation pipeline that combines sample selection with human verification, we curate 1,250 high-quality examples specifically designed to probe VL-GenRMs limitations. Comprehensive evaluation across 16 leading large vision-language models demonstrates VL-RewardBench's effectiveness as a challenging testbed, where even GPT-4o achieves only 65.4% accuracy, and state-of-the-art open-source models such as Qwen2-VL-72B, struggle to surpass random-guessing. Importantly, performance on VL-RewardBench strongly correlates (Pearson's r > 0.9) with MMMU-Pro accuracy using Best-of-N sampling with VL-GenRMs. Analysis experiments uncover three critical insights for improving VL-GenRMs: (i) models predominantly fail at basic visual perception tasks rather than reasoning tasks; (ii) inference-time scaling benefits vary dramatically by model capacity; and (iii) training VL-GenRMs to learn to judge substantially boosts judgment capability (+14.7% accuracy for a 7B VL-GenRM). We believe VL-RewardBench along with the experimental insights will become a valuable resource for advancing VL-GenRMs. Project page: https://vl-rewardbench.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VL-RewardBench_A_Challenging_Benchmark_for_Vision-Language_Generative_Reward_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Lei and Wei, Yuancheng and Xie, Zhihui and Yang, Xuqing and Song, Yifan and Wang, Peiyi and An, Chenxin and Liu, Tianyu and Li, Sujian and Lin, Bill Yuchen and Kong, Lingpeng and Liu, Qi},
    title     = {VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24657-24668}
}

raw_affiliation:
[{"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "SCUT", "city": "Guangzhou", "country": "China"}, {"affiliation": "SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "PKU", "city": "Beijing", "country": "China"}, {"affiliation": "UW", "city": "Seattle", "country": "USA"}, {"affiliation": "Allen AI", "city": "Seattle", "country": "USA"}]


title: ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis
authors: Yun Chang, Leonor Fermoselle, Duy Ta, Bernadette Bucher, Luca Carlone, Jiuguang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29458-29468
affiliations: ["Luca Carlone", "Jiuguang Wang", "Yun Chang", "Leonor Fermoselle", "Duy Ta", "Bernadette Bucher"]
cities: ["Boston", "Boston", "Boston", "Paris", "Boston", "Boston"]
countries: ["USA", "USA", "USA", "France", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_ASHiTA_Automatic_Scene-grounded_HIerarchical_Task_Analysis_CVPR_2025_paper.html

abstract:
While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract, high-level instructions to a 3D scene. High-level instructions might not explicitly invoke semantic elements in the scene, and even the process of breaking a high-level task into a set of more concrete subtasks --a process called hierarchical task analysis-- is environment-dependent. In this work, we propose ASHiTA, the first framework that generates a task hierarchy grounded to a 3D scene graph by breaking down high-level tasks into grounded subtasks. ASHiTA alternates LLM-assisted hierarchical task analysis --to generate the task breakdown-- with task-driven scene graph construction to generate a suitable representation of the environment. Our experiments show that ASHiTA performs significantly better than LLM baselines in breaking down high-level tasks into environment-dependent subtasks and is additionally able to achieve grounding performance comparable to state-of-the-art methods

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_ASHiTA_Automatic_Scene-grounded_HIerarchical_Task_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_ASHiTA_Automatic_Scene-grounded_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Yun and Fermoselle, Leonor and Ta, Duy and Bucher, Bernadette and Carlone, Luca and Wang, Jiuguang},
    title     = {ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29458-29468}
}

raw_affiliation:
[{"affiliation": "Luca Carlone", "city": "Boston", "country": "USA"}, {"affiliation": "Jiuguang Wang", "city": "Boston", "country": "USA"}, {"affiliation": "Yun Chang", "city": "Boston", "country": "USA"}, {"affiliation": "Leonor Fermoselle", "city": "Paris", "country": "France"}, {"affiliation": "Duy Ta", "city": "Boston", "country": "USA"}, {"affiliation": "Bernadette Bucher", "city": "Boston", "country": "USA"}]


authors: Ziyuan Yang, Yingyu Chen, Zhiwen Wang, Hongming Shan, Yang Chen, Yi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5154-5163
cities: ["Chengdu", "Nanchong", "Shanghai", "Nanjing", "Nanchang"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.html

title:
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

abstract:
Reducing radiation doses benefits patients, but the resultant low-dose computed tomography (LDCT) images often suffer from clinically unacceptable noise and artifacts. While deep learning (DL) has shown promise in LDCT reconstruction, it requires large-scale data collection from multiple clients, raising privacy concerns. Federated learning (FL) has been introduced to mitigate these privacy concerns; however, current methods are typically tailored to specific scanning protocols, which limits their generalizability and makes them less effective for unseen protocols. To address these issues, we propose SCAN-PhysFed, a novel SCanning- and ANatomy-level personalized Physics-Driven Federated learning paradigm for LDCT reconstruction. Since the noise distribution in LDCT data is closely tied to scanning protocols and anatomical structures, we propose a dual-level physics-informed way to address these challenges. Specifically, we incorporate physical and anatomical prompts into our physics-informed hypernetworks to capture scanning- and anatomy-specific information, enabling dual-level physics-driven personalization of imaging features. These prompts are derived from the scanning protocol and the radiology report generated by a medical large language model (MLLM). Subsequently, client-specific decoders project these dual-level personalized imaging features back into the image domain. Besides, to tackle the challenge of unseen data, we introduce a novel protocol vector-quantization strategy (PVQS), which ensures consistent performance across new clients by quantifying unseen scanning codes to the closest match in the scanning codebook. Extensive experimental results demonstrate the superior performance of SCAN-PhysFed on public datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Patient-Level_Anatomy_Meets_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Ziyuan and Chen, Yingyu and Wang, Zhiwen and Shan, Hongming and Chen, Yang and Zhang, Yi},
    title     = {Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5154-5163}
}

raw_affiliation:
[{"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Southwest Petroleum University", "city": "Nanchong", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Tianfu Jiangxi Laboratory", "city": "Nanchang", "country": "China"}]

affiliations:
["Sichuan University", "Southwest Petroleum University", "Fudan University", "Southeast University", "Tianfu Jiangxi Laboratory"]


title: Exploiting Deblurring Networks for Radiance Fields
authors: Haeyun Choi, Heemin Yang, Janghyeok Han, Sunghyun Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6012-6021
affiliations: ["KT", "POSTECH"]
cities: ["Seoul", "Pohang"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_Exploiting_Deblurring_Networks_for_Radiance_Fields_CVPR_2025_paper.html

abstract:
In this paper, we propose DeepDeblurRF, a novel radiance field deblurring approach that can synthesize high-quality novel views from blurred training views with significantly reduced training time. DeepDeblurRF leverages deep neural network (DNN)-based deblurring modules to enjoy their deblurring performance and computational efficiency. To effectively combine DNN-based deblurring and radiance field construction, we propose a novel radiance field (RF)-guided deblurring and an iterative framework that performs RF-guided deblurring and radiance field construction in an alternating manner. Moreover, DeepDeblurRF is compatible with various scene representations, such as voxel grids and 3D Gaussians, expanding its applicability. We also present BlurRF-Synth, the first large-scale synthetic dataset for training radiance field deblurring frameworks. We conduct extensive experiments on both camera motion blur and defocus blur, demonstrating that DeepDeblurRF achieves state-of-the-art novel-view synthesis quality with significantly reduced training time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_Exploiting_Deblurring_Networks_for_Radiance_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_Exploiting_Deblurring_Networks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Haeyun and Yang, Heemin and Han, Janghyeok and Cho, Sunghyun},
    title     = {Exploiting Deblurring Networks for Radiance Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6012-6021}
}

raw_affiliation:
[{"affiliation": "KT", "city": "Seoul", "country": "South Korea"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]


title: Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
authors: Yifan Chang, Junjie Huang, Xiaofeng Wang, Yun Ye, Zhujin Liang, Yi Shan, Dalong Du, Xingang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6802-6811
cities: ["Beijing", "Unknown", "Beijing", "Luoyang"]
countries: ["China", "Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_Rethinking_Lanes_and_Points_in_Complex_Scenarios_for_Monocular_3D_CVPR_2025_paper.html

abstract:
Monocular 3D lane detection is a fundamental task in autonomous driving. Although sparse-point methods lower computational load and maintain high accuracy in complex lane geometries, current methods fail to fully leverage the geometric structure of lanes in both lane geometry representations and model design. In lane geometry representations, we present a theoretical analysis alongside experimental validation to verify that current sparse lane representation methods contain inherent flaws, resulting in potential errors of up to 20 m, which raise significant safety concerns for driving. To address this issue, we propose a novel patching strategy to completely represent the full lane structure. To enable existing models to match this strategy, we introduce the EndPoint head (EP-head), which adds a patching distance to endpoints. The EP-head enables the model to predict more complete lane representations even with fewer preset points, effectively addressing existing limitations and paving the way for models that are faster and require fewer parameters in the future. In model design, to enhance the model's perception of lane structures, we propose the PointLane attention (PL-attention), which incorporates prior geometric knowledge into the attention mechanism. Extensive experiments demonstrate the effectiveness of the proposed methods on various state-of-the-art models. For instance, in terms of the overall F1-score, our methods improve Persformer by 4.4 points, Anchor3DLane by 3.2 points, and LATR by 2.8 points. The code will be available soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_Rethinking_Lanes_and_Points_in_Complex_Scenarios_for_Monocular_3D_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Yifan and Huang, Junjie and Wang, Xiaofeng and Ye, Yun and Liang, Zhujin and Shan, Yi and Du, Dalong and Wang, Xingang},
    title     = {Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6802-6811}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "PhiGent Robotics", "city": "Unknown", "country": "Unknown"}, {"affiliation": "UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "Luoyang Institute for Robot and Intelligent Equipment", "city": "Luoyang", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "PhiGent Robotics", "UCAS", "Luoyang Institute for Robot and Intelligent Equipment"]


title: SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images
authors: Zixuan Huang, Mark Boss, Aaryaman Vasishta, James M. Rehg, Varun Jampani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16860-16870
affiliations: ["Stability AI", "UIUC"]
cities: ["unknown", "Champaign"]
countries: ["unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_SPAR3D_Stable_Point-Aware_Reconstruction_of_3D_Objects_from_Single_Images_CVPR_2025_paper.html

abstract:
We study the problem of single-image 3D object reconstruction. Recent works have diverged into two directions: regression-based modeling and generative modeling. Regression methods efficiently infer visible surfaces, but struggle with occluded regions. Generative methods handle uncertain regions better by modeling distributions, but are computationally expensive and the generation is often misaligned with visible surfaces. In this paper, we present SPAR3D, a novel two-stage approach aiming to take the best of both directions. The first stage of SPAR3D generates sparse 3D point clouds using a lightweight point diffusion model, which has a fast sampling speed. The second stage uses both the sampled point cloud and the input image to create highly detailed meshes. Our two-stage design enables probabilistic modeling of the ill-posed single-image 3D task while maintaining high computational efficiency and great output fidelity. Using point clouds as an intermediate representation further allows for interactive user edits. Evaluated on diverse datasets, SPAR3D demonstrates superior performance over previous state-of-the-art methods, at an inference speed of 0.7 seconds. Project page with code and model: https://spar3d.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_SPAR3D_Stable_Point-Aware_Reconstruction_of_3D_Objects_from_Single_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_SPAR3D_Stable_Point-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zixuan and Boss, Mark and Vasishta, Aaryaman and Rehg, James M. and Jampani, Varun},
    title     = {SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16860-16870}
}

raw_affiliation:
[{"affiliation": "Stability AI", "city": "unknown", "country": "unknown"}, {"affiliation": "UIUC", "city": "Champaign", "country": "USA"}]


authors: Yan Xie, Zequn Zeng, Hao Zhang, Yucheng Ding, Yi Wang, Zhengjue Wang, Bo Chen, Hongwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30199-30209
cities: ["Xi'an", "Xi'an"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Discovering_Fine-Grained_Visual-Concept_Relations_by_Disentangled_Optimal_Transport_Concept_Bottleneck_CVPR_2025_paper.html

title:
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models

abstract:
Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input image and the output prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts, less considering local image information, leading to two main drawbacks: i) they often produce spurious visual-concept relations, hence decreasing model reliability; and ii) though CBMs could explain the importance of every concept to the final prediction, it is still challenging to tell which visual region produces the prediction. To solve these problems, this paper proposes a Disentangled Optimal Transport CBM (DOT-CBM) framework to explore fine-grained visual-concept relations between local image patches and concepts. Specifically, we model the concept prediction process as a transportation problem between the patches and concepts, thereby achieving explicit fine-grained feature alignment. We also incorporate orthogonal projection losses within the modality to enhance local feature disentanglement. To further address the shortcut issues caused by statistical biases in the data, we utilize the visual saliency map and concept label statistics as transportation priors. Thus, DOT-CBM can visualize inversion heatmaps, provide more reliable concept predictions, and produce more accurate class predictions. Comprehensive experiments demonstrate that our proposed DOT-CBM achieves SOTA performance on several tasks, including image classification, local part detection and out-of-distribution generalization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Discovering_Fine-Grained_Visual-Concept_Relations_by_Disentangled_Optimal_Transport_Concept_Bottleneck_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Discovering_Fine-Grained_Visual-Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yan and Zeng, Zequn and Zhang, Hao and Ding, Yucheng and Wang, Yi and Wang, Zhengjue and Chen, Bo and Liu, Hongwei},
    title     = {Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30199-30209}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Radar Signal Processing, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "State Key Laboratory of Integrated Service Networks, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["National Key Laboratory of Radar Signal Processing, Xidian University", "State Key Laboratory of Integrated Service Networks, Xidian University"]


title: Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18486-18496
affiliations: ["Google Research", "Northwestern University", "UT Austin", "Google DeepMind", "Google"]
cities: ["Mountain View", "Evanston", "Austin", "London", "Mountain View"]
countries: ["USA", "USA", "USA", "UK", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_Focus-N-Fix_Region-Aware_Fine-Tuning_for_Text-to-Image_Generation_CVPR_2025_paper.html

authors:
Xiaoying Xing, Avinab Saha, Junfeng He, Susan Hao, Paul Vicol, Moonkyung Ryu, Gang Li, Sahil Singla, Sarah Young, Yinxiao Li, Feng Yang, Deepak Ramachandran

abstract:
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety. The prevailing approach to address these issues involves collecting human feedback on generated images, training reward models to estimate human feedback, and then fine-tuning T2I models based on the reward models to align them with human preferences. However, while existing reward fine-tuning methods can produce images with higher rewards, they may change model behavior in unexpected ways. For example, fine-tuning for one quality aspect (e.g., safety) may degrade other aspects (e.g., prompt alignment), or may lead to reward hacking (e.g., finding a way to increase rewards without having the intended effect). In this paper, we propose Focus-N-Fix, the first region-aware fine-tuning method that trains models to correct only previously problematic image regions. The resulting fine-tuned model generates images with the same high-level structure as the original model but shows significant improvements in regions where the original model was deficient in safety (over-sexualization and violence), plausibility, or other criteria. Our experiments demonstrate that Focus-N-Fix improves these localized quality aspects with little or no degradation to others and typically imperceptible changes in the rest of the image. Disclaimer: This paper contains images that may be overly sexual, violent, offensive or harmful.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_Focus-N-Fix_Region-Aware_Fine-Tuning_for_Text-to-Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_Focus-N-Fix_Region-Aware_Fine-Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Xiaoying and Saha, Avinab and He, Junfeng and Hao, Susan and Vicol, Paul and Ryu, Moonkyung and Li, Gang and Singla, Sahil and Young, Sarah and Li, Yinxiao and Yang, Feng and Ramachandran, Deepak},
    title     = {Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18486-18496}
}

raw_affiliation:
[{"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}, {"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "London", "country": "UK"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27586-27596
cities: ["Abu Dhabi", "Shenzhen", "Sydney", "Hefei"]
countries: ["UAE", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_RoomTour3D_Geometry-Aware_Video-Instruction_Tuning_for_Embodied_Navigation_CVPR_2025_paper.html

authors:
Mingfei Han, Liang Ma, Kamila Zhumakhanova, Ekaterina Radionova, Jingyi Zhang, Xiaojun Chang, Xiaodan Liang, Ivan Laptev

abstract:
Vision-and-Language Navigation (VLN) suffers from the limited diversity and scale of training data, primarily constrained by the manual curation of existing simulators.To address this, we introduce RoomTour3D, a video-instruction dataset derived from web-based room tour videos that capture real-world indoor spaces and human walking demonstrations. Unlike existing VLN datasets, RoomTour3D leverages the scale and diversity of online videos to generate open-ended human walking trajectories and open-world navigable instructions. To compensate for the lack of navigation data in online videos, we perform 3D reconstruction and obtain 3D trajectories of walking paths augmented with additional information on the room types, object locations and 3D shape of surrounding scenes. Our dataset includes ~100K open-ended description-enriched trajectories with ~200K instructions, and 17K action-enriched trajectories from 1847 room tour environments.We demonstrate experimentally that RoomTour3D enables significant improvements across multiple VLN tasks including CVDN, SOON, R2R, and REVERIE.Moreover, RoomTour3D facilitates the development of trainable zero-shot VLN agents, showcasing the potential and challenges of advancing towards open-world navigation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_RoomTour3D_Geometry-Aware_Video-Instruction_Tuning_for_Embodied_Navigation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_RoomTour3D_Geometry-Aware_Video-Instruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Mingfei and Ma, Liang and Zhumakhanova, Kamila and Radionova, Ekaterina and Zhang, Jingyi and Chang, Xiaojun and Liang, Xiaodan and Laptev, Ivan},
    title     = {RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27586-27596}
}

raw_affiliation:
[{"affiliation": "Department of Computer Vision, MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Shenzhen Campus of Sun Yat-Sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "ReLER Lab, AAII, UTS", "city": "Sydney", "country": "Australia"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["Department of Computer Vision, MBZUAI", "Shenzhen Campus of Sun Yat-Sen University", "ReLER Lab, AAII, UTS", "University of Science and Technology of China"]


title: PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds
authors: Barza Nisar, Steven L. Waslander
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6670-6679
affiliations: ["University of Toronto Robotics Institute"]
cities: ["Toronto"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nisar_PSA-SSL_Pose_and_Size-aware_Self-Supervised_Learning_on_LiDAR_Point_Clouds_CVPR_2025_paper.html

abstract:
Self-supervised learning (SSL) on 3D point clouds has the potential to learn feature representations that can transfer to diverse sensors and multiple downstream perception tasks. However, recent SSL approaches fail to define pretext tasks that retain geometric information such as object pose and scale,  which can be detrimental to the performance of downstream localization and geometry-sensitive 3D scene understanding tasks, such as 3D semantic segmentation and 3D object detection. We propose PSA-SSL, a novel extension to point cloud SSL that learns object pose and size-aware (PSA) features. Our approach defines a self-supervised bounding box regression pretext task, which retains object pose and size information. Furthermore, we incorporate LiDAR beam pattern augmentation on input point clouds, which encourages learning sensor-agnostic features. Our experiments demonstrate that with a single pretrained model, our light-weight yet effective extensions achieve significant improvements on 3D semantic segmentation with limited labels across popular autonomous driving datasets (Waymo, nuScenes, SemanticKITTI). Moreover, our approach outperforms other state-of-the-art SSL methods on 3D semantic segmentation (using up to 10 times less labels), as well as on 3D object detection.  Our code will be released on https://github.com/TRAILab/PSA-SSL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nisar_PSA-SSL_Pose_and_Size-aware_Self-Supervised_Learning_on_LiDAR_Point_Clouds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nisar_PSA-SSL_Pose_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nisar_2025_CVPR,
    author    = {Nisar, Barza and Waslander, Steven L.},
    title     = {PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6670-6679}
}

raw_affiliation:
[{"affiliation": "University of Toronto Robotics Institute", "city": "Toronto", "country": "Canada"}]


title: Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis
authors: Hanbin Ko, Chang-Min Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25897-25906
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ko_Bringing_CLIP_to_the_Clinic_Dynamic_Soft_Labels_and_Negation-Aware_CVPR_2025_paper.html

abstract:
The development of large-scale image-text pair datasets has significantly advanced self-supervised learning in Vision-Language Processing (VLP). However, directly applying general-domain architectures such as CLIP to medical data presents challenges, particularly in handling negations and addressing the inherent data imbalance of medical datasets. To address these issues, we propose a novel approach that integrates clinically-enhanced dynamic soft labels and medical graphical alignment, thereby improving clinical comprehension and improving the applicability of contrastive loss in medical contexts. Furthermore, we introduce negation-based hard negatives to deepen the model's understanding of the complexities of clinical language. Our approach is easily integrated into medical CLIP training pipeline and achieves state-of-the-art performance across multiple tasks, including zero-shot, fine-tuned classification and report retrieval. To comprehensively evaluate our model's capacity in understanding clinical language, we introduce CXR-Align, a benchmark uniquely designed to evaluate the understanding of negation and clinical information within chest X-ray (CXR) datasets. Experimental results demonstrate that our proposed methods are straightforward to implement and generalize effectively across contrastive learning frameworks, enhancing medical VLP capabilities and advancing clinical language understanding in medical imaging.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ko_Bringing_CLIP_to_the_Clinic_Dynamic_Soft_Labels_and_Negation-Aware_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ko_Bringing_CLIP_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ko_2025_CVPR,
    author    = {Ko, Hanbin and Park, Chang-Min},
    title     = {Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25897-25906}
}

raw_affiliation:
[{"affiliation": "Interdisciplinary Program in Bioengineering, Seoul National University Graduate School", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Integrated Major in Innovative Medical Science, Seoul National University Graduate School", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Department of Radiology, Seoul National University Hospital", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Interdisciplinary Program in Bioengineering, Seoul National University Graduate School", "Integrated Major in Innovative Medical Science, Seoul National University Graduate School", "Department of Radiology, Seoul National University Hospital"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7997-8008
affiliations: ["Snap Inc.", "HKUST", "The University of Melbourne", "MBZUAI"]
cities: ["Santa Monica", "Hong Kong", "Melbourne", "Abu Dhabi"]
countries: ["USA", "China", "Australia", "UAE"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SnapGen_Taming_High-Resolution_Text-to-Image_Models_for_Mobile_Devices_with_Efficient_CVPR_2025_paper.html

title:
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training

authors:
Jierun Chen, Dongting Hu, Xijie Huang, Huseyin Coskun, Arpit Sahni, Aarush Gupta, Anujraaj Goyal, Dishani Lahiri, Rajesh Singh, Yerlan Idelbayev, Junli Cao, Yanyu Li, Kwang-Ting Cheng, S.-H. Gary Chan, Mingming Gong, Sergey Tulyakov, Anil Kag, Yanwu Xu, Jian Ren

abstract:
Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by developing an extremely small and fast T2I model that generates high-resolution and high-quality images on mobile platforms. We propose several techniques to achieve this goal. First, we systematically examine the design choices of the network architecture to reduce model parameters and latency, while ensuring high-quality generation. Second, to further improve generation quality, we employ cross-architecture knowledge distillation from a much larger model, using a multi-level approach to guide the training of our model from scratch. Third, we enable a few-step generation by integrating adversarial guidance with knowledge distillation. Our model, for the first time, demonstrates the generation of 1024x1024 px images on a mobile device in 1.2 to 2.3 seconds. On ImageNet-1K, our model, with only 372M parameters, achieves an FID of 2.06 for 256x256 px generation. On T2I benchmarks (i.e., GenEval and DPG-Bench), our model with merely 379M parameters surpasses large-scale models with billions of parameters at a significantly smaller size (e.g., 7x smaller than SDXL, 14x smaller than IF-XL).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SnapGen_Taming_High-Resolution_Text-to-Image_Models_for_Mobile_Devices_with_Efficient_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jierun and Hu, Dongting and Huang, Xijie and Coskun, Huseyin and Sahni, Arpit and Gupta, Aarush and Goyal, Anujraaj and Lahiri, Dishani and Singh, Rajesh and Idelbayev, Yerlan and Cao, Junli and Li, Yanyu and Cheng, Kwang-Ting and Chan, S.-H. Gary and Gong, Mingming and Tulyakov, Sergey and Kag, Anil and Xu, Yanwu and Ren, Jian},
    title     = {SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7997-8008}
}

raw_affiliation:
[{"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "USA"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}]


title: Label Shift Meets Online Learning: Ensuring Consistent Adaptation with Universal Dynamic Regret
authors: Yucong Dai, Shilin Gu, Ruidong Fan, Chao Xu, Chenping Hou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15392-15401
affiliations: ["College of Science, National University of Defense Technology"]
cities: ["Changsha"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Label_Shift_Meets_Online_Learning_Ensuring_Consistent_Adaptation_with_Universal_CVPR_2025_paper.html

abstract:
Label shift, which investigates the adaptation of label distributions between the fixed source and target domains, has attracted significant research interests and broad applications in offline settings. In real-world scenarios, however, data often arrives as a continuous stream. Addressing label shift in online learning settings is paramount. Existing strategies, which tailor traditional offline label shift techniques to online settings, have degraded performance due to the inconsistent estimation of label distributions and violation of convex assumption for theoretical guarantee. In this paper, we propose a novel method to ensure consistent adaptation to online label shift. We construct a new convex risk estimator that is pivotal for both online optimization and theoretical analysis. Furthermore, we enhance an optimistic online algorithm as the base learner and refine the classifier using an ensemble method. Theoretically, we derive a universal dynamic regret which achieves minimax optimal. Extensive experiments on both real-world datasets and human motion task demonstrate the superiority of our method comparing existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Label_Shift_Meets_Online_Learning_Ensuring_Consistent_Adaptation_with_Universal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_Label_Shift_Meets_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Yucong and Gu, Shilin and Fan, Ruidong and Xu, Chao and Hou, Chenping},
    title     = {Label Shift Meets Online Learning: Ensuring Consistent Adaptation with Universal Dynamic Regret},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15392-15401}
}

raw_affiliation:
[{"affiliation": "College of Science, National University of Defense Technology", "city": "Changsha", "country": "China"}]


title: A Physics-Informed Blur Learning Framework for Imaging Systems
authors: Liqun Chen, Yuxuan Li, Jun Dai, Jinwei Gu, Tianfan Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10913-10922
affiliations: ["Shanghai AI Laboratory", "NVIDIA", "The Chinese University of Hong Kong"]
cities: ["Shanghai", "Santa Clara", "Hong Kong"]
countries: ["China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_A_Physics-Informed_Blur_Learning_Framework_for_Imaging_Systems_CVPR_2025_paper.html

abstract:
Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for imaging systems, consisting of a simple calibration followed by a learning process. Our framework could achieve both high accuracy and universal applicability. Inspired by the Seidel PSF model for representing spatially varying PSF, we identify its limitations in optimization and introduce a novel wavefront-based PSF model accompanied by an optimization strategy, both reducing optimization complexity and improving estimation accuracy. Moreover, our wavefront-based PSF model is independent of lens parameters, eliminate the need for prior knowledge of the lens. To validate our approach, we compare it with recent PSF estimation methods (Degradation Transfer and Fast Two-step) through a deblurring task, where all the estimated PSFs are used to train state-of-the-art deblurring algorithms. Our approach demonstrates improvements in image quality in simulation and also showcases noticeable visual quality improvements on real captured images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_A_Physics-Informed_Blur_Learning_Framework_for_Imaging_Systems_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_A_Physics-Informed_Blur_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Liqun and Li, Yuxuan and Dai, Jun and Gu, Jinwei and Xue, Tianfan},
    title     = {A Physics-Informed Blur Learning Framework for Imaging Systems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10913-10922}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]


authors: Zheng Zhang, Guanchun Yin, Bo Zhang, Wu Liu, Xiuzhuang Zhou, Wendong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25940-25949
affiliations: ["null", "null", "null", "null", "null", "null"]
cities: ["null", "null", "null", "null", "null", "null"]
countries: ["null", "null", "null", "null", "null", "null"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_A_Semantic_Knowledge_Complementarity_based_Decoupling_Framework_for_Semi-supervised_Class-imbalanced_CVPR_2025_paper.html

title:
A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation

abstract:
The limited data annotations have made semi-supervised learning (SSL) increasingly popular in medical image analysis. However, the use of pseudo labels in SSL degrades the performance of decoders that heavily rely on high-accuracy annotations. This issue is particularly pronounced in class-imbalanced multi-organ segmentation tasks, where small organs may be under-segmented or even ignored. In this paper, we propose SKCDF, a semantic knowledge complementarity based decoupling framework for multi-organ segmentation in class-imbalanced medical images. SKCDF decouples the data flow based on the responsibilities of the encoder and decoder during model training to make the model effectively learn semantic features, while mitigating the negative impact of unlabeled data on the semantic segmentation task. We also design a semantic knowledge complementarity module that adopts labeled data to guide the generation of pseudo labels and enriches the semantic features of labeled data with unlabeled data, which improves the quality of generated pseudo labels and the robustness of the overall model. Furthermore, we design an auxiliary balanced segmentation head based training strategy to further enhance the segmentation performance of small organs. Experimental results on the Synapse and AMOS datasets show that our method significantly outperforms existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_A_Semantic_Knowledge_Complementarity_based_Decoupling_Framework_for_Semi-supervised_Class-imbalanced_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_A_Semantic_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zheng and Yin, Guanchun and Zhang, Bo and Liu, Wu and Zhou, Xiuzhuang and Wang, Wendong},
    title     = {A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25940-25949}
}

raw_affiliation:
[{"affiliation": "null", "city": "null", "country": "null"}, {"affiliation": "null", "city": "null", "country": "null"}, {"affiliation": "null", "city": "null", "country": "null"}, {"affiliation": "null", "city": "null", "country": "null"}, {"affiliation": "null", "city": "null", "country": "null"}, {"affiliation": "null", "city": "null", "country": "null"}]


title: Community Forensics: Using Thousands of Generators to Train Fake Image Detectors
authors: Jeongsoo Park, Andrew Owens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8245-8257
raw_affiliation: [{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}]
affiliations: ["University of Michigan"]
cities: ["Ann Arbor"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Community_Forensics_Using_Thousands_of_Generators_to_Train_Fake_Image_CVPR_2025_paper.html

abstract:
One of the key challenges of detecting AI-generated images is spotting images that have been created by previously unseen generative models. We argue that the limited diversity of the training data is a major obstacle to addressing this problem, and we propose a new dataset that is significantly larger and more diverse than prior works. As part of creating this dataset, we systematically download thousands of text-to-image latent diffusion models and sample images from them. We also collect images from dozens of popular open source and commercial models. The resulting dataset contains 2.7M images that have been sampled from 4803 different models. These images collectively capture a wide range of scene content, generator architectures, and image processing settings. Using this dataset, we study the generalization abilities of fake image detectors. Our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. We also find that increasing the diversity of the models improves detection performance, and that our trained detectors generalize better than those trained on other datasets. The dataset can be found in https://jespark.net/projects/2024/community_forensics

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Community_Forensics_Using_Thousands_of_Generators_to_Train_Fake_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Community_Forensics_Using_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jeongsoo and Owens, Andrew},
    title     = {Community Forensics: Using Thousands of Generators to Train Fake Image Detectors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8245-8257}
}


title: ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling
authors: Zikang Zhou, Hengjian Zhou, Haibo Hu, Zihao Wen, Jianping Wang, Yung-Hui Li, Yu-Kai Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1612-1621
cities: ["Hong Kong", "Hangzhou", "Taipei", "Pittsburgh"]
countries: ["China", "China", "Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_ModeSeq_Taming_Sparse_Multimodal_Motion_Prediction_with_Sequential_Mode_Modeling_CVPR_2025_paper.html

abstract:
Anticipating the multimodality of future events lays the foundation for safe autonomous driving. However, multimodal motion prediction for traffic agents has been clouded by the lack of multimodal ground truth. Existing works predominantly adopt the winner-take-all training strategy to tackle this challenge, yet still suffer from limited trajectory diversity and uncalibrated mode confidence. While some approaches address these limitations by generating excessive trajectory candidates, they necessitate a post-processing stage to identify the most representative modes, a process lacking universal principles and compromising trajectory accuracy. We are thus motivated to introduce ModeSeq, a new multimodal prediction paradigm that models modes as sequences. Unlike the common practice of decoding multiple plausible trajectories in one shot, ModeSeq requires motion decoders to infer the next mode step by step, thereby more explicitly capturing the correlation between modes and significantly enhancing the ability to reason about multimodality. Leveraging the inductive bias of sequential mode prediction, we also propose the Early-Match-Take-All (EMTA) training strategy to diversify the trajectories further. Without relying on dense mode prediction or heuristic post-processing, ModeSeq considerably improves the diversity of multimodal output while attaining satisfactory trajectory accuracy, resulting in balanced performance on motion prediction benchmarks. Moreover, ModeSeq naturally emerges with the capability of mode extrapolation, which supports forecasting more behavior modes when the future is highly uncertain.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_ModeSeq_Taming_Sparse_Multimodal_Motion_Prediction_with_Sequential_Mode_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_ModeSeq_Taming_Sparse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Zikang and Zhou, Hengjian and Hu, Haibo and Wen, Zihao and Wang, Jianping and Li, Yung-Hui and Huang, Yu-Kai},
    title     = {ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1612-1621}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hon Hai Research Institute", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]

affiliations:
["City University of Hong Kong", "Zhejiang University", "Hon Hai Research Institute", "Carnegie Mellon University"]


title: Quaffure: Real-Time Quasi-Static Neural Hair Simulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 239-249
raw_affiliation: [{"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}]
affiliations: ["Meta Reality Labs"]
cities: ["Menlo Park"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stuyck_Quaffure_Real-Time_Quasi-Static_Neural_Hair_Simulation_CVPR_2025_paper.html

authors:
Tuur Stuyck, Gene Wei-Chin Lin, Egor Larionov, Hsiao-yu Chen, Aljaz Bozic, Nikolaos Sarafianos, Doug Roble

abstract:
Realistic hair motion is crucial for high-quality avatars, but it is often limited by the computational resources available for real-time applications. To address this challenge, we propose a novel neural approach to predict physically plausible hair deformations that generalizes to various body poses, shapes, and hair styles. Our model is trained using a self-supervised loss, eliminating the need for expensive data generation and storage. We demonstrate our method's effectiveness through numerous results across a wide range of pose and shape variations, showcasing its robust generalization capabilities and temporally smooth results. Our approach is highly suitable for real-time applications with an inference time of only a few milliseconds on consumer hardware and its ability to scale to predicting 1000 grooms in 0.3 seconds. Code will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stuyck_Quaffure_Real-Time_Quasi-Static_Neural_Hair_Simulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stuyck_Quaffure_Real-Time_Quasi-Static_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Stuyck_2025_CVPR,
    author    = {Stuyck, Tuur and Lin, Gene Wei-Chin and Larionov, Egor and Chen, Hsiao-yu and Bozic, Aljaz and Sarafianos, Nikolaos and Roble, Doug},
    title     = {Quaffure: Real-Time Quasi-Static Neural Hair Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {239-249}
}


title: Towards Practical Real-Time Neural Video Compression
authors: Zhaoyang Jia, Bin Li, Jiahao Li, Wenxuan Xie, Linfeng Qi, Houqiang Li, Yan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12543-12552
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/Jia_Towards_Practical_Real-Time_Neural_Video_Compression_CVPR_2025_paper.html

abstract:
We introduce a practical real-time neural video codec (NVC) designed to deliver high compression ratio, low latency and broad versatility. In practice, the coding speed of NVCs depends on 1) computational costs, and 2) non-computational operational costs, such as memory I/O and the number of function calls. While most efficient NVCs prioritize reducing computational cost, we identify operational cost as the primary bottleneck to achieving higher coding speed. Leveraging this insight, we introduce a set of efficiency-driven design improvements focused on minimizing operational costs. Specifically, we employ implicit temporal modeling to eliminate complex explicit motion modules, and use single low-resolution latent representations rather than progressive downsampling. These innovations significantly accelerate NVC without sacrificing compression quality. Additionally, we implement model integerization for consistent cross-device coding and a module-bank-based rate control scheme to improve practical adaptability. Experiments show our proposed DCVC-RT achieves an impressive average encoding/decoding speed at 125.2/112.8 fps (frames per second) for 1080p video, while saving an average of 21% in bitrate compared to H.266/VTM. The code is available at https://github.com/microsoft/DCVC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_Towards_Practical_Real-Time_Neural_Video_Compression_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Zhaoyang and Li, Bin and Li, Jiahao and Xie, Wenxuan and Qi, Linfeng and Li, Houqiang and Lu, Yan},
    title     = {Towards Practical Real-Time Neural Video Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12543-12552}
}


title: DepthSplat: Connecting Gaussian Splatting and Depth
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16453-16463
cities: ["Zurich", "T\u00fcbingen", "Redmond", "Bonn"]
countries: ["Switzerland", "Germany", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_DepthSplat_Connecting_Gaussian_Splatting_and_Depth_CVPR_2025_paper.html

authors:
Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, Marc Pollefeys

abstract:
Gaussian splatting and single-view depth estimation are typically studied in isolation. In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and study their interactions. More specifically, we first contribute a robust multi-view depth model by leveraging pre-trained monocular depth features, leading to high-quality feed-forward 3D Gaussian splatting reconstructions. We also show that Gaussian splatting can serve as an unsupervised pre-training objective for learning powerful depth models from large-scale multi-view posed datasets. We validate the synergy between Gaussian splatting and depth estimation through extensive ablation and cross-task transfer experiments. Our DepthSplat achieves state-of-the-art performance on ScanNet, RealEstate10K and DL3DV datasets in terms of both depth estimation and novel view synthesis, demonstrating the mutual benefits of connecting both tasks. In addition, DepthSplat enables feed-forward reconstruction from 12 input views (512x960 resolutions) in 0.6 seconds.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_DepthSplat_Connecting_Gaussian_Splatting_and_Depth_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_DepthSplat_Connecting_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Haofei and Peng, Songyou and Wang, Fangjinhua and Blum, Hermann and Barath, Daniel and Geiger, Andreas and Pollefeys, Marc},
    title     = {DepthSplat: Connecting Gaussian Splatting and Depth},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16453-16463}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of T\u00a8ubingen, T\u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}]

affiliations:
["ETH Zurich", "University of T\u00a8ubingen, T\u00a8ubingen AI Center", "Microsoft", "University of Bonn"]


title: LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting
authors: Xiaoyan Xing, Konrad Groh, Sezer Karaoglu, Theo Gevers, Anand Bhattad
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 442-452
affiliations: ["University of Amsterdam", "BCAI-Bosch", "Toyota Technological Institute at Chicago"]
cities: ["Amsterdam", "Unknown", "Chicago"]
countries: ["Netherlands", "Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_LumiNet_Latent_Intrinsics_Meets_Diffusion_Models_for_Indoor_Scene_Relighting_CVPR_2025_paper.html

abstract:
We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for transferring lighting from one image to another. Given a source image and a target lighting image, LumiNet generates a relit version of the source scene that captures the target's lighting.  Our approach makes two key contributions: a data curation strategy from the StyleGAN-based relighting model for our training, and a modified diffusion-based ControlNet that processes both latent intrinsic properties from the source image and latent extrinsic properties from the target image. We further improve lighting transfer through a learned adaptor that injects the target's latent extrinsic properties via cross-attention and light-weight fine-tuning. Unlike traditional ControlNet, which generates images with conditional maps from a single scene, LumiNet processes latent representations from two different images - preserving geometry and albedo from the source while transferring lighting characteristics from the target. Experiments demonstrate that our method successfully transfers complex lighting phenomena including specular highlights and indirect illumination across scenes with varying spatial layouts and materials, outperforming existing approaches on challenging indoor scenes using only images as input.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_LumiNet_Latent_Intrinsics_Meets_Diffusion_Models_for_Indoor_Scene_Relighting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_LumiNet_Latent_Intrinsics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Xiaoyan and Groh, Konrad and Karaoglu, Sezer and Gevers, Theo and Bhattad, Anand},
    title     = {LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {442-452}
}

raw_affiliation:
[{"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "BCAI-Bosch", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Toyota Technological Institute at Chicago", "city": "Chicago", "country": "USA"}]


title: FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30440-30450
cities: ["Munich", "Munich", "Oxford", "Munich", "Munich"]
countries: ["Germany", "Germany", "United Kingdom", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.html

authors:
Haokun Chen, Hang Li, Yao Zhang, Jinhe Bi, Gengyuan Zhang, Yueqi Zhang, Philip Torr, Jindong Gu, Denis Krompass, Volker Tresp

abstract:
One-Shot Federated Learning (OSFL), a special decentralized machine learning paradigm, has recently gained significant attention. OSFL requires only a single round of client data or model upload, which reduces communication costs and mitigates privacy threats compared to traditional FL. Despite these promising prospects, existing methods face challenges due to client data heterogeneity and limited data quantity when applied to real-world OSFL systems. Recently, Latent Diffusion Models (LDM) have shown remarkable advancements in synthesizing high-quality images through pretraining on large-scale datasets, thereby presenting a potential solution to overcome these issues. However, directly applying pretrained LDM to heterogeneous OSFL results in significant distribution shifts in synthetic data, leading to performance degradation in classification models trained on such data. This issue is particularly pronounced in rare domains, such as medical imaging, which are underrepresented in LDM's pretraining data. To address this challenge, we propose Federated Bi-Level Personalization (FedBiP), which personalizes the pretrained LDM at both instance-level and concept-level. Hereby, FedBiP synthesizes images following the client's local data distribution without compromising the privacy regulations. FedBiP is also the first approach to simultaneously address feature space heterogeneity and client data scarcity in OSFL. Our method is validated through extensive experiments on three OSFL benchmarks with feature space heterogeneity, as well as on challenging medical and satellite image datasets with label heterogeneity. The results demonstrate the effectiveness of FedBiP, which substantially outperforms other OSFL methods. Our code is available at \href https://github.com/HaokunChen245/FedBiP  https://github.com/HaokunChen245/FedBiP .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_FedBiP_Heterogeneous_One-Shot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Haokun and Li, Hang and Zhang, Yao and Bi, Jinhe and Zhang, Gengyuan and Zhang, Yueqi and Torr, Philip and Gu, Jindong and Krompass, Denis and Tresp, Volker},
    title     = {FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30440-30450}
}

raw_affiliation:
[{"affiliation": "Ludwig Maximilian University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Siemens Technology", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning (MCML)", "city": "Munich", "country": "Germany"}]

affiliations:
["Ludwig Maximilian University of Munich", "Siemens Technology", "University of Oxford", "Technical University of Munich", "Munich Center for Machine Learning (MCML)"]


title: DiC: Rethinking Conv3x3 Designs in Diffusion Models
authors: Yuchuan Tian, Jing Han, Chengcheng Wang, Yuchen Liang, Chao Xu, Hanting Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2469-2478
cities: ["Beijing", "Beijing", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_DiC_Rethinking_Conv3x3_Designs_in_Diffusion_Models_CVPR_2025_paper.html

abstract:
Diffusion models have shown exceptional performance in visual generation tasks. Recently, these models have shifted from traditional U-Shaped CNN-Attention hybrid structures to fully transformer-based isotropic architectures. While these transformers exhibit strong scalability and performance, their reliance on complicated self-attention operation results in slow inference speeds. Contrary to these works, we rethink one of the simplest yet fastest module in deep learning, 3x3 Convolution, to construct a scaled-up purely convolutional diffusion model. We first discover that an Encoder-Decoder Hourglass design outperforms scalable isotropic architectures for Conv3x3, but still under-performing our expectation. Further improving the architecture, we introduce sparse skip connections to reduce redundancy and improve scalability. Based on the architecture, we introduce conditioning improvements including stage-specific embeddings, mid-block condition injection, and conditional gating. These improvements lead to our proposed Diffusion CNN (DiC), which serves as a swift yet competitive diffusion architecture baseline. Experiments on various scales and settings show that DiC surpasses existing diffusion transformers by considerable margins in terms of performance while keeping a good speed advantage. Project page: https://github.com/YuchuanTian/DiC

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_DiC_Rethinking_Conv3x3_Designs_in_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_DiC_Rethinking_Conv3x3_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Yuchuan and Han, Jing and Wang, Chengcheng and Liang, Yuchen and Xu, Chao and Chen, Hanting},
    title     = {DiC: Rethinking Conv3x3 Designs in Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2469-2478}
}

raw_affiliation:
[{"affiliation": "State Key Lab of General AI, School of Intelligence Science and Technology, Peking University.", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beijing University of Posts and Telecommunications.", "city": "Beijing", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab.", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Mathematical Sciences, Peking University.", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Lab of General AI, School of Intelligence Science and Technology, Peking University.", "School of Artificial Intelligence, Beijing University of Posts and Telecommunications.", "Huawei Noah\u2019s Ark Lab.", "School of Mathematical Sciences, Peking University."]


title: Dynamic Camera Poses and Where to Find Them
authors: Chris Rockwell, Joseph Tung, Tsung-Yi Lin, Ming-Yu Liu, David F. Fouhey, Chen-Hsuan Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12444-12455
affiliations: ["NVIDIA", "University of Michigan", "New York University"]
cities: ["Santa Clara", "Ann Arbor", "New York"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rockwell_Dynamic_Camera_Poses_and_Where_to_Find_Them_CVPR_2025_paper.html

abstract:
Annotating camera poses on dynamic Internet videos at scale is critical for advancing fields like realistic video generation and simulation. However, collecting such a dataset is difficult, as most Internet videos are unsuitable for pose estimation. Furthermore, annotating dynamic Internet videos present significant challenges even for state-of-the-art methods. In this paper, we introduce DynPose-100K, a large-scale dataset of dynamic Internet videos annotated with camera poses. Our collection pipeline addresses filtering using a carefully combined set of task-specific and generalist models. For pose estimation, we combine the latest techniques of point tracking, dynamic masking, and structure-from-motion to achieve improvements over the state-of-the-art approaches. Our analysis and experiments demonstrate that DynPose-100K is both large-scale and diverse across several key attributes, opening up avenues for advancements in various downstream applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rockwell_Dynamic_Camera_Poses_and_Where_to_Find_Them_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rockwell_Dynamic_Camera_Poses_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Rockwell_2025_CVPR,
    author    = {Rockwell, Chris and Tung, Joseph and Lin, Tsung-Yi and Liu, Ming-Yu and Fouhey, David F. and Lin, Chen-Hsuan},
    title     = {Dynamic Camera Poses and Where to Find Them},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12444-12455}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
authors: Jiahui Lei, Yijia Weng, Adam W. Harley, Leonidas Guibas, Kostas Daniilidis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6165-6177
affiliations: ["University of Pennsylvania", "Stanford University", "Archimedes, Athena RC"]
cities: ["Philadelphia", "Stanford", "Athens"]
countries: ["USA", "USA", "Greece"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_MoSca_Dynamic_Gaussian_Fusion_from_Casual_Videos_via_4D_Motion_CVPR_2025_paper.html

abstract:
We introduce 4D Motion Scaffolds (MoSca), a modern 4D reconstruction system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casually in the wild. To address such a challenging and ill-posed inverse problem, we leverage prior knowledge from foundational vision models and lift the video data to a novel Motion Scaffold (MoSca) representation, which compactly and smoothly encodes the underlying motions/deformations. The scene geometry and appearance are then disentangled from the deformation field and are encoded by globally fusing the Gaussians anchored onto the MoSca and optimized via Gaussian Splatting. Additionally, camera focal length and poses can be solved using bundle adjustment without the need of any other pose estimation tools. Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks and its effectiveness on real videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_MoSca_Dynamic_Gaussian_Fusion_from_Casual_Videos_via_4D_Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_MoSca_Dynamic_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Jiahui and Weng, Yijia and Harley, Adam W. and Guibas, Leonidas and Daniilidis, Kostas},
    title     = {MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6165-6177}
}

raw_affiliation:
[{"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Archimedes, Athena RC", "city": "Athens", "country": "Greece"}]


title: GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
authors: Weihang Li, Hongli XU, Junwen Huang, Hyunjun Jung, Peter KT Yu, Nassir Navab, Benjamin Busam
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27154-27165
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning", "XYZ Robotics"]
cities: ["Munich", "Munich", "Unknown"]
countries: ["Germany", "Germany", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GCE-Pose_Global_Context_Enhancement_for_Category-level_Object_Pose_Estimation_CVPR_2025_paper.html

abstract:
A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Recent approaches leverage foundational features to capture semantic and geometry cues from data. However, these approaches fail under partial visibility. We overcome this with a first-complete-then-aggregate strategy for feature extraction utilizing class priors. In this paper, we present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior. GCE-Pose first performs semantic shape reconstruction with a proposed Semantic Shape Reconstruction (SSR) module. Given an unseen partial RGB-D object instance, our SSR module reconstructs the instance's global geometry and semantics by deforming category-specific 3D semantic prototypes through a learned deep Linear Shape Model. We then introduce a Global Context Enhanced (GCE) feature fusion module that effectively fuses features from partial RGB-D observations and the reconstructed global context. Extensive experiments validate the impact of our global context prior and the effectiveness of the GCE fusion module, demonstrating that GCE-Pose significantly outperforms existing methods on challenging real-world datasets HouseCat6D and NOCS-REAL275.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GCE-Pose_Global_Context_Enhancement_for_Category-level_Object_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GCE-Pose_Global_Context_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Weihang and XU, Hongli and Huang, Junwen and Jung, Hyunjun and Yu, Peter KT and Navab, Nassir and Busam, Benjamin},
    title     = {GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27154-27165}
}

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


title: OmniGen: Unified Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13294-13304
affiliations: ["Beijing Academy of Artificial Intelligence"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_OmniGen_Unified_Image_Generation_CVPR_2025_paper.html

authors:
Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, Zheng Liu

abstract:
The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports various downstream tasks, such as image editing, subject-driven generation, and visual-conditional generation. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional plugins. Moreover, compared to existing diffusion models, it is more user-friendly and can complete complex tasks end-to-end through instructions without the need for extra intermediate steps, greatly simplifying the image generation workflow. 3) Knowledge Transfer: Benefit from learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model's reasoning capabilities and potential applications of the chain-of-thought mechanism. This work represents the first attempt at a general-purpose image generation model, and we will release our resources at https://github.com/VectorSpaceLab/OmniGen to foster future advancements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_OmniGen_Unified_Image_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Shitao and Wang, Yueze and Zhou, Junjie and Yuan, Huaying and Xing, Xingrun and Yan, Ruiran and Li, Chaofan and Wang, Shuting and Huang, Tiejun and Liu, Zheng},
    title     = {OmniGen: Unified Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13294-13304}
}

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


authors: Natacha Kuete Meli, Vladislav Golyanik, Marcel Seelbach Benkner, Michael Moeller
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11395-11405
affiliations: ["University of Siegen", "MPI for Informatics, SIC"]
cities: ["Siegen", "Saarbr\u00fccken"]
countries: ["Germany", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meli_QuCOOP_A_Versatile_Framework_for_Solving_Composite_and_Binary-Parametrised_Problems_CVPR_2025_paper.html

title:
QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers

abstract:
There is growing interest in solving computer vision problems such as mesh or point set alignment using Adiabatic Quantum Computing (AQC). Unfortunately, modern experimental AQC devices such as D-Wave only support Quadratic Unconstrained Binary Optimisation (QUBO) problems, which severely limits their applicability. This paper proposes a new way to overcome this limitation and introduces QuCOOP, an optimisation framework extending the scope of AQC to composite and binary-parametrised, possibly non-quadratic problems. The key idea of QuCOOP is to iteratively approximate the original objective function by a sequel of local (intermediate) QUBO forms, whose binary parameters can be sampled on AQC devices. We experiment with quadratic assignment problems, shape matching and point set registration without knowing the correspondences in advance. Our approach achieves state-of-the-art results across multiple instances of tested problems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meli_QuCOOP_A_Versatile_Framework_for_Solving_Composite_and_Binary-Parametrised_Problems_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meli_QuCOOP_A_Versatile_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Meli_2025_CVPR,
    author    = {Meli, Natacha Kuete and Golyanik, Vladislav and Benkner, Marcel Seelbach and Moeller, Michael},
    title     = {QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11395-11405}
}

raw_affiliation:
[{"affiliation": "University of Siegen", "city": "Siegen", "country": "Germany"}, {"affiliation": "MPI for Informatics, SIC", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes
authors: Kaiwei Zhang, Dandan Zhu, Xiongkuo Min, Guangtao Zhai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16219-16228
affiliations: ["Shanghai Jiao Tong University", "East China Normal University"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Mesh_Mamba_A_Unified_State_Space_Model_for_Saliency_Prediction_CVPR_2025_paper.html

abstract:
Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention. To investigate the interaction between geometric structure and texture in shaping visual attention, we establish a comprehensive mesh saliency dataset, which is the first to systematically capture the differences in saliency distribution under both textured and non-textured visual conditions. Furthermore, we introduce mesh Mamba, a unified saliency prediction model based on a state space model (SSM), designed to adapt across various mesh types. Mesh Mamba effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework, ensuring coherence throughout appearance-enhanced modeling. More importantly, by subgraph embedding and a bidirectional SSM, the model enables global context modeling for both local geometry and texture, preserving the topological structure and improving the understanding of visual details and structural complexity. Through extensive theoretical and empirical validation, our model not only improves performance across various mesh types but also demonstrates high scalability and versatility, particularly through cross validations of various visual features.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Mesh_Mamba_A_Unified_State_Space_Model_for_Saliency_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Mesh_Mamba_A_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Kaiwei and Zhu, Dandan and Min, Xiongkuo and Zhai, Guangtao},
    title     = {Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16219-16228}
}

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


title: Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2997-3007
cities: ["Guangzhou", "Oxford", "Hong Kong", "Shenzhen", "Philadelphia"]
countries: ["China", "United Kingdom", "China", "China", "United States"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Not_Just_Text_Uncovering_Vision_Modality_Typographic_Threats_in_Image_CVPR_2025_paper.html

authors:
Hao Cheng, Erjia Xiao, Jiayan Yang, Jiahang Cao, Qiang Zhang, Jize Zhang, Kaidi Xu, Jindong Gu, Renjing Xu

abstract:
Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Image tasks, attackers can generate a series of images containing inappropriate content by simply editing the language modality input. To mitigate this security concern, numerous guarding or defensive strategies have been proposed, with a particular emphasis on safeguarding language modality. However, in practical applications, threats in the vision modality, particularly in tasks involving the editing of real-world images, present heightened security risks as they can easily infringe upon the rights of the image owner.  Therefore, this paper employs a method named typographic attack to reveal that various image generation models are also susceptible to threats within the vision modality. Furthermore, we also evaluate the defense performance of various existing methods when facing threats in the vision modality and uncover their ineffectiveness. Finally, we propose the Vision Modal Threats in Image Generation Models (VMT-IGMs) dataset, which would serve as a baseline for evaluating the vision modality vulnerability of various image generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Not_Just_Text_Uncovering_Vision_Modality_Typographic_Threats_in_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Not_Just_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Hao and Xiao, Erjia and Yang, Jiayan and Cao, Jiahang and Zhang, Qiang and Zhang, Jize and Xu, Kaidi and Gu, Jindong and Xu, Renjing},
    title     = {Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2997-3007}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Drexel University", "city": "Philadelphia", "country": "United States"}]

affiliations:
["The Hong Kong University of Science and Technology (Guangzhou)", "University of Oxford", "The Hong Kong University of Science and Technology", "The Chinese University of Hong Kong, Shenzhen", "Drexel University"]


title: SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
authors: Leigang Qu, Haochuan Li, Wenjie Wang, Xiang Liu, Juncheng Li, Liqiang Nie, Tat-Seng Chua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18497-18508
cities: ["Singapore", "Hefei", "Hangzhou", "Shenzhen"]
countries: ["Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_SILMM_Self-Improving_Large_Multimodal_Models_for_Compositional_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation.However, achieving accurate text-image alignment for LMMs, particularly in compositional scenarios, remains challenging. Existing approaches, such as layout planning for multi-step generation and learning from human feedback or AI feedback, depend heavily on prompt engineering, costly human annotations, and continual upgrading, limiting flexibility and scalability. In this work, we introduce a model-agnostic iterative self-improvement framework (**SILMM**) that can enable LMMs to provide helpful and scalable self-feedback and optimize text-image alignment via Direct Preference Optimization (DPO). DPO can readily applied to LMMs that use discrete visual tokens as intermediate image representations; while it is less suitable for LMMs with continuous visual features, as obtaining generation probabilities is challenging.To adapt SILMM to LMMs with continuous features, we propose a diversity mechanism to obtain diverse representations and a kernel-based continuous DPO for alignment. Extensive experiments on three compositional text-to-image generation benchmarks validate the effectiveness and superiority of SILMM, showing improvements exceeding 30% on T2I-CompBench++ and around 20% on DPG-Bench.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_SILMM_Self-Improving_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Leigang and Li, Haochuan and Wang, Wenjie and Liu, Xiang and Li, Juncheng and Nie, Liqiang and Chua, Tat-Seng},
    title     = {SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18497-18508}
}

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

affiliations:
["National University of Singapore", "University of Science and Technology of China", "Zhejiang University", "Harbin Institute of Technology (Shenzhen)"]


title: Calibrated Multi-Preference Optimization for Aligning Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18465-18475
affiliations: ["Google DeepMind", "KAIST", "Google", "Google Research", "Georgia Institute of Technology"]
cities: ["Mountain View", "Daejeon", "Mountain View", "Mountain View", "Atlanta"]
countries: ["USA", "South Korea", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Calibrated_Multi-Preference_Optimization_for_Aligning_Diffusion_Models_CVPR_2025_paper.html

authors:
Kyungmin Lee, Xiahong Li, Qifei Wang, Junfeng He, Junjie Ke, Ming-Hsuan Yang, Irfan Essa, Jinwoo Shin, Feng Yang, Yinxiao Li

abstract:
Aligning text-to-image (T2I) diffusion models with prefer-ence optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability. Using reward models offers an alternative, however, current preference optimization methods fall short in exploiting the rich information, as they only consider pairwise preference distribution. Furthermore, they lack generalization to multi-preference scenarios and struggle to handle inconsistencies between rewards. To address this, we present Calibrated Preference Optimization (CaPO), a novel method to align T2I diffusion models by incorporating the general preference from multiple reward models without human annotated data. The core of our approach involves a reward calibration method to approximate the general preference by computing the expected win-rate against the samples generated by the pretrained models. Additionally, we propose a frontier-based pair selection method that effectively manages the multi-preference distribution by selecting pairs from Pareto frontiers. Finally, we use regression loss to fine-tune diffusion models to match the difference between calibrated rewards of a selected pair. Experimental results show that CaPO consistently outperforms prior methods, such as Direct Preference Optimization (DPO), in both single and multi-reward settings validated by evaluation on T2I benchmarks, including GenEval and T2I-Compbench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Calibrated_Multi-Preference_Optimization_for_Aligning_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Calibrated_Multi-Preference_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Kyungmin and Li, Xiahong and Wang, Qifei and He, Junfeng and Ke, Junjie and Yang, Ming-Hsuan and Essa, Irfan and Shin, Jinwoo and Yang, Feng and Li, Yinxiao},
    title     = {Calibrated Multi-Preference Optimization for Aligning Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18465-18475}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


title: Learning from Neighbors: Category Extrapolation for Long-Tail Learning
authors: Shizhen Zhao, Xin Wen, Jiahui Liu, Chuofan Ma, Chunfeng Yuan, Xiaojuan Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30483-30492
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Learning_from_Neighbors_Category_Extrapolation_for_Long-Tail_Learning_CVPR_2025_paper.html

abstract:
Balancing training on long-tail data distributions remains a long-standing challenge in deep learning. While methods such as re-weighting and re-sampling help alleviate the imbalance issue, limited sample diversity continues to hinder models from learning robust and generalizable feature representations, particularly for tail classes. In contrast to existing methods, we offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance. In this paper, we investigate this phenomenon through both quantitative and qualitative studies, showing that increased granularity enhances the generalization of learned features in tail categories. Motivated by these findings, we propose a method to increase dataset granularity through category extrapolation. Specifically, we introduce open-set fine-grained classes that are related to existing ones, aiming to enhance representation learning for both head and tail classes. To automate the curation of auxiliary data, we leverage large language models (LLMs) as knowledge bases to search for auxiliary categories and retrieve relevant images through web crawling. To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss that encourages the model to focus on class discrimination within the target dataset. During inference, the classifier weights for auxiliary categories are masked out, leaving only the target class weights for use.  Extensive experiments on three standard long-tail benchmarks demonstrate the effectiveness of our approach, notably outperforming strong baseline methods that use the same amount of data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Learning_from_Neighbors_Category_Extrapolation_for_Long-Tail_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Learning_from_Neighbors_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Shizhen and Wen, Xin and Liu, Jiahui and Ma, Chuofan and Yuan, Chunfeng and Qi, Xiaojuan},
    title     = {Learning from Neighbors: Category Extrapolation for Long-Tail Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30483-30492}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["The University of Hong Kong", "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"]


title: Material Anything: Generating Materials for Any 3D Object via Diffusion
authors: Xin Huang, Tengfei Wang, Ziwei Liu, Qing Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26556-26565
cities: ["Xi'an", "Shanghai", "Singapore"]
countries: ["China", "China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Material_Anything_Generating_Materials_for_Any_3D_Object_via_Diffusion_CVPR_2025_paper.html

abstract:
We present **Material Anything**, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverages a pre-trained image diffusion model, enhanced with a triple-head architecture and rendering loss to improve stability and material quality. Additionally, we introduce confidence masks as a dynamic switcher within the diffusion model, enabling it to effectively handle both textured and texture-less objects across varying lighting conditions. By employing a progressive material generation strategy guided by these confidence masks, along with a UV-space material refiner, our method ensures consistent, UV-ready material outputs. Extensive experiments demonstrate our approach outperforms existing methods across a wide range of object categories and lighting conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Material_Anything_Generating_Materials_for_Any_3D_Object_via_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Material_Anything_Generating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Xin and Wang, Tengfei and Liu, Ziwei and Wang, Qing},
    title     = {Material Anything: Generating Materials for Any 3D Object via Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26556-26565}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Northwestern Polytechnical University", "Shanghai AI Lab", "S-Lab, Nanyang Technological University"]


title: TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization
authors: Liang Pan, Zeshi Yang, Zhiyang Dou, Wenjia Wang, Buzhen Huang, Bo Dai, Taku Komura, Jingbo Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5379-5391
cities: ["Shanghai", "Hong Kong", "Unknown", "Nanjing", "Unknown"]
countries: ["China", "China", "Unknown", "China", "Unknown"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_TokenHSI_Unified_Synthesis_of_Physical_Human-Scene_Interactions_through_Task_Tokenization_CVPR_2025_paper.html

abstract:
Synthesizing diverse and physically plausible Human-Scene Interactions (HSI) is pivotal for both computer animation and embodied AI. Despite encouraging progress, current methods mainly focus on developing separate controllers, each specialized for a specific interaction task. This significantly hinders the ability to tackle a wide variety of challenging HSI tasks that require the integration of multiple skills, e.g., sitting down while carrying an object (see Fig. 1). To address this issue, we present TokenHSI, a single, unified transformer-based policy capable of multi-skill unification and flexible adaptation. The key insight is to model the humanoid proprioception as a separate shared token and combine it with distinct task tokens via a masking mechanism. Such a unified policy enables effective knowledge sharing across skills, thereby facilitating the multi-task training. Moreover, our policy architecture supports variable length inputs, enabling flexible adaptation of learned skills to new scenarios. By training additional task tokenizers, we can not only modify the geometries of interaction targets but also coordinate multiple skills to address complex tasks. The experiments demonstrate that our approach can significantly improve versatility, adaptability, and extensibility in various HSI tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_TokenHSI_Unified_Synthesis_of_Physical_Human-Scene_Interactions_through_Task_Tokenization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_TokenHSI_Unified_Synthesis_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Liang and Yang, Zeshi and Dou, Zhiyang and Wang, Wenjia and Huang, Buzhen and Dai, Bo and Komura, Taku and Wang, Jingbo},
    title     = {TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5379-5391}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Feeling AI", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Shanghai AI Laboratory", "The University of Hong Kong", "Independent Researcher", "Southeast University", "Feeling AI"]


title: ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion
authors: Nissim Maruani, Wang Yifan, Matthew Fisher, Pierre Alliez, Mathieu Desbrun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 605-617
cities: ["Sophia Antipolis", "San Jose", "San Jose", "Sophia Antipolis", "Paris"]
countries: ["France", "USA", "USA", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Maruani_ShapeShifter_3D_Variations_Using_Multiscale_and_Sparse_Point-Voxel_Diffusion_CVPR_2025_paper.html

abstract:
This paper proposes ShapeShifter, a new 3D generative model that learns to synthesize shape variations based on a single reference model. While generative methods for 3D objects have recently attracted much attention, current techniques often lack geometric details and/or require long training times and large resources. Our approach remedies these issues by combining sparse voxel grids and multiscale point, normal, and color sampling within an encoder-free neural architecture that can be trained efficiently and in parallel. We show that our resulting variations better capture the fine details of their original input and can capture more general types of surfaces than previous SDF-based methods. Moreover, we offer interactive generation of 3D shape variants, allowing more human control in the design loop if needed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Maruani_ShapeShifter_3D_Variations_Using_Multiscale_and_Sparse_Point-Voxel_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maruani_ShapeShifter_3D_Variations_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Maruani_2025_CVPR,
    author    = {Maruani, Nissim and Yifan, Wang and Fisher, Matthew and Alliez, Pierre and Desbrun, Mathieu},
    title     = {ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {605-617}
}

raw_affiliation:
[{"affiliation": "Inria, Universit\u00b4e C\u02c6ote d\u2019Azur", "city": "Sophia Antipolis", "country": "France"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Inria, Universit\u00b4e C\u02c6ote d\u2019Azur", "city": "Sophia Antipolis", "country": "France"}, {"affiliation": "Inria/X, IP Paris", "city": "Paris", "country": "France"}]

affiliations:
["Inria, Universit\u00b4e C\u02c6ote d\u2019Azur", "Adobe Research", "Adobe Research", "Inria, Universit\u00b4e C\u02c6ote d\u2019Azur", "Inria/X, IP Paris"]


authors: Haoyuan Yang, Xiaoou Li, Jiaming Lv, Xianjun Cheng, Qilong Wang, Peihua Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30020-30031
cities: ["Dalian", "Beijing", "Tianjin"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html

title:
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning

abstract:
Adapting CLIP models for few-shot recognition has recently attracted significant attention. Despite considerable progress, these adaptations remain hindered by the pervasive challenge of data scarcity. Text-to-image models, capable of generating abundant photorealistic labeled images, offer a promising solution. However, existing approaches treat synthetic images merely as complements to real images, rather than as standalone knowledge repositories stemming from distinct foundation models. To overcome this limitation, we reconceptualize synthetic images as an *imagined base set*, i.e., a unique, large-scale synthetic dataset encompassing diverse concepts. We introduce a novel CLIP adaptation methodology called *ImagineFSL*, involving pretraining on the imagined base set followed by fine-tuning on downstream few-shot tasks. We find that, compared to no pretraining, both supervised   and self-supervised pretraining are beneficial, with the latter providing better performance. Building on this finding, we propose an improved self-supervised method tailored for few-shot scenarios, enhancing the transferability of representations from synthetic to real image domains. Additionally, we present an image generation pipeline that employs chain-of-thought and in-context learning techniques, harnessing foundation models to automatically generate diverse, realistic images. Our methods are validated across eleven datasets, consistently outperforming state-of-the-art methods by substantial margins.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Haoyuan and Li, Xiaoou and Lv, Jiaming and Cheng, Xianjun and Wang, Qilong and Li, Peihua},
    title     = {ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30020-30031}
}

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

affiliations:
["Dalian University of Technology", "Beijing University of Posts and Telecommunications", "Tianjin University"]


title: Continuous Locomotive Crowd Behavior Generation
authors: Inhwan Bae, Junoh Lee, Hae-Gon Jeon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22416-22431
affiliations: ["Gwangju Institute of Science and Technology"]
cities: ["Gwangju"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bae_Continuous_Locomotive_Crowd_Behavior_Generation_CVPR_2025_paper.html

abstract:
Modeling and reproducing crowd behaviors are important in various domains including psychology, robotics, transport engineering and virtual environments. Conventional methods have focused on synthesizing momentary scenes, which have difficulty in replicating the continuous nature of real-world crowds. In this paper, we introduce a novel method for automatically generating continuous, realistic crowd trajectories with heterogeneous behaviors and interactions among individuals. We first design a crowd emitter model. To do this, we obtain spatial layouts from single input images, including a segmentation map, appearance map, population density map and population probability, prior to crowd generation. The emitter then continually places individuals on the timeline by assigning independent behavior characteristics such as agents' type, pace, and start/end positions using diffusion models. Next, our crowd simulator produces their long-term locomotions. To simulate diverse actions, it can augment their behaviors based on a Markov chain. As a result, our overall framework populates the scenes with heterogeneous crowd behaviors by alternating between the proposed emitter and simulator. Note that all the components in the proposed framework are user-controllable. Lastly, we propose a benchmark protocol to evaluate the realism and quality of the generated crowds in terms of the scene-level population dynamics and the individual-level trajectory accuracy. We demonstrate that our approach effectively models diverse crowd behavior patterns and generalizes well across different geographical environments. Code is publicly available at https://github.com/InhwanBae/CrowdES.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bae_Continuous_Locomotive_Crowd_Behavior_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Bae_2025_CVPR,
    author    = {Bae, Inhwan and Lee, Junoh and Jeon, Hae-Gon},
    title     = {Continuous Locomotive Crowd Behavior Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22416-22431}
}

raw_affiliation:
[{"affiliation": "Gwangju Institute of Science and Technology", "city": "Gwangju", "country": "South Korea"}]


title: Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness
authors: Beier Zhu, Jiequan Cui, Hanwang Zhang, Chi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25487-25496
affiliations: ["Nanyang Technological University", "Westlake University"]
cities: ["Singapore", "Hangzhou"]
countries: ["Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Project-Probe-Aggregate_Efficient_Fine-Tuning_for_Group_Robustness_CVPR_2025_paper.html

abstract:
While image-text foundation models have succeeded across diverse downstream tasks, they still face challenges in the presence of spurious correlations between the input and label. To address this issue, we propose a simple three-step approach-Project-Probe-Aggregate (PPA)-that enables parameter-efficient fine-tuning for foundation models without relying on group annotations. Building upon the failure-based debiasing scheme, our method, PPA, improves its two key components: minority samples identification and the robust training algorithm. Specifically, we first train biased classifiers by projecting image features onto the nullspace of class proxies from text encoders. Next, we infer group labels using the biased classifier and probe group targets with prior correction. Finally, we aggregate group weights of each class to produce the debiased classifier. Our theoretical analysis shows that our PPA enhances minority group identification and is Bayes optimal for minimizing the balanced group error, mitigating spurious correlations. Extensive experimental results confirm the effectiveness of our PPA: it outperforms the state-of-the-art by an average worst-group accuracy while requiring less than 0.01% tunable parameters without training group labels.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Project-Probe-Aggregate_Efficient_Fine-Tuning_for_Group_Robustness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Project-Probe-Aggregate_Efficient_Fine-Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Beier and Cui, Jiequan and Zhang, Hanwang and Zhang, Chi},
    title     = {Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25487-25496}
}

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


title: Implicit Bias Injection Attacks against Text-to-Image Diffusion Models
authors: Huayang Huang, Xiangye Jin, Jiaxu Miao, Yu Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28779-28789
cities: ["Wuhan", "Wuhan", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Implicit_Bias_Injection_Attacks_against_Text-to-Image_Diffusion_Models_CVPR_2025_paper.html

abstract:
The proliferation of text-to-image diffusion models (T2I DMs) has led to an increased presence of AI-generated images in daily life. However, biased T2I models can generate content with specific tendencies, potentially influencing people's perceptions. Intentional exploitation of these biases risks conveying misleading information to the public. Current research on bias primarily addresses explicit biases with recognizable visual patterns, such as skin color and gender. This paper introduces a novel form of implicit bias that lacks explicit visual features but can manifest in diverse ways across various semantic contexts. This subtle and versatile nature makes this bias challenging to detect, easy to propagate, and adaptable to a wide range of scenarios. We further propose an implicit bias injection attack framework (IBI-Attacks) against T2I diffusion models by precomputing a general bias direction in the prompt embedding space and adaptively adjusting it based on different inputs. Our attack module can be seamlessly integrated into pre-trained diffusion models in a plug-and-play manner without direct manipulation of user input or model retraining. Extensive experiments validate the effectiveness of our scheme in introducing bias through subtle and diverse modifications while preserving the original semantics.The strong concealment and transferability of our attack across various scenarios further underscore the significance of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Implicit_Bias_Injection_Attacks_against_Text-to-Image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Implicit_Bias_Injection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Huayang and Jin, Xiangye and Miao, Jiaxu and Wu, Yu},
    title     = {Implicit Bias Injection Attacks against Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28779-28789}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Mathematics and Statistics, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Cyber Science and Technology, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "School of Mathematics and Statistics, Wuhan University", "School of Cyber Science and Technology, Sun Yat-sen University"]


title: ROICtrl: Boosting Instance Control for Visual Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23658-23667
affiliations: ["Show Lab, National University of Singapore", "GenAI, Meta", "MIT"]
cities: ["Singapore", "Menlo Park", "Cambridge"]
countries: ["Singapore", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_ROICtrl_Boosting_Instance_Control_for_Visual_Generation_CVPR_2025_paper.html

authors:
Yuchao Gu, Yipin Zhou, Yunfan Ye, Yixin Nie, Licheng Yu, Pingchuan Ma, Kevin Qinghong Lin, Mike Zheng Shou

abstract:
Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances. To address this limitation, this work enhances diffusion models by introducing regional instance control, where each instance is governed by a bounding box paired with a free-form caption. Previous methods in this area typically rely on implicit position encoding or explicit attention masks to separate regions of interest (ROIs), resulting in either inaccurate coordinate injection or large computational overhead. Inspired by ROI-Align in object detection, we introduce a complementary operation called ROI-Unpool. Together, ROI-Align and ROI-Unpool enable explicit, efficient, and accurate ROI manipulation on high-resolution feature maps for visual generation. Building on ROI-Unpool, we propose ROICtrl, an adapter for pretrained diffusion models that enables precise regional instance control. ROICtrl is compatible with community-finetuned diffusion models, as well as with existing spatial-based add-ons (e.g., ControlNet, T2I-Adapter) and embedding-based add-ons (e.g., IP-Adapter, ED-LoRA), extending their applications to multi-instance generation. Experiments show that ROICtrl achieves superior performance in regional instance control while significantly reducing computational costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_ROICtrl_Boosting_Instance_Control_for_Visual_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_ROICtrl_Boosting_Instance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Yuchao and Zhou, Yipin and Ye, Yunfan and Nie, Yixin and Yu, Licheng and Ma, Pingchuan and Lin, Kevin Qinghong and Shou, Mike Zheng},
    title     = {ROICtrl: Boosting Instance Control for Visual Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23658-23667}
}

raw_affiliation:
[{"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "GenAI, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}]


title: FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 281-291
affiliations: ["Australian National University", "Meta Reality Labs Research"]
cities: ["Canberra", "Menlo Park"]
countries: ["Australia", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_FRESA_Feedforward_Reconstruction_of_Personalized_Skinned_Avatars_from_Few_Images_CVPR_2025_paper.html

authors:
Rong Wang, Fabian Prada, Ziyan Wang, Zhongshi Jiang, Chengxiang Yin, Junxuan Li, Shunsuke Saito, Igor Santesteban, Javier Romero, Rohan Joshi, Hongdong Li, Jason Saragih, Yaser Sheikh

abstract:
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images. Due to the large variations in body shapes, poses, and cloth types, existing methods mostly require hours of per-subject optimization during inference, which limits their practical applications. In contrast, we learn a universal prior from over a thousand clothed humans to achieve instant feedforward generation and zero-shot generalization. Specifically, instead of rigging the avatar with shared skinning weights, we jointly infer personalized avatar shape, skinning weights, and pose-dependent deformations, which effectively improves overall geometric fidelity and reduces deformation artifacts. Moreover, to normalize pose variations and resolve coupled ambiguity between canonical shapes and skinning weights, we design a 3D canonicalization process to produce pixel-aligned initial conditions, which helps to reconstruct fine-grained geometric details. We then propose a multi-frame feature aggregation to robustly reduce artifacts introduced in canonicalization and fuse a plausible avatar preserving person-specific identities. Finally, we train the model in an end-to-end framework on a large-scale capture dataset, which contains diverse human subjects paired with high-quality 3D scans. Extensive experiments show that our method generates more authentic reconstruction and animation than state-of-the-arts, and can be directly generalized to inputs from casually taken phone photos. Project page and code is available at https://github.com/rongakowang/FRESA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_FRESA_Feedforward_Reconstruction_of_Personalized_Skinned_Avatars_from_Few_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_FRESA_Feedforward_Reconstruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Rong and Prada, Fabian and Wang, Ziyan and Jiang, Zhongshi and Yin, Chengxiang and Li, Junxuan and Saito, Shunsuke and Santesteban, Igor and Romero, Javier and Joshi, Rohan and Li, Hongdong and Saragih, Jason and Sheikh, Yaser},
    title     = {FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {281-291}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Meta Reality Labs Research", "city": "Menlo Park", "country": "USA"}]


authors: Zhenyang Liu, Yikai Wang, Sixiao Zheng, Tongying Pan, Longfei Liang, Yanwei Fu, Xiangyang Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3718-3727
cities: ["Shanghai", "Singapore", "Shanghai", "Unknown"]
countries: ["China", "Singapore", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_ReasonGrounder_LVLM-Guided_Hierarchical_Feature_Splatting_for_Open-Vocabulary_3D_Visual_Grounding_CVPR_2025_paper.html

title:
ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning

abstract:
Open-vocabulary 3D visual grounding and reasoning aim to localize objects in a scene based on implicit language descriptions, even when they are occluded. This ability is crucial for tasks such as vision-language navigation and autonomous robotics. However, current methods struggle because they rely heavily on fine-tuning with 3D annotations and mask proposals, which limits their ability to handle diverse semantics and common knowledge required for effective reasoning. To address this, we propose ReasonGrounder, an LVLM-guided framework that uses hierarchical 3D feature Gaussian fields for adaptive grouping based on physical scale, enabling open-vocabulary 3D grounding and reasoning. ReasonGrounder interprets implicit instructions using large vision-language models (LVLM) and localizes occluded objects through 3D Gaussian splatting. By incorporating 2D segmentation masks from the Segment Anything Model (SAM) and multi-view CLIP embeddings, ReasonGrounder selects Gaussian groups based on object scale, enabling accurate localization through both explicit and implicit language understanding, even in novel, occluded views. We also contribute ReasoningGD, a new dataset containing over 10K scenes and 2 million annotations for evaluating open-vocabulary 3D grounding and amodal perception under occlusion. Experiments show that ReasonGrounder significantly improves 3D grounding accuracy in real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_ReasonGrounder_LVLM-Guided_Hierarchical_Feature_Splatting_for_Open-Vocabulary_3D_Visual_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_ReasonGrounder_LVLM-Guided_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhenyang and Wang, Yikai and Zheng, Sixiao and Pan, Tongying and Liang, Longfei and Fu, Yanwei and Xue, Xiangyang},
    title     = {ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3718-3727}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "NeuHelium Co., Ltd", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Fudan University", "Nanyang Technological University", "Shanghai Innovation Institute", "NeuHelium Co., Ltd"]


title: Cropper: Vision-Language Model for Image Cropping through In-Context Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30010-30019
cities: ["Mountain View", "Mountain View", "Mountain View", "Ann Arbor", "College Park", "Seoul"]
countries: ["USA", "USA", "USA", "USA", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Cropper_Vision-Language_Model_for_Image_Cropping_through_In-Context_Learning_CVPR_2025_paper.html

authors:
Seung Hyun Lee, Jijun Jiang, Yiran Xu, Zhuofang Li, Junjie Ke, Yinxiao Li, Junfeng He, Steven Hickson, Katie Datsenko, Sangpil Kim, Ming-Hsuan Yang, Irfan Essa, Feng Yang

abstract:
The goal of image cropping is to identify visually appealing crops in an image. Conventional methods are trained on specific datasets and fail to adapt to new requirements. Recent breakthroughs in large vision-language models (VLMs) enable visual in-context learning without explicit training. However, downstream tasks with VLMs remain under explored. In this paper, we propose an effective approach to leverage VLMs for image cropping. First, we propose an efficient prompt retrieval mechanism for image cropping to automate the selection of in-context examples. Second, we introduce an iterative refinement strategy to iteratively enhance the predicted crops. The proposed framework, we refer to as  Cropper, is applicable to a wide range of cropping tasks, including free-form cropping, subject-aware cropping, and aspect ratio-aware cropping. Extensive experiments demonstrate that Cropper significantly outperforms state-of-the-art methods across several benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Cropper_Vision-Language_Model_for_Image_Cropping_through_In-Context_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Cropper_Vision-Language_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Seung Hyun and Jiang, Jijun and Xu, Yiran and Li, Zhuofang and Ke, Junjie and Li, Yinxiao and He, Junfeng and Hickson, Steven and Datsenko, Katie and Kim, Sangpil and Yang, Ming-Hsuan and Essa, Irfan and Yang, Feng},
    title     = {Cropper: Vision-Language Model for Image Cropping through In-Context Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30010-30019}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Google DeepMind", "Google Research", "Google", "University of Michigan", "University of Maryland", "Korea University"]


title: Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack
authors: Nicole Meng, Caleb Manicke, Ronak Sahu, Caiwen Ding, Yingjie Lao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16388-16397
affiliations: ["1", "2", "3"]
cities: ["unknown", "unknown", "unknown"]
countries: ["unknown", "unknown", "unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_Advancing_Adversarial_Robustness_in_GNeRFs_The_IL2-NeRF_Attack_CVPR_2025_paper.html

abstract:
Generalizable Neural Radiance Fields (GNeRF) are recognized as one of the most promising techniques for novel view synthesis and 3D model generation in real-world applications. However, like other generative models in computer vision, ensuring their adversarial robustness against various threat models is essential for practical use. The pioneering work in this area, NeRFool, introduced a state-of-the-art attack that targets GNeRFs by manipulating source views before feature extraction, successfully disrupting the color and density results of the constructed views. Building on this foundation, we propose IL2-NeRF (Iterative L2 NeRF Attack), a novel adversarial attack method that explores a new threat model (in the L2 domain) for attacking GNeRFs.  We evaluated IL2-NeRF against two standard GNeRF models across three benchmark datasets, demonstrating similar performance compared to NeRFool, based on the same evaluation metrics proposed by NeRFool. Our results establish IL2-NeRF as the first adversarial method for GNeRFs under the L2 norm. We establish a foundational L2 threat model for future research, enabling direct performance comparisons while introducing a smoother, image-wide perturbation approach in Adversarial 3D Reconstruction. Our code is available at: https://github.com/The-NRC-SCAR-Group/IL2-NeRF

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Advancing_Adversarial_Robustness_in_GNeRFs_The_IL2-NeRF_Attack_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_Advancing_Adversarial_Robustness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Nicole and Manicke, Caleb and Sahu, Ronak and Ding, Caiwen and Lao, Yingjie},
    title     = {Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16388-16397}
}

raw_affiliation:
[{"affiliation": "1", "city": "unknown", "country": "unknown"}, {"affiliation": "2", "city": "unknown", "country": "unknown"}, {"affiliation": "3", "city": "unknown", "country": "unknown"}]


title: WonderWorld: Interactive 3D Scene Generation from a Single Image
authors: Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T. Freeman, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5916-5926
affiliations: ["Stanford University", "MIT"]
cities: ["Stanford", "Cambridge"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_WonderWorld_Interactive_3D_Scene_Generation_from_a_Single_Image_CVPR_2025_paper.html

abstract:
We present WonderWorld, a novel framework for interactive 3D scene generation that enables users to interactively specify scene contents and layout and see the created scenes in low latency. The major challenge lies in achieving fast generation of 3D scenes. Existing scene generation approaches fall short of speed as they often require (1) progressively generating many views and depth maps, and (2) time-consuming optimization of the scene representations. Our approach does not need multiple views, and it leverages a geometry-based initialization that significantly reduces optimization time. Another challenge is generating coherent geometry that allows all scenes to be connected. We introduce the guided depth diffusion that allows partial conditioning of depth estimation. WonderWorld generates connected and diverse 3D scenes in less than 10 seconds on a single A6000 GPU, enabling real-time user interaction and exploration. Our interactive demo, full code, data, and software can be found at https://kovenyu.com/WonderWorld/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_WonderWorld_Interactive_3D_Scene_Generation_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_WonderWorld_Interactive_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Hong-Xing and Duan, Haoyi and Herrmann, Charles and Freeman, William T. and Wu, Jiajun},
    title     = {WonderWorld: Interactive 3D Scene Generation from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5916-5926}
}

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


title: A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions
authors: Jiangbei Hu, Yanggeng Li, Fei Hou, Junhui Hou, Zhebin Zhang, Shengfa Wang, Na Lei, Ying He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1297-1307
cities: ["Dalian", "Singapore", "Beijing", "Beijing", "Hong Kong", "Dongguan"]
countries: ["China", "Singapore", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_A_Lightweight_UDF_Learning_Framework_for_3D_Reconstruction_Based_on_CVPR_2025_paper.html

abstract:
Unsigned distance fields (UDFs) provide a versatile framework for representing a diverse array of 3D shapes, encompassing both watertight and non-watertight geometries. Traditional UDF learning methods typically require extensive training on large 3D shape datasets, which is costly and necessitates re-training for new datasets. This paper presents a novel neural framework, *LoSF-UDF*, for reconstructing surfaces from 3D point clouds by leveraging local shape functions to learn UDFs. We observe that 3D shapes manifest simple patterns in localized regions, prompting us to develop a training dataset of point cloud patches characterized by mathematical functions that represent a continuum from smooth surfaces to sharp edges and corners. Our approach learns features within a specific radius around each query point and utilizes an attention mechanism to focus on the crucial features for UDF estimation. Despite being highly lightweight, with only 653 KB of trainable parameters and a modest-sized training dataset with 0.5 GB storage, our method enables efficient and robust surface reconstruction from point clouds without requiring for shape-specific training. Furthermore, our method exhibits enhanced resilience to noise and outliers in point clouds compared to existing methods. We conduct comprehensive experiments and comparisons across various datasets, including synthetic and real-scanned point clouds, to validate our method's efficacy. Notably, our lightweight framework offers rapid and reliable initialization for other unsupervised iterative approaches, improving both the efficiency and accuracy of their reconstructions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_A_Lightweight_UDF_Learning_Framework_for_3D_Reconstruction_Based_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_A_Lightweight_UDF_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Jiangbei and Li, Yanggeng and Hou, Fei and Hou, Junhui and Zhang, Zhebin and Wang, Shengfa and Lei, Na and He, Ying},
    title     = {A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1297-1307}
}

raw_affiliation:
[{"affiliation": "School of Software, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "CCDS, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Key Laboratory of System Software (CAS) and SKLCS, Institute of Software, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "OPPO", "city": "Dongguan", "country": "China"}]

affiliations:
["School of Software, Dalian University of Technology", "CCDS, Nanyang Technological University", "Key Laboratory of System Software (CAS) and SKLCS, Institute of Software, CAS", "University of Chinese Academy of Sciences", "Department of Computer Science, City University of Hong Kong", "OPPO"]


title: DiffCAM: Data-Driven Saliency Maps by Capturing Feature Differences
authors: Xingjian Li, Qiming Zhao, Neelesh Bisht, Mostofa Rafid Uddin, Jin Yu Kim, Bryan Zhang, Min Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10327-10337
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "United States"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["United States"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DiffCAM_Data-Driven_Saliency_Maps_by_Capturing_Feature_Differences_CVPR_2025_paper.html

abstract:
In recent years, the interpretability of Deep Neural Networks (DNNs) has garnered significant attention, particularly due to their widespread deployment in critical domains like healthcare, finance, and autonomous systems. To address the challenge of understanding how DNNs make decisions, Explainable AI (XAI) methods, such as saliency maps, have been developed to provide insights into the inner workings of these models. This paper introduces DiffCAM, a novel XAI method designed to overcome limitations in existing Class Activation Map (CAM)-based techniques, which often rely on decision boundary gradients to estimate feature importance. DiffCAM differentiates itself by considering the actual data distribution of the reference class, identifying feature importance based on how a target example differs from reference examples. This approach captures the most discriminative features without relying on decision boundaries or prediction results, making DiffCAM applicable to a broader range of models, including foundation models. Through extensive experiments, we demonstrate the superior performance and flexibility of DiffCAM in providing meaningful explanations across diverse datasets and scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DiffCAM_Data-Driven_Saliency_Maps_by_Capturing_Feature_Differences_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DiffCAM_Data-Driven_Saliency_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xingjian and Zhao, Qiming and Bisht, Neelesh and Uddin, Mostofa Rafid and Kim, Jin Yu and Zhang, Bryan and Xu, Min},
    title     = {DiffCAM: Data-Driven Saliency Maps by Capturing Feature Differences},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10327-10337}
}


title: PolarNeXt: Rethink Instance Segmentation with Polar Representation
authors: Jiacheng Sun, Xinghong Zhou, Yiqiang Wu, Bin Zhu, Jiaxuan Lu, Yu Qin, Xiaomao Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19315-19324
affiliations: ["Shanghai University", "Shanghai Artificial Intelligence Laboratory"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_PolarNeXt_Rethink_Instance_Segmentation_with_Polar_Representation_CVPR_2025_paper.html

abstract:
One of the roadblocks for instance segmentation today is heavy computational overhead and model parameters. Previous methods based on Polar Representation made the initial mark to address this challenge by formulating instance segmentation as polygon detection, but failed to align with mainstream methods in performance. In this paper, we highlight that Representation Errors, arising from the limited capacity of polygons to capture boundary details, have long been overlooked, which results in severe performance degradation. Observing that optimal starting point selection effectively alleviates this issue, we propose an Adaptive Polygonal Sample Decision strategy to dynamically capture the positional variation of representation errors across samples. Additionally, we design a Union-aligned Rasterization Module to incorporate these errors into polygonal assessment, further advancing the proposed strategy. With these components, our framework called PolarNeXt achieves a remarkable performance boost of over 4.8% AP compared to other polar-based methods. PolarNeXt is markedly more lightweight and efficient than state-of-the-art instance segmentation methods, while achieving comparable segmentation accuracy. We expect this work will open up a new direction for instance segmentation in high-resolution images and resource-limited scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_PolarNeXt_Rethink_Instance_Segmentation_with_Polar_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_PolarNeXt_Rethink_Instance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Jiacheng and Zhou, Xinghong and Wu, Yiqiang and Zhu, Bin and Lu, Jiaxuan and Qin, Yu and Li, Xiaomao},
    title     = {PolarNeXt: Rethink Instance Segmentation with Polar Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19315-19324}
}

raw_affiliation:
[{"affiliation": "Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]


title: ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27872-27882
cities: ["Guangzhou", "Shenzhen", "Shanghai", "Beijing", "Shanghai", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_ScaMo_Exploring_the_Scaling_Law_in_Autoregressive_Motion_Generation_Model_CVPR_2025_paper.html

authors:
Shunlin Lu, Jingbo Wang, Zeyu Lu, Ling-Hao Chen, Wenxun Dai, Junting Dong, Zhiyang Dou, Bo Dai, Ruimao Zhang

abstract:
The scaling law has been validated in various domains, such as natural language processing (NLP) and massive computer vision tasks; however, its application to motion generation remains largely unexplored. In this paper, we introduce a scalable motion generation framework that includes the motion tokenizer Motion FSQ-VAE and a text-prefix autoregressive transformer. Through comprehensive experiments, we observe the scaling behavior of this system. For the first time, we confirm the existence of scaling laws within the context of motion generation. Specifically, our results demonstrate that the normalized test loss of our prefix autoregressive models adheres to a logarithmic law in relation to compute budgets. Furthermore, we also confirm the power law between Non-Vocabulary Parameters, Vocabulary Parameters, and Data Tokens with respect to compute budgets respectively. Leveraging the scaling law, we predict the optimal transformer size, vocabulary size, and data requirements for a compute budget of 1e18. The test loss of the system, when trained with the optimal model size, vocabulary size, and required data, aligns precisely with the predicted test loss.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_ScaMo_Exploring_the_Scaling_Law_in_Autoregressive_Motion_Generation_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_ScaMo_Exploring_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Shunlin and Wang, Jingbo and Lu, Zeyu and Chen, Ling-Hao and Dai, Wenxun and Dong, Junting and Dou, Zhiyang and Dai, Bo and Zhang, Ruimao},
    title     = {ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27872-27882}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hongkong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Feeling AI", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Sun Yat-sen University", "The Chinese University of Hongkong, Shenzhen", "Shanghai AI Laboratory", "Tsinghua University", "Feeling AI", "Shanghai Jiao Tong University", "The University of Hong Kong"]


title: From Sparse Signal to Smooth Motion: Real-Time Motion Generation with Rolling Prediction Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1850-1860
cities: ["Menlo Park", "Barcelona", "Barcelona", "London"]
countries: ["USA", "Spain", "Spain", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Barquero_From_Sparse_Signal_to_Smooth_Motion_Real-Time_Motion_Generation_with_CVPR_2025_paper.html

authors:
German Barquero, Nadine Bertsch, Manojkumar Marramreddy, Carlos Chacón, Filippo Arcadu, Ferran Rigual, Nicky Sijia He, Cristina Palmero, Sergio Escalera, Yuting Ye, Robin Kips

abstract:
In extended reality (XR), generating full-body motion of the users is important to understand their actions, drive their virtual avatars for social interaction, and convey a realistic sense of presence. While prior works focused on spatially sparse and always-on input signals from motion controllers, many XR applications opt for vision-based hand tracking for reduced user friction and better immersion. Compared to controllers, hand tracking signals are less accurate and can even be missing for an extended period of time. To handle such unreliable inputs, we present Rolling Prediction Model (RPM), an online and real-time approach that generates smooth full-body motion from temporally and spatially sparse input signals. Our model generates 1) accurate motion that matches the inputs (i.e., tracking mode) and 2) plausible motion when inputs are missing (i.e., synthesis mode). More importantly, RPM generates seamless transitions from tracking to synthesis, and vice versa. To demonstrate the practical importance of handling noisy and missing inputs, we present GORP, the first dataset of realistic sparse inputs from a commercial virtual reality (VR) headset with paired high quality body motion ground truth. GORP provides >14 hours of VR gameplay data from 28 people using motion controllers (spatially sparse) and hand tracking (spatially and temporally sparse). We benchmark RPM against the state of the art on both synthetic data and GORP to highlight how we can bridge the gap for real-world applications with a realistic dataset and by handling unreliable input signals. Our code, pretrained models, and GORP dataset are available in the project webpage.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Barquero_From_Sparse_Signal_to_Smooth_Motion_Real-Time_Motion_Generation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Barquero_From_Sparse_Signal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Barquero_2025_CVPR,
    author    = {Barquero, German and Bertsch, Nadine and Marramreddy, Manojkumar and Chac\'on, Carlos and Arcadu, Filippo and Rigual, Ferran and He, Nicky Sijia and Palmero, Cristina and Escalera, Sergio and Ye, Yuting and Kips, Robin},
    title     = {From Sparse Signal to Smooth Motion: Real-Time Motion Generation with Rolling Prediction Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1850-1860}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Universitat de Barcelona", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Computer Vision Center", "city": "Barcelona", "country": "Spain"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "UK"}]

affiliations:
["Meta Reality Labs", "Universitat de Barcelona", "Computer Vision Center", "King\u2019s College London"]


title: Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy
authors: You Li, Fan Ma, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3984-3993
raw_affiliation: [{"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["ReLER, CCAI, Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Imagine_and_Seek_Improving_Composed_Image_Retrieval_with_an_Imagined_CVPR_2025_paper.html

abstract:
The Zero-shot Composed Image Retrieval (ZSCIR) requires retrieving images that match the query image and the relative captions.Current methods focus on projecting the query image into the text feature space, subsequently combining them with features of query texts for retrieval. However, retrieving images only with the text features cannot guarantee detailed alignment due to the natural gap between images and text.In this paper, we introduce Imagined Proxy for CIR, a training-free method that creates a proxy image aligned with the query image and text description, enhancing query representation in the retrieval process.We first leverage the large language model's generalization capability to generate an image layout, and then apply both the query text and image for conditional generation.The robust query features are enhanced by merging the proxy image, query image, and text semantic perturbation. Our newly proposed balancing metric integrates text-based and proxy retrieval similarities, allowing for more accurate retrieval of the target image while incorporating image-side information into the process.Experiments on three public datasets demonstrate that our method significantly improves retrieval performances. We achieve state-of-the-art (SOTA) results on the CIRR dataset with a Recall@K of 70.07 at K=10. Additionally, we achieved an improvement in Recall@10 on the FashionIQ dataset, rising from 45.11 to 45.74, and improved the baseline performance in CIRCO with a mAPK@10 score, increasing from 32.24 to 34.26.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Imagine_and_Seek_Improving_Composed_Image_Retrieval_with_an_Imagined_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Imagine_and_Seek_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, You and Ma, Fan and Yang, Yi},
    title     = {Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3984-3993}
}


title: EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5455-5466
cities: ["Hong Kong", "Hong Kong", "Shenzhen", "Hong Kong", "Guangzhou", "Shenzhen"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_EMOVA_Empowering_Language_Models_to_See_Hear_and_Speak_with_CVPR_2025_paper.html

authors:
Kai Chen, Yunhao Gou, Runhui Huang, Zhili Liu, Daxin Tan, Jing Xu, Chunwei Wang, Yi Zhu, Yihan Zeng, Kuo Yang, Dingdong Wang, Kun Xiang, Haoyuan Li, Haoli Bai, Jianhua Han, Xiaohui Li, Weike Jin, Nian Xie, Yu Zhang, James T. Kwok, Hengshuang Zhao, Xiaodan Liang, Dit-Yan Yeung, Xiao Chen, Zhenguo Li, Wei Zhang, Qun Liu, Lanqing Hong, Lu Hou, Hang Xu

abstract:
GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging for the open-source community. Existing vision-language models rely on external tools for speech processing, while speech-language models still suffer from limited or totally without vision-understanding capabilities. To address this gap, we propose the EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech abilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we surprisingly notice that omni-modal alignment can further enhance vision-language and speech abilities compared with the bi-modal aligned counterparts. Moreover, a lightweight style module is introduced for the flexible speech style controls including emotions and pitches. For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_EMOVA_Empowering_Language_Models_to_See_Hear_and_Speak_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_EMOVA_Empowering_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Kai and Gou, Yunhao and Huang, Runhui and Liu, Zhili and Tan, Daxin and Xu, Jing and Wang, Chunwei and Zhu, Yi and Zeng, Yihan and Yang, Kuo and Wang, Dingdong and Xiang, Kun and Li, Haoyuan and Bai, Haoli and Han, Jianhua and Li, Xiaohui and Jin, Weike and Xie, Nian and Zhang, Yu and Kwok, James T. and Zhao, Hengshuang and Liang, Xiaodan and Yeung, Dit-Yan and Chen, Xiao and Li, Zhenguo and Zhang, Wei and Liu, Qun and Hong, Lanqing and Hou, Lu and Xu, Hang},
    title     = {EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5455-5466}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["Hong Kong University of Science and Technology", "The University of Hong Kong", "Huawei Noah\u2019s Ark Lab", "The Chinese University of Hong Kong", "Sun Yat-sen University", "Southern University of Science and Technology"]


authors: Chongkai Yu, Ting Liu, Anqi Li, Xiaochao Qu, Chengjing Wu, Luoqi Liu, Xiaolin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19356-19365
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_SAM-REF_Introducing_Image-Prompt_Synergy_during_Interaction_for_Detail_Enhancement_in_CVPR_2025_paper.html

title:
SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model

abstract:
Interactive segmentation is to segment the mask of the target object according to the user's interactive prompts. There are two mainstream strategies: early fusion and late fusion. Current specialist models utilize the early fusion strategy that encodes the combination of images and prompts to target the prompted objects, yet repetitive complex computations on the images result in high latency. Late fusion models extract image embeddings once and merge them with the prompts in later interactions. This strategy avoids redundant image feature extraction and improves efficiency significantly. A recent milestone is the Segment Anything Model (SAM). However, this strategy limits the models ability to extract detailed information from the prompted target zone. To address this issue, we propose SAM-REF, a two-stage refinement framework that fully integrates images and prompts by using a lightweight refiner into the interaction of late fusion, which combines the accuracy of early fusion and maintains the efficiency of late fusion. Through extensive experiments, we show that our SAM-REF model outperforms the current state-of-the-art method in most metrics on segmentation quality without compromising efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_SAM-REF_Introducing_Image-Prompt_Synergy_during_Interaction_for_Detail_Enhancement_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_SAM-REF_Introducing_Image-Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Chongkai and Liu, Ting and Li, Anqi and Qu, Xiaochao and Wu, Chengjing and Liu, Luoqi and Hu, Xiaolin},
    title     = {SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19356-19365}
}

raw_affiliation:
[{"affiliation": "MT Lab, Meitu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["MT Lab, Meitu Inc.", "Beijing Institute of Technology", "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University"]


title: DarkIR: Robust Low-Light Image Restoration
authors: Daniel Feijoo, Juan C. Benito, Alvaro Garcia, Marcos V. Conde
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10879-10889
affiliations: ["Cidaut AI", "University of W\u00a8urzburg"]
cities: ["Valladolid", "Wurzburg"]
countries: ["Spain", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feijoo_DarkIR_Robust_Low-Light_Image_Restoration_CVPR_2025_paper.html

abstract:
Photography during night or in dark conditions typically suffers from noise, low light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches in image restoration solve these tasks separately. In this paper, we present an efficient and robust neural network for multi-task low-light image restoration. Instead of following the current tendency of Transformer-based models, we propose new attention mechanisms to enhance the receptive field of efficient CNNs. Our method reduces the computational costs in terms of parameters and MAC operations compared to previous methods. Our model, DarkIR, achieves new state-of-the-art results on the popular LOLBlur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feijoo_DarkIR_Robust_Low-Light_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feijoo_DarkIR_Robust_Low-Light_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feijoo_2025_CVPR,
    author    = {Feijoo, Daniel and Benito, Juan C. and Garcia, Alvaro and Conde, Marcos V.},
    title     = {DarkIR: Robust Low-Light Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10879-10889}
}

raw_affiliation:
[{"affiliation": "Cidaut AI", "city": "Valladolid", "country": "Spain"}, {"affiliation": "University of W\u00a8urzburg", "city": "Wurzburg", "country": "Germany"}]


title: R2C: Mapping Room to Chessboard to Unlock LLM As Low-Level Action Planner
authors: Ziyi Bai, Hanxuan Li, Bin Fu, Chuyan Xiong, Ruiping Wang, Xilin Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19456-19466
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_R2C_Mapping_Room_to_Chessboard_to_Unlock_LLM_As_Low-Level_CVPR_2025_paper.html

abstract:
This paper explores using large language models (LLMs) as low-level action planners for embodied tasks. While LLMs excel as the robot's "brain" for high-level planning, they face challenges in directly controlling the "body" by generating precise low-level actions. This limitation arises from LLMs' strength in high-level conceptual understanding but their inability to handle spatial perception effectively, restricting their potential in embodied tasks. To address this, we bridge the gap by enabling LLMs to not only comprehend complex instructions but also produce actionable, low-level plans. We introduce Room to Chessboard (R2C), a novel semantic representation that maps environmental states onto a grid-based chessboard, empowering LLMs to generate specific low-level coordinates and guide the robot in a manner akin to playing a game of chess. To further enhance decision-making, we propose the Chain-of-Thought Decision (CoT-D) paradigm, which improves LLMs' interpretability and context-awareness in spatial reasoning. By jointly training LLMs for high-level task decomposition and low-level action generation, we create a unified "brain-body" system capable of handling complex, free-form instructions while producing precise low-level actions, allowing the robot to flexibly control its movements and adapt to varying tasks. We validate R2C using both fine-tuned open-source LLMs and GPT-4, demonstrating effectiveness on the challenging ALFRED benchmark. Results show that with our R2C framework, LLMs can effectively act as low-level planners, generalizing across diverse settings and open-vocabulary robotic tasks. The code and demonstrations are available at: https://vipl-vsu.github.io/Room2Chessboard.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_R2C_Mapping_Room_to_Chessboard_to_Unlock_LLM_As_Low-Level_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_R2C_Mapping_Room_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Ziyi and Li, Hanxuan and Fu, Bin and Xiong, Chuyan and Wang, Ruiping and Chen, Xilin},
    title     = {R2C: Mapping Room to Chessboard to Unlock LLM As Low-Level Action Planner},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19456-19466}
}

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

affiliations:
["Key Laboratory of AI Safety of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS", "University of Chinese Academy of Sciences"]


title: ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
authors: Fernando Julio Cendra, Kai Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23734-23743
affiliations: ["Visual AI Lab, The University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cendra_ICE_Intrinsic_Concept_Extraction_from_a_Single_Image_via_Diffusion_CVPR_2025_paper.html

abstract:
The inherent ambiguity in defining visual concepts poses significant challenges for modern generative models, such as the diffusion-based Text-to-Image (T2I) models, in accurately learning concepts from a single image. Existing methods lack a systematic way to reliably extract the interpretable underlying intrinsic concepts. To address this challenge, we present ICE, short for Intrinsic Concept Extraction, a novel framework that exclusively utilizes a T2I model to automatically and systematically extract intrinsic concepts from a single image. ICE consists of two pivotal stages. In the first stage, ICE devises an automatic concept localization module to pinpoint relevant text-based concepts and their corresponding masks within the image. This critical stage streamlines concept initialization and provides precise guidance for subsequent analysis. The second stage delves deeper into each identified mask, decomposing the object-level concepts into intrinsic concepts and general concepts. This decomposition allows for a more granular and interpretable breakdown of visual elements. Our framework demonstrates superior performance on intrinsic concept extraction from a single image in an unsupervised manner. Project page: https://visual-ai.github.io/ice

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cendra_ICE_Intrinsic_Concept_Extraction_from_a_Single_Image_via_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cendra_ICE_Intrinsic_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cendra_2025_CVPR,
    author    = {Cendra, Fernando Julio and Han, Kai},
    title     = {ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23734-23743}
}

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


title: ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30829-30838
affiliations: ["Vanderbilt University", "Vanderbilt Univeristy Medical Center"]
cities: ["Nashville", "Nashville"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_ASIGN_An_Anatomy-aware_Spatial_Imputation_Graphic_Network_for_3D_Spatial_CVPR_2025_paper.html

authors:
Junchao Zhu, Ruining Deng, Tianyuan Yao, Juming Xiong, Chongyu Qu, Junlin Guo, Siqi Lu, Mengmeng Yin, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo

abstract:
Spatial transcriptomics (ST) is an emerging technology that enables medical computer vision scientists to automatically interpret the molecular profiles underlying morphological features. Currently, however, most deep learning-based ST analyses are limited to two-dimensional (2D) sections, which can introduce diagnostic errors due to the heterogeneity of pathological tissues across 3D sections. Expanding ST to three-dimensional (3D) volumes is challenging due to the prohibitive costs; a 2D ST acquisition already costs over 50 times more than whole slide imaging (WSI), and a full 3D volume with 10 sections can be an order of magnitude more expensive. To reduce costs, scientists have attempted to predict ST data directly from WSI without performing actual ST acquisition. However, these methods typically yield unsatisfying results. To address this, we introduce a novel problem setting: 3D ST imputation using 3D WSI histology sections combined with a single 2D ST slide. To do so, we present the Anatomy-aware Spatial Imputation Graph Network (ASIGN) for more precise, yet affordable, 3D ST modeling. The ASIGN architecture extends existing 2D spatial relationships into 3D by leveraging cross-layer overlap and similarity-based expansion. Moreover, a multi-level spatial attention graph network integrates features comprehensively across different data sources. We evaluated ASIGN on three public spatial transcriptomics datasets, with experimental results demonstrating that ASIGN achieves state-of-the-art performance on both 2D and 3D scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_ASIGN_An_Anatomy-aware_Spatial_Imputation_Graphic_Network_for_3D_Spatial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_ASIGN_An_Anatomy-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Junchao and Deng, Ruining and Yao, Tianyuan and Xiong, Juming and Qu, Chongyu and Guo, Junlin and Lu, Siqi and Yin, Mengmeng and Wang, Yu and Zhao, Shilin and Yang, Haichun and Huo, Yuankai},
    title     = {ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30829-30838}
}

raw_affiliation:
[{"affiliation": "Vanderbilt University", "city": "Nashville", "country": "USA"}, {"affiliation": "Vanderbilt Univeristy Medical Center", "city": "Nashville", "country": "USA"}]


title: Reversing Flow for Image Restoration
authors: Haina Qin, Wenyang Luo, Libin Wang, Dandan Zheng, Jingdong Chen, Ming Yang, Bing Li, Weiming Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7545-7558
cities: ["Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Reversing_Flow_for_Image_Restoration_CVPR_2025_paper.html

abstract:
Image restoration aims to recover high-quality (HQ) images from degraded low-quality (LQ) ones by reversing the effects of degradation. Existing generative models for image restoration, including diffusion and score-based models, often treat the degradation process as a stochastic transformation, which introduces inefficiency and complexity. In this work, we propose ResFlow, a novel image restoration framework that models the degradation process as a deterministic path using continuous normalizing flows. ResFlow augments the degradation process with an auxiliary process that disambiguates the uncertainty in HQ prediction to enable reversible modeling of the degradation process. ResFlow adopts entropy-preserving flow paths and learns the augmented degradation flow by matching the velocity field. ResFlow significantly improves the performance and speed of image restoration, completing the task in fewer than four sampling steps. Extensive experiments demonstrate that ResFlow achieves state-of-the-art results across various image restoration benchmarks, offering a practical and efficient solution for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Reversing_Flow_for_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_Reversing_Flow_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Haina and Luo, Wenyang and Wang, Libin and Zheng, Dandan and Chen, Jingdong and Yang, Ming and Li, Bing and Hu, Weiming},
    title     = {Reversing Flow for Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7545-7558}
}

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

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


title: Shadow Generation Using Diffusion Model with Geometry Prior
authors: Haonan Zhao, Qingyang Liu, Xinhao Tao, Li Niu, Guangtao Zhai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7603-7612
affiliations: ["Shanghai Jiao Tong University", "miguo.ai"]
cities: ["Shanghai", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Shadow_Generation_Using_Diffusion_Model_with_Geometry_Prior_CVPR_2025_paper.html

abstract:
Image composition involves integrating foreground object into background image to obtain a composite image. One of the key challenges is to produce realistic shadow for the inserted foreground object. Recently, diffusion-based methods have shown superior performance compared to GAN-based methods in shadow generation. However, they are still struggling to generate shadows with plausible geometry in complex cases. In this paper, we focus on promoting diffusion-based methods by leveraging geometry priors. Specifically, we first predict the rotated bounding box and matched shadow shapes for the foreground shadow. Then, the geometry information of rotated bounding box and matched shadow shapes is injected into ControlNet to facilitate shadow generation. Extensive experiments on both DESOBAv2 dataset and real composite images validate the effectiveness of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Shadow_Generation_Using_Diffusion_Model_with_Geometry_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Shadow_Generation_Using_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Haonan and Liu, Qingyang and Tao, Xinhao and Niu, Li and Zhai, Guangtao},
    title     = {Shadow Generation Using Diffusion Model with Geometry Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7603-7612}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "miguo.ai", "city": "Unknown", "country": "Unknown"}]


authors: Chen-Chen Zong, Sheng-Jun Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10153-10162
affiliations: ["Nanjing University of Aeronautics and Astronautics"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zong_Rethinking_Epistemic_and_Aleatoric_Uncertainty_for_Active_Open-Set_Annotation_An_CVPR_2025_paper.html

title:
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach

abstract:
Active learning (AL), which iteratively queries the most informative examples from a large pool of unlabeled candidates for model training, faces significant challenges in the presence of open-set classes. Existing methods either prioritize query examples likely to belong to known classes, indicating low epistemic uncertainty (EU), or focus on querying those with highly uncertain predictions, reflecting high aleatoric uncertainty (AU). However, they both yield suboptimal performance, as low EU corresponds to limited useful information, and closed-set AU metrics for unknown class examples are less meaningful. In this paper, we propose an Energy-based Active Open-set Annotation (EAOA) framework, which effectively integrates EU and AU to achieve superior performance. EAOA features a (C+1)-class detector and a target classifier, incorporating an energy-based EU measure and a margin-based energy loss designed for the detector, alongside an energy-based AU measure for the target classifier. Another crucial component is the target-driven adaptive sampling strategy. It first forms a smaller candidate set with low EU scores to ensure closed-set properties, making AU metrics meaningful. Subsequently, examples with high AU scores are queried to form the final query set, with the candidate set size adjusted adaptively. Extensive experiments show that EAOA achieves state-of-the-art performance while maintaining high query precision and low training overhead. The code is available at https://github.com/chenchenzong/EAOA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zong_Rethinking_Epistemic_and_Aleatoric_Uncertainty_for_Active_Open-Set_Annotation_An_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zong_Rethinking_Epistemic_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zong_2025_CVPR,
    author    = {Zong, Chen-Chen and Huang, Sheng-Jun},
    title     = {Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10153-10162}
}

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


title: Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking
authors: Phuc Nguyen, Minh Luu, Anh Tran, Cuong Pham, Khoi Nguyen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3636-3645
affiliations: ["MovianAI", "Qualcomm AI Research"]
cities: ["Unknown", "Unknown"]
countries: ["Unknown", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_Any3DIS_Class-Agnostic_3D_Instance_Segmentation_by_2D_Mask_Tracking_CVPR_2025_paper.html

abstract:
Existing 3D instance segmentation methods frequently encounter issues with over-segmentation, leading to redundant and inaccurate 3D proposals that complicate downstream tasks. This challenge arises from their unsupervised merging approach, where dense 2D instance masks are lifted across frames into point clouds to form 3D candidate proposals without direct supervision. These candidates are then hierarchically merged based on heuristic criteria, often resulting in numerous redundant segments that fail to combine into precise 3D proposals. To overcome these limitations, we propose a 3D-Aware 2D Mask Tracking module that uses robust 3D priors from a 2D mask segmentation and tracking foundation model (SAM-2) to ensure consistent object masks across video frames. Rather than merging all visible superpoints across views to create a 3D mask, our 3D Mask Optimization module leverages a dynamic programming algorithm to select an optimal set of views, refining the superpoints to produce a final 3D proposal for each object. Our approach achieves comprehensive object coverage within the scene, significantly reduces unnecessary proposals by half, and lowers the average runtime by a factor of 10, mitigating potential negative impacts on downstream applications. Evaluations on ScanNet200 and ScanNet++ confirm the effectiveness of our method, with improvements across Class-Agnostic, Open-Vocabulary, and Open-Ended 3D Instance Segmentation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_Any3DIS_Class-Agnostic_3D_Instance_Segmentation_by_2D_Mask_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_Any3DIS_Class-Agnostic_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Phuc and Luu, Minh and Tran, Anh and Pham, Cuong and Nguyen, Khoi},
    title     = {Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3636-3645}
}

raw_affiliation:
[{"affiliation": "MovianAI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Qualcomm AI Research", "city": "Unknown", "country": "Unknown"}]


title: FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing
authors: Yufan Ren, Zicong Jiang, Tong Zhang, Søren Forchhammer, Sabine Süsstrunk
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2651-2660
cities: ["Lausanne", "Kongens Lyngby", "Gothenburg"]
countries: ["Switzerland", "Denmark", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_FDS_Frequency-Aware_Denoising_Score_for_Text-Guided_Latent_Diffusion_Image_Editing_CVPR_2025_paper.html

abstract:
Text-guided image editing using Text-to-Image (T2I) models often fails to yield satisfactory results, frequently introducing unintended modifications, such as the loss of local detail and color changes. In this paper, we analyze these failure cases and attribute them to the indiscriminate optimization across all frequency bands, even though only specific frequencies may require adjustment. To address this, we introduce a simple yet effective approach that enables the selective optimization of specific frequency bands within localized spatial regions for precise edits. Our method leverages wavelets to decompose images into different spatial resolutions across multiple frequency bands, enabling precise modifications at various levels of detail. To extend the applicability of our approach, we provide a comparative analysis of different frequency-domain techniques. Additionally, we extend our method to 3D texture editing by performing frequency decomposition on the triplane representation, enabling frequency-aware adjustments for 3D textures. Quantitative evaluations and user studies demonstrate the effectiveness of our method in producing high-quality and precise edits.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_FDS_Frequency-Aware_Denoising_Score_for_Text-Guided_Latent_Diffusion_Image_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_FDS_Frequency-Aware_Denoising_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Yufan and Jiang, Zicong and Zhang, Tong and Forchhammer, S{\o}ren and S\"usstrunk, Sabine},
    title     = {FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2651-2660}
}

raw_affiliation:
[{"affiliation": "School of Computer and Communication Sciences, EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Department of Electrical and Photonics Engineering, DTU", "city": "Kongens Lyngby", "country": "Denmark"}, {"affiliation": "Department of Electrical Engineering, Chalmers University of Technology", "city": "Gothenburg", "country": "Sweden"}]

affiliations:
["School of Computer and Communication Sciences, EPFL", "Department of Electrical and Photonics Engineering, DTU", "Department of Electrical Engineering, Chalmers University of Technology"]


title: MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling
authors: Jian Yang, Dacheng Yin, Yizhou Zhou, Fengyun Rao, Wei Zhai, Yang Cao, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7974-7985
cities: ["Hefei", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_MMAR_Towards_Lossless_Multi-Modal_Auto-Regressive_Probabilistic_Modeling_CVPR_2025_paper.html

abstract:
Recent advancements in multi-modal large language models have propelled the development of joint probabilistic models capable of both image understanding and generation. However, we have identified that recent methods suffer from loss of image information during understanding task, due to either image discretization or diffusion denoising steps. To address this issue, we propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework. Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss in an efficient way. Differing from diffusion-based approaches, we disentangle the diffusion process from auto-regressive backbone model by employing a light-weight diffusion head on top each auto-regressed image patch embedding. In this way, when the model transits from image generation to understanding through text generation, the backbone model's hidden representation of the image is not limited to the last denoising step. To successfully train our method, we also propose a theoretically proven technique that addresses the numerical stability issue and a training strategy that balances the generation and understanding task goals. Extensive evaluations on 18 image understanding benchmarks show that MMAR significantly outperforms most of the existing joint multi-modal models, surpassing the method that employs pre-trained CLIP vision encoder. Meanwhile, MMAR is able to generate high quality images. We also show that our method is scalable with larger data and model size.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_MMAR_Towards_Lossless_Multi-Modal_Auto-Regressive_Probabilistic_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_MMAR_Towards_Lossless_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jian and Yin, Dacheng and Zhou, Yizhou and Rao, Fengyun and Zhai, Wei and Cao, Yang and Zha, Zheng-Jun},
    title     = {MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7974-7985}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "WeChat Vision, Tencent Inc.", "city": "Shenzhen", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "WeChat Vision, Tencent Inc."]


title: ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object
authors: Zhe Shan, Yang Liu, Lei Zhou, Cheng Yan, Heng Wang, Xia Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3625-3635
affiliations: ["Hainan University", "Zhejiang University", "Tianjin University"]
cities: ["Haikou", "Hangzhou", "Tianjin"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shan_ROS-SAM_High-Quality_Interactive_Segmentation_for_Remote_Sensing_Moving_Object_CVPR_2025_paper.html

abstract:
The availability of large-scale remote sensing video data underscores the importance of high-quality interactive segmentation. However, challenges such as small object sizes, ambiguous features, and limited generalization make it difficult for current methods to achieve this goal. In this work, we propose ROS-SAM, a method designed to achieve high-quality interactive segmentation while preserving generalization across diverse remote sensing data. The ROS-SAM is built upon three key innovations: 1) LoRA-based fine-tuning, which enables efficient domain adaptation while maintaining SAM's generalization ability, 2) Enhancement of network deep layers to improve the discriminability of extracted features, thereby reducing misclassifications, and 3) Integration of global context with local boundary details in the mask decoder to generate high-quality segmentation masks. Additionally, we redesign the data pipeline to ensure the model learns to better handle objects at varying scales during training while focusing on high-quality predictions during inference. Experiments on remote sensing video datasets show that the data pipeline boosts the IoU by 6%, while ROS-SAM increases the IoU by 13%. Finally, when evaluated on existing remote sensing object tracking datasets, ROS-SAM demonstrates impressive zero-shot capabilities, generating masks that closely resemble manual annotations. These results confirm ROS-SAM as a powerful tool for fine-grained segmentation in remote sensing applications. Code is available at: https://github.com/ShanZard/ROS-SAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shan_ROS-SAM_High-Quality_Interactive_Segmentation_for_Remote_Sensing_Moving_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shan_ROS-SAM_High-Quality_Interactive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shan_2025_CVPR,
    author    = {Shan, Zhe and Liu, Yang and Zhou, Lei and Yan, Cheng and Wang, Heng and Xie, Xia},
    title     = {ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3625-3635}
}

raw_affiliation:
[{"affiliation": "Hainan University", "city": "Haikou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}]


title: MultiMorph: On-demand Atlas Construction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30906-30917
cities: ["Cambridge", "Boston"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Abulnaga_MultiMorph_On-demand_Atlas_Construction_CVPR_2025_paper.html

authors:
S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian Dalca

abstract:
We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essential for quantifying anatomical variability across populations. However, current atlas construction methods often require days to weeks of computation, thereby discouraging rapid experimentation. As a result, many scientific studies rely on suboptimal, precomputed atlases from mismatched populations, negatively impacting downstream analyses. MultiMorph addresses these challenges with a feedforward model that rapidly produces high-quality, population-specific atlases in a single forward pass for any 3D brain dataset, without any fine-tuning or optimization. MultiMorph is based on a linear group-interaction layer that aggregates and shares features within the group of input images. Further, by leveraging auxiliary synthetic data, MultiMorph generalizes to new imaging modalities and population groups at test-time. Experimentally, MultiMorph outperforms state-of-the-art optimization-based and learning-based atlas construction methods in both small and large population settings, with a 100-fold reduction in time. This makes MultiMorph an accessible framework for biomedical researchers without machine learning expertise, enabling rapid, high-quality atlas generation for diverse studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Abulnaga_MultiMorph_On-demand_Atlas_Construction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Abulnaga_MultiMorph_On-demand_Atlas_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Abulnaga_2025_CVPR,
    author    = {Abulnaga, S. Mazdak and Hoopes, Andrew and Dey, Neel and Hoffmann, Malte and Fischl, Bruce and Guttag, John and Dalca, Adrian},
    title     = {MultiMorph: On-demand Atlas Construction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30906-30917}
}

raw_affiliation:
[{"affiliation": "MIT Computer Science and Arti\ufb01cial Intelligence Laboratory", "city": "Cambridge", "country": "USA"}, {"affiliation": "Massachusetts General Hospital, Harvard Medical School", "city": "Boston", "country": "USA"}]

affiliations:
["MIT Computer Science and Arti\ufb01cial Intelligence Laboratory", "Massachusetts General Hospital, Harvard Medical School"]


title: MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism
authors: Zhixiong Nan, Xianghong Li, Jifeng Dai, Tao Xiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4703-4712
cities: ["Chongqing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nan_MI-DETR_An_Object_Detection_Model_with_Multi-time_Inquiries_Mechanism_CVPR_2025_paper.html

abstract:
Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features. However, the challenges for object detection in natural scenes (e.g., extremely-small, heavily-occluded, and confusingly mixed with the background) require an object detection model to fully utilize image features, which motivates us to propose a new decoder architecture with the parallel Multi-time Inquiries (MI) mechanism. MI mechanism is very simple, enabling object queries to parallelly perform multi-time inquiries to learn more comprehensive information from image features. Our MI based model, MI-DETR, outperforms all existing DETR-like models on COCO benchmark under different backbones and training epochs, achieving +2.3 AP and +0.6 AP improvements compared to the most representative model DINO and SOTA model Relation-DETR under ResNet-50 backbone.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nan_MI-DETR_An_Object_Detection_Model_with_Multi-time_Inquiries_Mechanism_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nan_MI-DETR_An_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nan_2025_CVPR,
    author    = {Nan, Zhixiong and Li, Xianghong and Dai, Jifeng and Xiang, Tao},
    title     = {MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4703-4712}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "Department of Electronic Engineering, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["College of Computer Science, Chongqing University", "Department of Electronic Engineering, Tsinghua University"]


title: From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling
authors: Jinhong Lin, Cheng-En Wu, Huanran Li, Jifan Zhang, Yu Hen Hu, Pedro Morgado
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20028-20038
raw_affiliation: [{"affiliation": "University of Wisconsin\u2013Madison", "city": "Madison", "country": "USA"}]
affiliations: ["University of Wisconsin\u2013Madison"]
cities: ["Madison"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_From_Prototypes_to_General_Distributions_An_Efficient_Curriculum_for_Masked_CVPR_2025_paper.html

abstract:
Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations by predicting masked portions of images from their visible regions. While this approach has shown promising results, we hypothesize that its effectiveness may be limited by optimization challenges during early training stages, where models are expected to learn complex image distributions from partial observations before developing basic visual processing capabilities. To address this limitation, we propose a prototype-driven curriculum leagrning framework that structures the learning process to progress from prototypical examples to more complex variations in the dataset. Our approach introduces a temperature-based annealing scheme that gradually expands the training distribution, enabling more stable and efficient learning trajectories. Through extensive experiments on ImageNet-1K, we demonstrate that our curriculum learning strategy significantly improves both training efficiency and representation quality while requiring substantially fewer training epochs compared to standard Masked Auto-Encoding. Our findings suggest that carefully controlling the order of training examples plays a crucial role in self-supervised visual learning, providing a practical solution to the early-stage optimization challenges in MIM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_From_Prototypes_to_General_Distributions_An_Efficient_Curriculum_for_Masked_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_From_Prototypes_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jinhong and Wu, Cheng-En and Li, Huanran and Zhang, Jifan and Hu, Yu Hen and Morgado, Pedro},
    title     = {From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20028-20038}
}


title: Synthetic Visual Genome
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9073-9086
cities: ["Seattle", "Seattle", "Stanford", "Tokyo"]
countries: ["USA", "USA", "USA", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Synthetic_Visual_Genome_CVPR_2025_paper.html

authors:
Jae Sung Park, Zixian Ma, Linjie Li, Chenhao Zheng, Cheng-Yu Hsieh, Ximing Lu, Khyathi Chandu, Quan Kong, Norimasa Kobori, Ali Farhadi, Yejin Choi, Ranjay Krishna

abstract:
Reasoning over visual relationships--spatial, functional, interactional, social, etc.--are considered to be a fundamental component of human cognition.Yet, despite the major advances in visual comprehension in multimodal language models (MLMs), precise reasoning over relationships remains a challenge. We introduce Robin: an MLM instruction-tuned with densely annotated relationships capable of constructing high-quality dense scene graphs at scale. To train ROBIN, we curate SVG, a synthetic scene graph dataset by completing the missing relations of selected objects in existing scene graphs using a teacher MLM and a carefully designed filtering process to ensure high-quality. To generate more accurate and rich scene graphs at scale for any image, we introduce SG-EDIT: a self-distillation framework where GPT-4o further refines ROBIN's predicted scene graphs by removing unlikely relations and/or suggesting relevant ones. In total, our dataset contains 146K images and 5.6M relationships for 2.6M objects. Results show that our Robin-3B model, despite being trained on less than 3 million instances, outperforms similar-size models trained on over 300 million instances on relationship understanding benchmarks, and even surpasses larger models up to 13B parameters. Notably, it achieves state-of-the-art performance in referring expression comprehension with a score of 88.2, surpassing the previous best of 87.4. Our results suggest that training on the refined scene graph data is crucial to maintaining high performance across diverse visual reasoning tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Synthetic_Visual_Genome_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Synthetic_Visual_Genome_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jae Sung and Ma, Zixian and Li, Linjie and Zheng, Chenhao and Hsieh, Cheng-Yu and Lu, Ximing and Chandu, Khyathi and Kong, Quan and Kobori, Norimasa and Farhadi, Ali and Choi, Yejin and Krishna, Ranjay},
    title     = {Synthetic Visual Genome},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9073-9086}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Allen Institute for Arti\ufb01cial Intelligence", "city": "Seattle", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Woven by Toyota", "city": "Tokyo", "country": "Japan"}]

affiliations:
["University of Washington", "Allen Institute for Arti\ufb01cial Intelligence", "Stanford University", "Woven by Toyota"]


authors: Hyunsoo Kim, Donghyun Kim, Suhyun Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18250-18259
affiliations: ["Korea University", "Korea Institute of Science and Technology", "Kyung Hee University"]
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Difference_Inversion_Interpolate_and_Isolate_the_Difference_with_Token_Consistency_CVPR_2025_paper.html

title:
Difference Inversion: Interpolate and Isolate the Difference with Token Consistency for Image Analogy Generation

abstract:
How can we generate an image B' that satisfies A:A'::B:B', given the input images A,A' and B? Recent works have tackled this challenge through approaches like visual in-context learning or visual instruction. However, these methods are typically limited to specific models (InstructPix2Pix. Inpainting models) rather than general diffusion models (Stable Diffusion, SDXL). This dependency may lead to inherited biases or lower editing capabilities. In this paper, we propose Difference Inversion, a method that isolates only the difference from A and A' and applies it to B to generate a plausible B'. To address model dependency, it is crucial to structure prompts in the form of a "Full Prompt" suitable for input to stable diffusion models, rather than using an "Instruction Prompt". To this end, we accurately extract the Difference between A and A' and combine it with the prompt of B, enabling a plug-and-play application of the difference. To extract a precise difference, we first identify it through 1) Delta Interpolation. Additionally, to ensure accurate training, we propose the 2) Token Consistency Loss and 3) Zero Initialization of Token Embeddings. Our extensive experiments demonstrate that Difference Inversion outperforms existing baselines both quantitatively and qualitatively, indicating its ability to generate more feasible B' in a model-agnostic manner.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Difference_Inversion_Interpolate_and_Isolate_the_Difference_with_Token_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Difference_Inversion_Interpolate_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Hyunsoo and Kim, Donghyun and Kim, Suhyun},
    title     = {Difference Inversion: Interpolate and Isolate the Difference with Token Consistency for Image Analogy Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18250-18259}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea Institute of Science and Technology", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Kyung Hee University", "city": "Seoul", "country": "South Korea"}]


title: Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
authors: Wei Suo, Lijun Zhang, Mengyang Sun, Lin Yuanbo Wu, Peng Wang, Yanning Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29904-29914
cities: ["Xi'an", "Ningbo", "Xi'an", "Xi'an", "Swansea"]
countries: ["China", "China", "China", "China", "United Kingdom"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Suo_Octopus_Alleviating_Hallucination_via_Dynamic_Contrastive_Decoding_CVPR_2025_paper.html

abstract:
Large Vision-Language Models (LVLMs) have obtained impressive performance in visual content understanding and multi-modal reasoning. Unfortunately, these large models suffer from serious hallucination problems and tend to generate fabricated responses. Recently, several Contrastive Decoding (CD) strategies have been proposed to alleviate hallucination by introducing disturbed inputs. Although great progress has been made, these CD strategies mostly apply a one-size-fits-all approach for all input conditions. In this paper, we revisit this process through extensive experiments. Related results show that hallucination causes are hybrid and each generative step faces a unique hallucination challenge. Leveraging these meaningful insights, we introduce a simple yet effective Octopus-like framework that enables the model to adaptively identify hallucination types and create a dynamic CD workflow. Our Octopus framework not only outperforms existing methods across four benchmarks but also demonstrates excellent deployability and expansibility. Code is available at https://github.com/LijunZhang01/Octopus.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Suo_Octopus_Alleviating_Hallucination_via_Dynamic_Contrastive_Decoding_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Suo_2025_CVPR,
    author    = {Suo, Wei and Zhang, Lijun and Sun, Mengyang and Wu, Lin Yuanbo and Wang, Peng and Zhang, Yanning},
    title     = {Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29904-29914}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ningbo Institute, Northwestern Polytechnical University", "city": "Ningbo", "country": "China"}, {"affiliation": "School of Cybersecurity, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean", "city": "Xi'an", "country": "China"}, {"affiliation": "Department of Computer Science, Swansea University", "city": "Swansea", "country": "United Kingdom"}]

affiliations:
["School of Computer Science, Northwestern Polytechnical University", "Ningbo Institute, Northwestern Polytechnical University", "School of Cybersecurity, Northwestern Polytechnical University", "National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean", "Department of Computer Science, Swansea University"]


title: MTADiffusion: Mask Text Alignment Diffusion Model for Object Inpainting
authors: Jun Huang, Ting Liu, Yihang Wu, Xiaochao Qu, Luoqi Liu, Xiaolin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18325-18334
cities: ["Beijing", "Singapore", "Beijing"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_MTADiffusion_Mask_Text_Alignment_Diffusion_Model_for_Object_Inpainting_CVPR_2025_paper.html

abstract:
Advancements in generative models have enabled image inpainting models to generate content within specific regions of an image based on provided prompts and masks. However, existing inpainting methods often suffer from problems such as semantic misalignment, structural distortion, and style inconsistency. In this work, we present MTADiffusion, a Mask-Text Alignment diffusion model designed for object inpainting. To enhance the semantic capabilities of the inpainting model, we introduce MTAPipeline, an automatic solution for annotating masks with detailed descriptions. Based on the MTAPipeline, we construct a new MTADataset comprising 5 million images and 25 million mask-text pairs. Furthermore, we propose a multi-task training strategy that integrates both inpainting and edge prediction tasks to improve structural stability. To promote style consistency, we present a novel inpainting style-consistency loss using a pre-trained VGG network and the Gram matrix. Comprehensive evaluations on BrushBench and EditBench demonstrate that MTADiffusion achieves state-of-the-art performance compared to other methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_MTADiffusion_Mask_Text_Alignment_Diffusion_Model_for_Object_Inpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_MTADiffusion_Mask_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Jun and Liu, Ting and Wu, Yihang and Qu, Xiaochao and Liu, Luoqi and Hu, Xiaolin},
    title     = {MTADiffusion: Mask Text Alignment Diffusion Model for Object Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18325-18334}
}

raw_affiliation:
[{"affiliation": "MT Lab, Meitu Inc", "city": "Beijing", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["MT Lab, Meitu Inc", "National University of Singapore", "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University"]


title: Stop Learning it all to Mitigate Visual Hallucination, Focus on the Hallucination Target.
authors: Dokyoon Yoon, Youngsook Song, Woomyoung Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4200-4208
raw_affiliation: [{"affiliation": "SIONIC AI", "city": "Seoul", "country": "South Korea"}]
affiliations: ["SIONIC AI"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yoon_Stop_Learning_it_all_to_Mitigate_Visual_Hallucination_Focus_on_CVPR_2025_paper.html

abstract:
Multimodal Large Language Models (MLLMs) frequently suffer from hallucination issues, generating information about objects that are not present in input images during vision-language tasks. These hallucinations particularly undermine model reliability in practical applications requiring accurate object identification. To address this challenge, we propose TL-DPO, a preference learning approach that mitigates hallucinations by focusing on targeted areas where they occur. To implement this, we build a dataset containing hallucinated responses, correct responses, and target information (i.e., objects present in the images and the corresponding chunk positions in responses affected by hallucinations). By applying a preference learning method restricted to these specific targets, the model can filter out irrelevant signals and focus on correcting hallucinations. This allows the model to produce more factual responses by concentrating solely on relevant information. Experimental results demonstrate that TL-DPO effectively reduces hallucinations across multiple vision hallucination tasks, improving the reliability and performance of MLLMs without diminishing overall performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yoon_Stop_Learning_it_all_to_Mitigate_Visual_Hallucination_Focus_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yoon_Stop_Learning_it_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yoon_2025_CVPR,
    author    = {Yoon, Dokyoon and Song, Youngsook and Park, Woomyoung},
    title     = {Stop Learning it all to Mitigate Visual Hallucination, Focus on the Hallucination Target.},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4200-4208}
}


title: Seeing the Abstract: Translating the Abstract Language for Vision Language Models
authors: Davide Talon, Federico Girella, Ziyue Liu, Marco Cristani, Yiming Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9253-9262
raw_affiliation: [{"affiliation": "string", "city": "string", "country": "string"}]
affiliations: ["string"]
cities: ["string"]
countries: ["string"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Talon_Seeing_the_Abstract_Translating_the_Abstract_Language_for_Vision_Language_CVPR_2025_paper.html

abstract:
Natural language goes beyond dryly describing visual content. It contains rich abstract concepts to express feeling, creativity and properties that cannot be directly perceived. Yet, current research in Vision Language Models (VLMs) has not shed light on abstract-oriented language.Our research breaks new ground by uncovering its wide presence and under-estimated value, with extensive analysis.Particularly, we focus our investigation on the fashion domain, a highly-representative field with abstract expressions. By analyzing recent large-scale multimodal fashion datasets, we find that abstract terms have a dominant presence, rivaling the concrete ones, providing novel information, and being useful in the retrieval task. However, a critical challenge emerges: current general-purpose or fashion-specific VLMs are pre-trained with databases that lack sufficient abstract words in their text corpora, thus hindering their ability to effectively represent abstract-oriented language. We propose a training-free and model-agnostic method, Abstract-to-Concrete Translator (ACT), to shift abstract representations towards well-represented concrete ones in the VLM latent space, using pre-trained models and existing multimodal databases.On the text-to-image retrieval task, despite being training-free, ACT outperforms the fine-tuned VLMs in both same- and cross-dataset settings, exhibiting its effectiveness with a strong generalization capability. Moreover, the improvement introduced by ACT is consistent with various VLMs, making it a plug-and-play solution.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Talon_Seeing_the_Abstract_Translating_the_Abstract_Language_for_Vision_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Talon_Seeing_the_Abstract_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Talon_2025_CVPR,
    author    = {Talon, Davide and Girella, Federico and Liu, Ziyue and Cristani, Marco and Wang, Yiming},
    title     = {Seeing the Abstract: Translating the Abstract Language for Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9253-9262}
}


title: Spiking Transformer: Introducing Accurate Addition-Only Spiking Self-Attention for Transformer
authors: Yufei Guo, Xiaode Liu, Yuanpei Chen, Weihang Peng, Yuhan Zhang, Zhe Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24398-24408
affiliations: ["Intelligent Science & Technology Academy of CASIC, China"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Spiking_Transformer_Introducing_Accurate_Addition-Only_Spiking_Self-Attention_for_Transformer_CVPR_2025_paper.html

abstract:
Transformers have demonstrated outstanding performance across a wide range of tasks, owing to their self-attention mechanism, but they are highly energy-consuming. Spiking Neural Networks have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks, leveraging event-driven computation and binary spikes for information transfer. The combination of Transformers' capabilities with the energy efficiency of SNNs offers a compelling opportunity. This paper addresses the challenge of adapting the self-attention mechanism of Transformers to the spiking paradigm by introducing a novel approach: Accurate Addition-Only Spiking Self-Attention (A^2OS^2A). Unlike existing methods that rely solely on binary spiking neurons for all components of the self-attention mechanism, our approach integrates binary, ReLU, and ternary spiking neurons. This hybrid strategy significantly improves accuracy while preserving non-multiplicative computations. Moreover, our method eliminates the need for softmax and scaling operations. Extensive experiments show that the A^2OS^2A-based Spiking Transformer outperforms existing SNN-based Transformers on several datasets, even achieving an accuracy of 78.66% on ImageNet-1K. Our work represents a significant advancement in SNN-based Transformer models, offering a more accurate and efficient solution for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Spiking_Transformer_Introducing_Accurate_Addition-Only_Spiking_Self-Attention_for_Transformer_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yufei and Liu, Xiaode and Chen, Yuanpei and Peng, Weihang and Zhang, Yuhan and Ma, Zhe},
    title     = {Spiking Transformer: Introducing Accurate Addition-Only Spiking Self-Attention for Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24398-24408}
}

raw_affiliation:
[{"affiliation": "Intelligent Science & Technology Academy of CASIC, China", "city": "Beijing", "country": "China"}]


title: Grounding 3D Object Affordance with Language Instructions, Visual Observations and Interactions
authors: He Zhu, Quyu Kong, Kechun Xu, Xunlong Xia, Bing Deng, Jieping Ye, Rong Xiong, Yue Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17337-17346
affiliations: ["Zhejiang University", "Alibaba Cloud"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Grounding_3D_Object_Affordance_with_Language_Instructions_Visual_Observations_and_CVPR_2025_paper.html

abstract:
Grounding 3D object affordance is a task that locates objects in 3D space where they can be manipulated, which links perception and action for embodied intelligence. For example, for an intelligent robot, it is necessary to accurately ground the affordance of an object and grasp it according to human instructions. In this paper, we introduce a novel task that grounds 3D object affordance based on language instructions, visual observations and interactions, which is inspired by cognitive science. We collect an Affordance Grounding dataset with Points, Images and Language instructions (AGPIL) to support the proposed task. In the 3D physical world, due to observation orientation, object rotation, or spatial occlusion, we can only get a partial observation of the object. So this dataset includes affordance estimations of objects from full-view, partial-view, and rotation-view perspectives. To accomplish this task, we propose LMAffordance3D, the first multi-modal, language-guided 3D affordance grounding network, which applies a vision-language model to fuse 2D and 3D spatial features with semantic features. Comprehensive experiments on AGPIL demonstrate the effectiveness and superiority of our method on this task, even in unseen experimental settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Grounding_3D_Object_Affordance_with_Language_Instructions_Visual_Observations_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Grounding_3D_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, He and Kong, Quyu and Xu, Kechun and Xia, Xunlong and Deng, Bing and Ye, Jieping and Xiong, Rong and Wang, Yue},
    title     = {Grounding 3D Object Affordance with Language Instructions, Visual Observations and Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17337-17346}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Alibaba Cloud", "city": "Hangzhou", "country": "China"}]


title: MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes
authors: Ruijie Lu, Yixin Chen, Junfeng Ni, Baoxiong Jia, Yu Liu, Diwen Wan, Gang Zeng, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26767-26778
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_MOVIS_Enhancing_Multi-Object_Novel_View_Synthesis_for_Indoor_Scenes_CVPR_2025_paper.html

abstract:
Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to compositional multi-object scenarios yields inferior results, especially incorrect object placement and inconsistent shape and appearance under novel views. How to enhance and systematically evaluate the cross-view consistency of such models remains under-explored. To address this issue, we propose MOVIS to enhance the structural awareness of the view-conditioned diffusion model for multi-object NVS in terms of model inputs, auxiliary tasks, and training strategy. First, we inject structure-aware features, including depth and object mask, into the denoising U-Net to enhance the model's comprehension of object instances and their spatial relationships. Second, we introduce an auxiliary task requiring the model to simultaneously predict novel view object masks, further improving the model's capability in differentiating and placing objects. Finally, we conduct an in-depth analysis of the diffusion sampling process and carefully devise a structure-guided timestep sampling scheduler during training, which balances the learning of global object placement and fine-grained detail recovery. To systematically evaluate the plausibility of synthesized images, we propose to assess cross-view consistency and novel view object placement alongside existing image-level NVS metrics. Extensive experiments on challenging synthetic and realistic datasets demonstrate that our method exhibits strong generalization capabilities and produces consistent novel view synthesis, highlighting its potential to guide future 3D-aware multi-object NVS tasks. Our project page is available at https://jason-aplp.github.io/MOVIS/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_MOVIS_Enhancing_Multi-Object_Novel_View_Synthesis_for_Indoor_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_MOVIS_Enhancing_Multi-Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Ruijie and Chen, Yixin and Ni, Junfeng and Jia, Baoxiong and Liu, Yu and Wan, Diwen and Zeng, Gang and Huang, Siyuan},
    title     = {MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26767-26778}
}

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

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


title: One-Step Event-Driven High-Speed Autofocus
authors: Yuhan Bao, Shaohua Gao, Wenyong Li, Kaiwei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6222-6230
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bao_One-Step_Event-Driven_High-Speed_Autofocus_CVPR_2025_paper.html

abstract:
High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in "focus hunting". Event-driven methods have advanced focusing speed and improved performance in low-light conditions; however, current approaches still require at least one lengthy round of "focus hunting", involving the collection of a complete focus stack. We introduce the Event Laplacian Product (ELP) focus detection function, which combines event data with grayscale Laplacian information, redefining focus search as a detection task. This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset. Additionally, we present an autofocus pipeline tailored for event-only cameras, achieving accurate results across a range of challenging motion and lighting conditions. All datasets and code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bao_One-Step_Event-Driven_High-Speed_Autofocus_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bao_One-Step_Event-Driven_High-Speed_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bao_2025_CVPR,
    author    = {Bao, Yuhan and Gao, Shaohua and Li, Wenyong and Wang, Kaiwei},
    title     = {One-Step Event-Driven High-Speed Autofocus},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6222-6230}
}


title: Symbolic Representation for Any-to-Any Generative Tasks
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27816-27826
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Symbolic_Representation_for_Any-to-Any_Generative_Tasks_CVPR_2025_paper.html

authors:
Jiaqi Chen, Xiaoye Zhu, Yue Wang, Tianyang Liu, Xinhui Chen, Ying Chen, Chak Tou Leong, Yifei Ke, Joseph Liu, Yiwen Yuan, Julian McAuley, Li-jia Li

abstract:
We propose a symbolic generative task description language and a corresponding inference engine that can represent arbitrary multimodal tasks as structured symbolic flows. Unlike conventional generative models, which rely on large-scale training and implicit neural representations to learn cross-modal mappings--often with high computational costs and limited flexibility--our framework introduces an explicit symbolic representation composed of three core primitives: functions, parameters, and topological logic. Using a pre-trained language model, our inference engine maps natural language instructions directly to symbolic workflows in a training-free manner. Our framework successfully performs over 12 diverse multimodal generative tasks, demonstrating strong performance and flexibility without requiring task-specific tuning. Experiments show that our method not only matches or outperforms existing state-of-the-art unified models in content quality but also offers greater efficiency, editability, and interruptibility. We believe symbolic task representations provide a cost-effective and extensible foundation for advancing the capabilities of generative AI.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Symbolic_Representation_for_Any-to-Any_Generative_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Symbolic_Representation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jiaqi and Zhu, Xiaoye and Wang, Yue and Liu, Tianyang and Chen, Xinhui and Chen, Ying and Leong, Chak Tou and Ke, Yifei and Liu, Joseph and Yuan, Yiwen and McAuley, Julian and Li, Li-jia},
    title     = {Symbolic Representation for Any-to-Any Generative Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27816-27826}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Fellou AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "Fenz.AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "University of Illinois at Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "LiveX AI", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Stanford University", "Fellou AI", "Fudan University", "South China University of Technology", "Cornell University", "University of California San Diego", "Fenz.AI", "Wuhan University", "University of Illinois at Urbana-Champaign", "Hong Kong Polytechnic University", "University of Southern California", "Carnegie Mellon University", "LiveX AI"]

cities:
["Stanford", "Unknown", "Shanghai", "Guangzhou", "Ithaca", "San Diego", "Unknown", "Wuhan", "Urbana-Champaign", "Hong Kong", "Los Angeles", "Pittsburgh", "Unknown"]

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


title: Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations
authors: Haitong Liu, Kuofeng Gao, Yang Bai, Jinmin Li, Jinxiao Shan, Tao Dai, Shu-Tao Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24056-24065
cities: ["Beijing", "Beijing", "Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Protecting_Your_Video_Content_Disrupting_Automated_Video-based_LLM_Annotations_CVPR_2025_paper.html

abstract:
Recently, video-based large language models (video-based LLMs) have achieved impressive performance across various video comprehension tasks. However, this rapid advancement raises significant privacy and security concerns, particularly regarding the unauthorized use of personal video data in automated annotation by video-based LLMs. These unauthorized annotated video-text pairs can then be used to improve the performance of downstream tasks, such as text-to-video generation. To safeguard personal videos from unauthorized use, we propose two series of protective video watermarks with imperceptible adversarial perturbations, named Ramblings and Mutes. Concretely, Ramblings aim to mislead video-based LLMs into generating inaccurate captions for the videos, thereby degrading the quality of video annotations through inconsistencies between video content and captions. Mutes, on the other hand, are designed to prompt video-based LLMs to produce exceptionally brief captions, lacking descriptive detail. Extensive experiments demonstrate that our video watermarking methods effectively protect video data by significantly reducing video annotation performance across various video-based LLMs, showcasing both stealthiness and robustness in protecting personal video content. Our code is available at https://github.com/ttthhl/Protecting_Your_Video_Content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Protecting_Your_Video_Content_Disrupting_Automated_Video-based_LLM_Annotations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Protecting_Your_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Haitong and Gao, Kuofeng and Bai, Yang and Li, Jinmin and Shan, Jinxiao and Dai, Tao and Xia, Shu-Tao},
    title     = {Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24056-24065}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "Digital Center, China Merchants Group Limited", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Tsinghua University", "ByteDance", "Digital Center, China Merchants Group Limited", "Shenzhen University", "Peng Cheng Laboratory"]


