CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id stringlengths 29 139 | title stringlengths 8 154 | authors listlengths 1 40 | cvf_url stringlengths 86 196 | pdf_url stringlengths 87 197 | supp_url stringlengths 98 147 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 4
values | bibtex large_stringlengths 308 1.06k | abstract large_stringlengths 562 2.77k |
|---|---|---|---|---|---|---|---|---|---|
Xiao_Generalizable_Structure-Aware_Keypoint_Correspondence_for_Category-Unified_3D_Single_Object_Tracking_CVPR_2026_paper | Generalizable Structure-Aware Keypoint Correspondence for Category-Unified 3D Single Object Tracking | [
"Jie Xiao",
"Yinchao Ma",
"Yuyang Tang",
"Dengqing Yang",
"Jianpeng Yang",
"Xu Zhou",
"Qiao Li",
"Wenfei Yang",
"Tianzhu Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xiao_Generalizable_Structure-Aware_Keypoint_Correspondence_for_Category-Unified_3D_Single_Object_Tracking_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xiao_Generalizable_Structure-Aware_Keypoint_Correspondence_for_Category-Unified_3D_Single_Object_Tracking_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xiao_Generalizable_Structure-Aware_Keypoint_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Xiao_2026_CVPR,
author = {Xiao, Jie and Ma, Yinchao and Tang, Yuyang and Yang, Dengqing and Yang, Jianpeng and Zhou, Xu and Li, Qiao and Yang, Wenfei and Zhang, Tianzhu},
title = {Generalizable Structure-Aware Keypoint Correspondence for Category-Unified 3D Single Object Tracking},
boo... | 3D single object tracking (SOT) in point clouds is essential for real-world 3D perception, yet it remains challenging due to data sparsity and large variations in scale and structure across diverse object categories. Most existing methods rely on a category-specific paradigm that trains separate models for each class, ... |
Yang_DirectFisheye-GS_Enabling_Native_Fisheye_Input_in_Gaussian_Splatting_with_Cross-View_CVPR_2026_paper | DirectFisheye-GS: Enabling Native Fisheye Input in Gaussian Splatting with Cross-View Joint Optimization | [
"Zhengxian Yang",
"Fei Xie",
"Xutao Xue",
"Rui Zhang",
"Taicheng Huang",
"Yang Liu",
"Mengqi Ji",
"Tao Yu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Yang_DirectFisheye-GS_Enabling_Native_Fisheye_Input_in_Gaussian_Splatting_with_Cross-View_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yang_DirectFisheye-GS_Enabling_Native_Fisheye_Input_in_Gaussian_Splatting_with_Cross-View_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Yang_DirectFisheye-GS_Enabling_Native_CVPR_2026_supplemental.zip | 2604.00648 | cvf | @InProceedings{Yang_2026_CVPR,
author = {Yang, Zhengxian and Xie, Fei and Xue, Xutao and Zhang, Rui and Huang, Taicheng and Liu, Yang and Ji, Mengqi and Yu, Tao},
title = {DirectFisheye-GS: Enabling Native Fisheye Input in Gaussian Splatting with Cross-View Joint Optimization},
booktitle = {Proceedin... | 3D Gaussian Splatting (3DGS) has enabled efficient 3D scene reconstruction from everyday images with real-time, high-fidelity rendering, greatly advancing VR/AR applications. Fisheye cameras, with their wider field of view (FOV), promise high-quality reconstructions from fewer inputs and have recently attracted much at... |
Jia_CompBench_Benchmarking_Complex_Instruction-guided_Image_Editing_CVPR_2026_paper | CompBench: Benchmarking Complex Instruction-guided Image Editing | [
"Bohan Jia",
"Wenxuan Huang",
"Yuntian Tang",
"Junbo Qiao",
"Jincheng Liao",
"Shaosheng Cao",
"Fei Zhao",
"Zhaopeng Feng",
"Zhouhong Gu",
"Zhenfei Yin",
"Lei Bai",
"Wanli Ouyang",
"Lin Chen",
"Fei Zhao",
"Zihan Wang",
"Yuan Xie",
"Shaohui Lin"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Jia_CompBench_Benchmarking_Complex_Instruction-guided_Image_Editing_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jia_CompBench_Benchmarking_Complex_Instruction-guided_Image_Editing_CVPR_2026_paper.pdf | null | 2505.12200 | cvf | @InProceedings{Jia_2026_CVPR,
author = {Jia, Bohan and Huang, Wenxuan and Tang, Yuntian and Qiao, Junbo and Liao, Jincheng and Cao, Shaosheng and Zhao, Fei and Feng, Zhaopeng and Gu, Zhouhong and Yin, Zhenfei and Bai, Lei and Ouyang, Wanli and Chen, Lin and Zhao, Fei and Wang, Zihan and Xie, Yuan and Lin, Shaohu... | While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack comprehensive, fine-grained instructions. To bridge this gap, we introduce CompBench, a large-scale benchmark specifically designed for complex... |
Lyu_Choreographing_a_World_of_Dynamic_Objects_CVPR_2026_paper | Choreographing a World of Dynamic Objects | [
"Yanzhe Lyu",
"Chen Geng",
"Karthik Dharmarajan",
"Yunzhi Zhang",
"Hadi Alzayer",
"Shangzhe Wu",
"Jiajun Wu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lyu_Choreographing_a_World_of_Dynamic_Objects_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lyu_Choreographing_a_World_of_Dynamic_Objects_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lyu_Choreographing_a_World_CVPR_2026_supplemental.pdf | 2601.04194 | cvf | @InProceedings{Lyu_2026_CVPR,
author = {Lyu, Yanzhe and Geng, Chen and Dharmarajan, Karthik and Zhang, Yunzhi and Alzayer, Hadi and Wu, Shangzhe and Wu, Jiajun},
title = {Choreographing a World of Dynamic Objects},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Dynamic objects in our physical 4D (3D + time) world are constantly evolving, deforming, and interacting with other objects, leading to diverse 4D scene dynamics. In this paper, we present a universal generative pipeline, CHORD, for CHOReographing Dynamic objects and scenes and synthesizing this type of phenomena. Trad... |
Wang_Spk2VidNet_A_Hierarchical_Recurrent_Architecture_for_High-Fidelity_Video_Reconstruction_from_CVPR_2026_paper | Spk2VidNet: A Hierarchical Recurrent Architecture for High-Fidelity Video Reconstruction from Long Spike-Camera Streams | [
"Yuanlin Wang",
"Ruiqin Xiong",
"Jiyu Xie",
"Zhenkun Zhu",
"Zhaofei Yu",
"Xiaopeng Fan",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wang_Spk2VidNet_A_Hierarchical_Recurrent_Architecture_for_High-Fidelity_Video_Reconstruction_from_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wang_Spk2VidNet_A_Hierarchical_Recurrent_Architecture_for_High-Fidelity_Video_Reconstruction_from_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wang_Spk2VidNet_A_Hierarchical_CVPR_2026_supplemental.zip | null | null | @InProceedings{Wang_2026_CVPR,
author = {Wang, Yuanlin and Xiong, Ruiqin and Xie, Jiyu and Zhu, Zhenkun and Yu, Zhaofei and Fan, Xiaopeng and Huang, Tiejun},
title = {Spk2VidNet: A Hierarchical Recurrent Architecture for High-Fidelity Video Reconstruction from Long Spike-Camera Streams},
booktitle = ... | Spike camera is a neuromorphic vision sensor with ultra-high temporal resolution, capable of capturing fast-moving scenes by firing a stream of binary spikes. However, its relatively low spatial resolution limits the acquisition of fine-grained visual details, motivating research on spike camera super resolution (SCSR)... |
Michel_Continual_Distillation_of_Teachers_from_Different_Domains_CVPR_2026_paper | Continual Distillation of Teachers from Different Domains | [
"Nicolas Michel",
"Maorong Wang",
"Jiangpeng He",
"Toshihiko Yamasaki"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Michel_Continual_Distillation_of_Teachers_from_Different_Domains_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Michel_Continual_Distillation_of_Teachers_from_Different_Domains_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Michel_Continual_Distillation_of_CVPR_2026_supplemental.pdf | 2605.04059 | cvf | @InProceedings{Michel_2026_CVPR,
author = {Michel, Nicolas and Wang, Maorong and He, Jiangpeng and Yamasaki, Toshihiko},
title = {Continual Distillation of Teachers from Different Domains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a student learns sequentially from a stream of teacher models without retaining access to earlier teachers. CD faces two challenges: teacher trai... |
Shekhar_GT-SVJ_Generative-Transformer-Based_Self-Supervised_Video_Judge_For_Efficient_Video_Reward_Modeling_CVPR_2026_paper | GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling | [
"Shivanshu Shekhar",
"Uttaran Bhattacharya",
"Raghavendra Addanki",
"Mehrab Tanjim",
"Somdeb Sarkhel",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shekhar_GT-SVJ_Generative-Transformer-Based_Self-Supervised_Video_Judge_For_Efficient_Video_Reward_Modeling_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shekhar_GT-SVJ_Generative-Transformer-Based_Self-Supervised_Video_Judge_For_Efficient_Video_Reward_Modeling_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shekhar_GT-SVJ_Generative-Transformer-Based_Self-Supervised_CVPR_2026_supplemental.zip | 2602.05202 | cvf | @InProceedings{Shekhar_2026_CVPR,
author = {Shekhar, Shivanshu and Bhattacharya, Uttaran and Addanki, Raghavendra and Tanjim, Mehrab and Sarkhel, Somdeb and Zhang, Tong},
title = {GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling},
booktitle = {Proce... | Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle to capture subtle temporal dynamics. We propose a fundamentally different approach: repurposing video generative models, which are inherent... |
Xu_Beyond_Euclidean_Gossip_KL-Barycentric_Consensus_on_Heterogeneous_and_Imbalanced_Images_CVPR_2026_paper | Beyond Euclidean Gossip: KL-Barycentric Consensus on Heterogeneous and Imbalanced Images | [
"Lu Xu",
"Guosheng Yin"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xu_Beyond_Euclidean_Gossip_KL-Barycentric_Consensus_on_Heterogeneous_and_Imbalanced_Images_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xu_Beyond_Euclidean_Gossip_KL-Barycentric_Consensus_on_Heterogeneous_and_Imbalanced_Images_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xu_Beyond_Euclidean_Gossip_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Xu_2026_CVPR,
author = {Xu, Lu and Yin, Guosheng},
title = {Beyond Euclidean Gossip: KL-Barycentric Consensus on Heterogeneous and Imbalanced Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Fully decentralized deep learning removes global servers and ensures local data privacy. However, Euclidean consensus, averaging weights, gradients or momentum, may degrade under the situations with non-i.i.d. (non-independent and identically distributed) data and client size imbalance. We propose a geometry-aware appr... |
Bassole_HybridDriveVLA_Vision-Language-Action_Model_with_Visual_CoT_reasoning_and_ToT_Evaluation_CVPR_2026_paper | HybridDriveVLA: Vision-Language-Action Model with Visual CoT reasoning and ToT Evaluation for Autonomous Driving | [
"Yipene Cedric Francois Bassole",
"Sungwoo Kim",
"Jiwoo Jung",
"Yunsick Sung"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Bassole_HybridDriveVLA_Vision-Language-Action_Model_with_Visual_CoT_reasoning_and_ToT_Evaluation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Bassole_HybridDriveVLA_Vision-Language-Action_Model_with_Visual_CoT_reasoning_and_ToT_Evaluation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Bassole_HybridDriveVLA_Vision-Language-Action_Model_CVPR_2026_supplemental.zip | null | null | @InProceedings{Bassole_2026_CVPR,
author = {Bassole, Yipene Cedric Francois and Kim, Sungwoo and Jung, Jiwoo and Sung, Yunsick},
title = {HybridDriveVLA: Vision-Language-Action Model with Visual CoT reasoning and ToT Evaluation for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Confere... | Vision-Language-Action (VLA) models are emerging as an important technology in autonomous driving, recognized for their sophisticated reasoning and interpretability. However, traditional VLA models often rely on image-to-text with Chain-of-Thought (CoT) reasoning, which converts sequential visual scenes into textual sy... |
Jeong_Training-free_Perceptually_Consistent_Low-Resolution_Previews_with_High-Resolution_Image_for_Efficient_CVPR_2026_paper | Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models | [
"Wongi Jeong",
"Hoigi Seo",
"Se Young Chun"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Jeong_Training-free_Perceptually_Consistent_Low-Resolution_Previews_with_High-Resolution_Image_for_Efficient_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jeong_Training-free_Perceptually_Consistent_Low-Resolution_Previews_with_High-Resolution_Image_for_Efficient_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Jeong_Training-free_Perceptually_Consistent_CVPR_2026_supplemental.pdf | 2604.09227 | cvf | @InProceedings{Jeong_2026_CVPR,
author = {Jeong, Wongi and Seo, Hoigi and Chun, Se Young},
title = {Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However, a desired outcome often requires generating a large number of HR images with different prompts and seeds, resulting in high computational co... |
Chen_Catalyst4D_High-Fidelity_3D-to-4D_Scene_Editing_via_Dynamic_Propagation_CVPR_2026_paper | Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation | [
"Shifeng Chen",
"Yihui Li",
"Jun Liao",
"Hongyu Yang",
"Di Huang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Chen_Catalyst4D_High-Fidelity_3D-to-4D_Scene_Editing_via_Dynamic_Propagation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chen_Catalyst4D_High-Fidelity_3D-to-4D_Scene_Editing_via_Dynamic_Propagation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Chen_Catalyst4D_High-Fidelity_3D-to-4D_CVPR_2026_supplemental.pdf | 2603.12766 | cvf | @InProceedings{Chen_2026_CVPR,
author = {Chen, Shifeng and Li, Yihui and Liao, Jun and Yang, Hongyu and Huang, Di},
title = {Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion artifacts, temporal flickering, and inconsistent style propagation. We introduce Cataly... |
Xia_Cloning_Deterministic_Worlds_The_Critical_Role_of_Latent_Geometry_in_CVPR_2026_paper | Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models | [
"Zaishuo Xia",
"Yukuan Lu",
"Xinyi Li",
"Yifan Xu",
"Yubei Chen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xia_Cloning_Deterministic_Worlds_The_Critical_Role_of_Latent_Geometry_in_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xia_Cloning_Deterministic_Worlds_The_Critical_Role_of_Latent_Geometry_in_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xia_Cloning_Deterministic_Worlds_CVPR_2026_supplemental.pdf | 2510.26782 | cvf | @InProceedings{Xia_2026_CVPR,
author = {Xia, Zaishuo and Lu, Yukuan and Li, Xinyi and Xu, Yifan and Chen, Yubei},
title = {Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical state of both the embodied agent and its environment. Accurate world models are essential for enabling agents to think, plan, and reason effectively in complex, dynamic settings.... |
Niu_CME-CAD_Heterogeneous_Collaborative_Multi-Expert_Reinforcement_Learning_for_CAD_Code_Generation_CVPR_2026_paper | CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation | [
"Ke Niu",
"Haiyang Yu",
"Zhuofan Chen",
"Zhengtao Yao",
"Weitao Jia",
"Xiaodong Ge",
"Jingqun Tang",
"Benlei Cui",
"Bin Li",
"Xiangyang Xue"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Niu_CME-CAD_Heterogeneous_Collaborative_Multi-Expert_Reinforcement_Learning_for_CAD_Code_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Niu_CME-CAD_Heterogeneous_Collaborative_Multi-Expert_Reinforcement_Learning_for_CAD_Code_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Niu_CME-CAD_Heterogeneous_Collaborative_CVPR_2026_supplemental.pdf | 2512.23333 | cvf | @InProceedings{Niu_2026_CVPR,
author = {Niu, Ke and Yu, Haiyang and Chen, Zhuofan and Yao, Zhengtao and Jia, Weitao and Ge, Xiaodong and Tang, Jingqun and Cui, Benlei and Li, Bin and Xue, Xiangyang},
title = {CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation... | Computer-Aided Design (CAD) is essential in industrial design, but the complexity of traditional CAD modeling and workflows presents significant challenges for automating the generation of high-precision, editable CAD models. Existing methods, such as 3D reconstruction from sketches, often produce non-editable, approxi... |
Han_Unique_Lives_Shared_World_Learning_from_Single-Life_Videos_CVPR_2026_paper | Unique Lives, Shared World: Learning from Single-Life Videos | [
"Tengda Han",
"Sayna Ebrahimi",
"Dilara Gokay",
"Li Yang Ku",
"Maks Ovsjanikov",
"Iva Babukova",
"Daniel Zoran",
"Viorica Patraucean",
"Joao Carreira",
"Andrew Zisserman",
"Dima Damen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Han_Unique_Lives_Shared_World_Learning_from_Single-Life_Videos_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Han_Unique_Lives_Shared_World_Learning_from_Single-Life_Videos_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Han_Unique_Lives_Shared_CVPR_2026_supplemental.pdf | 2512.04085 | cvf | @InProceedings{Han_2026_CVPR,
author = {Han, Tengda and Ebrahimi, Sayna and Gokay, Dilara and Ku, Li Yang and Ovsjanikov, Maks and Babukova, Iva and Zoran, Daniel and Patraucean, Viorica and Carreira, Joao and Zisserman, Andrew and Damen, Dima},
title = {Unique Lives, Shared World: Learning from Single-L... | We introduce the "single-life" learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual. We leverage the multiple viewpoints naturally captured within a single life to learn a visual encoder in a self-supervised manner. Our experiments demonstrate three key f... |
Sinha_Quantized_Residuals_to_Continuous_Prompts_for_Few-Shot_Class_Incremental_Learning_CVPR_2026_paper | Quantized Residuals to Continuous Prompts for Few-Shot Class Incremental Learning in Vision-Language Models | [
"Abhishek Kumar Sinha",
"Nitant Dube",
"Soma Biswas"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Sinha_Quantized_Residuals_to_Continuous_Prompts_for_Few-Shot_Class_Incremental_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Sinha_Quantized_Residuals_to_Continuous_Prompts_for_Few-Shot_Class_Incremental_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Sinha_Quantized_Residuals_to_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Sinha_2026_CVPR,
author = {Sinha, Abhishek Kumar and Dube, Nitant and Biswas, Soma},
title = {Quantized Residuals to Continuous Prompts for Few-Shot Class Incremental Learning in Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Few-shot Class-Incremental Learning (FSCIL) requires learning new classes from very limited data while preventing catastrophic forgetting. Existing methods rely mainly on visual features and are prone to overfitting, while recent vision-language models (VLMs) offer better transferability but suppress fine-grained infor... |
Bui_EcoSplat_Efficiency-controllable_Feed-forward_3D_Gaussian_Splatting_from_Multi-view_Images_CVPR_2026_paper | EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images | [
"Minh-Quan Viet Bui",
"Jongmin Park",
"Juan Luis Gonzalez",
"Jaeho Moon",
"Jihyong Oh",
"Munchurl Kim"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Bui_EcoSplat_Efficiency-controllable_Feed-forward_3D_Gaussian_Splatting_from_Multi-view_Images_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Bui_EcoSplat_Efficiency-controllable_Feed-forward_3D_Gaussian_Splatting_from_Multi-view_Images_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Bui_EcoSplat_Efficiency-controllable_Feed-forward_CVPR_2026_supplemental.zip | 2512.18692 | cvf | @InProceedings{Bui_2026_CVPR,
author = {Bui, Minh-Quan Viet and Park, Jongmin and Gonzalez, Juan Luis and Moon, Jaeho and Oh, Jihyong and Kim, Munchurl},
title = {EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images},
booktitle = {Proceedings of the IEEE/CVF Con... | Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned primitives per-view, producing an excessive number of primitives in dense-view settin... |
Yuan_UniComp_Rethinking_Video_Compression_Through_Informational_Uniqueness_CVPR_2026_paper | UniComp: Rethinking Video Compression Through Informational Uniqueness | [
"Chao Yuan",
"Shimin Chen",
"Minliang Lin",
"Limeng Qiao",
"Guanglu Wan",
"Lin Ma"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Yuan_UniComp_Rethinking_Video_Compression_Through_Informational_Uniqueness_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yuan_UniComp_Rethinking_Video_Compression_Through_Informational_Uniqueness_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Yuan_UniComp_Rethinking_Video_CVPR_2026_supplemental.pdf | 2512.03575 | cvf | @InProceedings{Yuan_2026_CVPR,
author = {Yuan, Chao and Chen, Shimin and Lin, Minliang and Qiao, Limeng and Wan, Guanglu and Ma, Lin},
title = {UniComp: Rethinking Video Compression Through Informational Uniqueness},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the information fidelity of video representations under constrained computational budgets. Starting from the information-theoretic perspective, we f... |
Shen_AD-GBC_Anisotropic_Granular-Ball_Skip-Connection_Refiner_for_UNet-Based_Medical_Image_Segmentation_CVPR_2026_paper | AD-GBC: Anisotropic Granular-Ball Skip-Connection Refiner for UNet-Based Medical Image Segmentation | [
"Xiya Shen",
"Qinglin Zhao",
"Li Feng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shen_AD-GBC_Anisotropic_Granular-Ball_Skip-Connection_Refiner_for_UNet-Based_Medical_Image_Segmentation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shen_AD-GBC_Anisotropic_Granular-Ball_Skip-Connection_Refiner_for_UNet-Based_Medical_Image_Segmentation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shen_AD-GBC_Anisotropic_Granular-Ball_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Shen_2026_CVPR,
author = {Shen, Xiya and Zhao, Qinglin and Feng, Li},
title = {AD-GBC: Anisotropic Granular-Ball Skip-Connection Refiner for UNet-Based Medical Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Prototype or region-attention modules have recently improved medical image segmentation but still suffer from two fundamental limitations: 1) they represent each semantic concept as a point or isotropic region, failing to capture the inherently anisotropic geometry of real feature distributions; and 2) many rely on non... |
Li_White-Balance_First_Adjust_Later_Cross-Camera_Color_Constancy_via_Vision-Language_Evaluation_CVPR_2026_paper | White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation | [
"Shuwei Li",
"Lei Tan",
"Robby T. Tan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_White-Balance_First_Adjust_Later_Cross-Camera_Color_Constancy_via_Vision-Language_Evaluation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_White-Balance_First_Adjust_Later_Cross-Camera_Color_Constancy_via_Vision-Language_Evaluation_CVPR_2026_paper.pdf | null | 2605.19613 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Shuwei and Tan, Lei and Tan, Robby T.},
title = {White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Color constancy aims to keep object colors consistent under varying illumination. Cross-camera generalization in color constancy remains challenging because learning-based models often overfit to the color response characteristics of the training camera, resulting in degraded performance on images captured by other cam... |
Lu_Reallocating_Attention_Across_Layers_to_Reduce_Multimodal_Hallucination_CVPR_2026_paper | Reallocating Attention Across Layers to Reduce Multimodal Hallucination | [
"Haolang Lu",
"Bolun Chu",
"WeiYe Fu",
"Guoshun Nan",
"Junning Liu",
"Minghui Pan",
"Qiankun Li",
"Yi Yu",
"Hua Wang",
"Kun Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lu_Reallocating_Attention_Across_Layers_to_Reduce_Multimodal_Hallucination_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lu_Reallocating_Attention_Across_Layers_to_Reduce_Multimodal_Hallucination_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lu_Reallocating_Attention_Across_CVPR_2026_supplemental.pdf | 2510.10285 | cvf | @InProceedings{Lu_2026_CVPR,
author = {Lu, Haolang and Chu, Bolun and Fu, WeiYe and Nan, Guoshun and Liu, Junning and Pan, Minghui and Li, Qiankun and Yu, Yi and Wang, Hua and Wang, Kun},
title = {Reallocating Attention Across Layers to Reduce Multimodal Hallucination},
booktitle = {Proceedings of th... | Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between perception and reasoning processes. Building upon recent interpretability findings suggesting a staged division of attention across layers, we anal... |
Lin_PanoEnv_Exploring_3D_Spatial_Intelligence_in_Panoramic_Environments_with_Reinforcement_CVPR_2026_paper | PanoEnv: Exploring 3D Spatial Intelligence in Panoramic Environments with Reinforcement Learning | [
"Zekai Lin",
"Xu Zheng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lin_PanoEnv_Exploring_3D_Spatial_Intelligence_in_Panoramic_Environments_with_Reinforcement_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lin_PanoEnv_Exploring_3D_Spatial_Intelligence_in_Panoramic_Environments_with_Reinforcement_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lin_PanoEnv_Exploring_3D_CVPR_2026_supplemental.pdf | 2602.21992 | cvf | @InProceedings{Lin_2026_CVPR,
author = {Lin, Zekai and Zheng, Xu},
title = {PanoEnv: Exploring 3D Spatial Intelligence in Panoramic Environments with Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | 360deg panoramic images are increasingly used in VR, autonomous driving, and robotics for holistic scene understanding. However, current Vision-Language Models (VLMs) struggle with 3D spatial reasoning on Equirectangular Projection (ERP) images due to geometric distortion and limited 3D supervision. We introduce PanoEn... |
Seo_Erasing_Thousands_of_Concepts_Towards_Scalable_and_Practical_Concept_Erasure_CVPR_2026_paper | Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models | [
"Hoigi Seo",
"Byung Hyun Lee",
"Jaehyun Cho",
"Sungjin Lim",
"Se Young Chun"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Seo_Erasing_Thousands_of_Concepts_Towards_Scalable_and_Practical_Concept_Erasure_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Seo_Erasing_Thousands_of_Concepts_Towards_Scalable_and_Practical_Concept_Erasure_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Seo_Erasing_Thousands_of_CVPR_2026_supplemental.pdf | 2604.16481 | cvf | @InProceedings{Seo_2026_CVPR,
author = {Seo, Hoigi and Lee, Byung Hyun and Cho, Jaehyun and Lim, Sungjin and Chun, Se Young},
title = {Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference... | Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable content, such as copyrighted ones. Concept erasure has emerged as a mitigation strategy, yet existing approaches struggle to balance scalability, precision, and robust... |
Wu_TALON_Test-time_Adaptive_Learning_for_On-the-Fly_Category_Discovery_CVPR_2026_paper | TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery | [
"Yanan Wu",
"Yuhan Yan",
"Tailai Chen",
"Zhixiang Chi",
"ZiZhang Wu",
"Yi Jin",
"Yang Wang",
"Zhenbo Li"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_TALON_Test-time_Adaptive_Learning_for_On-the-Fly_Category_Discovery_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_TALON_Test-time_Adaptive_Learning_for_On-the-Fly_Category_Discovery_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wu_TALON_Test-time_Adaptive_CVPR_2026_supplemental.pdf | 2603.08075 | cvf | @InProceedings{Wu_2026_CVPR,
author = {Wu, Yanan and Yan, Yuhan and Chen, Tailai and Chi, Zhixiang and Wu, ZiZhang and Jin, Yi and Wang, Yang and Li, Zhenbo},
title = {TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream, using a model trained only on labeled data. Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into ... |
Chng_SenseSearch_Empowering_Vision-Language_Models_with_High-Resolution_Agentic_Search-Reasoning_via_Reinforcement_CVPR_2026_paper | SenseSearch: Empowering Vision-Language Models with High-Resolution Agentic Search-Reasoning via Reinforcement Learning | [
"Yong Xien Chng",
"Tao Hu",
"Wenwen Tong",
"Xueheng Li",
"Jiandong Chen",
"Haojia Yu",
"Jiefan Lu",
"Hewei Guo",
"Hanming Deng",
"Chengjun Xie",
"Gao Huang",
"Lewei Lu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Chng_SenseSearch_Empowering_Vision-Language_Models_with_High-Resolution_Agentic_Search-Reasoning_via_Reinforcement_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chng_SenseSearch_Empowering_Vision-Language_Models_with_High-Resolution_Agentic_Search-Reasoning_via_Reinforcement_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Chng_SenseSearch_Empowering_Vision-Language_CVPR_2026_supplemental.pdf | 2512.24330 | title_judge | @InProceedings{Chng_2026_CVPR,
author = {Chng, Yong Xien and Hu, Tao and Tong, Wenwen and Li, Xueheng and Chen, Jiandong and Yu, Haojia and Lu, Jiefan and Guo, Hewei and Deng, Hanming and Xie, Chengjun and Huang, Gao and Lu, Lewei},
title = {SenseSearch: Empowering Vision-Language Models with High-Resolu... | Vision-Language Models (VLMs) are limited by static knowledge and insufficient fine-grained visual analysis, hindering their performance on knowledge-intensive and visually complex tasks. While recent research has explored VLMs that employ external tools like search or cropping to enhance model performance, they typica... |
Wu_REArtGS_Generalizable_Articulation_Reconstruction_with_Temporal_Geometry_Constraint_via_Planar_CVPR_2026_paper | REArtGS++: Generalizable Articulation Reconstruction with Temporal Geometry Constraint via Planar Gaussian Splatting | [
"Di Wu",
"Liu Liu",
"Anran Huang",
"Yuyan Liu",
"Qiaojun Yu",
"Shaofan Liu",
"Liangtu Song",
"Cewu Lu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_REArtGS_Generalizable_Articulation_Reconstruction_with_Temporal_Geometry_Constraint_via_Planar_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_REArtGS_Generalizable_Articulation_Reconstruction_with_Temporal_Geometry_Constraint_via_Planar_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wu_REArtGS_Generalizable_Articulation_CVPR_2026_supplemental.pdf | 2511.17059 | cvf | @InProceedings{Wu_2026_CVPR,
author = {Wu, Di and Liu, Liu and Huang, Anran and Liu, Yuyan and Yu, Qiaojun and Liu, Shaofan and Song, Liangtu and Lu, Cewu},
title = {REArtGS++: Generalizable Articulation Reconstruction with Temporal Geometry Constraint via Planar Gaussian Splatting},
booktitle = {Pro... | Articulated objects are pervasive in daily environments, such as drawers and refrigerators. Towards their part-level surface reconstruction and joint parameter estimation, REArtGS [??] introduces a category-agnostic approach using multi-view RGB images at two different states. However, we observe that REArtGS still str... |
Jia_Quant_Experts_Token-aware_Adaptive_Error_Reconstruction_with_Mixture_of_Experts_CVPR_2026_paper | Quant Experts: Token-aware Adaptive Error Reconstruction with Mixture of Experts for Large Vision-Language Models Quantization | [
"Chenwei Jia",
"Baoting Li",
"Xuchong Zhang",
"Mingzhuo Wei",
"Bochen Lin",
"Hongbin Sun"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Jia_Quant_Experts_Token-aware_Adaptive_Error_Reconstruction_with_Mixture_of_Experts_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jia_Quant_Experts_Token-aware_Adaptive_Error_Reconstruction_with_Mixture_of_Experts_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Jia_Quant_Experts_Token-aware_CVPR_2026_supplemental.pdf | 2602.24059 | cvf | @InProceedings{Jia_2026_CVPR,
author = {Jia, Chenwei and Li, Baoting and Zhang, Xuchong and Wei, Mingzhuo and Lin, Bochen and Sun, Hongbin},
title = {Quant Experts: Token-aware Adaptive Error Reconstruction with Mixture of Experts for Large Vision-Language Models Quantization},
booktitle = {Proceedin... | Post-Training Quantization (PTQ) has emerged as an effective technique for alleviating the substantial computational and memory overheads of Vision-Language Models (VLMs) by compressing both weights and activations without retraining the full model. Existing PTQ methods primarily rely on static identification and globa... |
Huynh_Efficient_and_High-Fidelity_Omni_Modality_Retrieval_CVPR_2026_paper | Efficient and High-Fidelity Omni Modality Retrieval | [
"Chuong Huynh",
"Manh Luong",
"Abhinav Shrivastava"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huynh_Efficient_and_High-Fidelity_Omni_Modality_Retrieval_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huynh_Efficient_and_High-Fidelity_Omni_Modality_Retrieval_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huynh_Efficient_and_High-Fidelity_CVPR_2026_supplemental.pdf | 2603.02098 | cvf | @InProceedings{Huynh_2026_CVPR,
author = {Huynh, Chuong and Luong, Manh and Shrivastava, Abhinav},
title = {Efficient and High-Fidelity Omni Modality Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Multimodal retrieval is the task of aggregating information from queries across heterogeneous modalities to retrieve desired targets. State-of-the-art multimodal retrieval models can understand complex queries, yet they are typically limited to two modalities: text and vision. This limitation impedes the development of... |
Xie_SynCLIP_Synonym-Coherent_Language-Image_Pretraining_for_Robust_Open-Vocabulary_Dense_Perception_CVPR_2026_paper | SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception | [
"Mingjie Xie",
"Guangjun He",
"Dongli Xu",
"Youtian Lin",
"Hongjue Li",
"Pengming Feng",
"Jian Guan",
"Yue Deng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xie_SynCLIP_Synonym-Coherent_Language-Image_Pretraining_for_Robust_Open-Vocabulary_Dense_Perception_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xie_SynCLIP_Synonym-Coherent_Language-Image_Pretraining_for_Robust_Open-Vocabulary_Dense_Perception_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xie_SynCLIP_Synonym-Coherent_Language-Image_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Xie_2026_CVPR,
author = {Xie, Mingjie and He, Guangjun and Xu, Dongli and Lin, Youtian and Li, Hongjue and Feng, Pengming and Guan, Jian and Deng, Yue},
title = {SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception},
booktitle = {Proceedings ... | Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge. Despite the remarkable progress of recent CLIP-based approaches, we identify a critical limitation: synonym-induced grounding inconsistency, where semantically equivalent expressions yield disparate ... |
Zhu_Dr.Occ_Depth-_and_Region-Guided_3D_Occupancy_from_Surround-View_Cameras_for_CVPR_2026_paper | Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving | [
"Xubo Zhu",
"Haoyang Zhang",
"Fei He",
"Rui Wu",
"Yanhu Shan",
"Wen Yang",
"Huai Yu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhu_Dr.Occ_Depth-_and_Region-Guided_3D_Occupancy_from_Surround-View_Cameras_for_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhu_Dr.Occ_Depth-_and_Region-Guided_3D_Occupancy_from_Surround-View_Cameras_for_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhu_Dr.Occ_Depth-_and_CVPR_2026_supplemental.pdf | 2603.01007 | title_snapshot | @InProceedings{Zhu_2026_CVPR,
author = {Zhu, Xubo and Zhang, Haoyang and He, Fei and Wu, Rui and Shan, Yanhu and Yang, Wen and Yu, Huai},
title = {Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on C... | 3D semantic occupancy prediction is crucial for autonomous driving perception, offering comprehensive geometric scene understanding and semantic recognition. However, existing methods struggle with geometric misalignment in view transformation due to lack of pixel-level accurate depth estimation, and severe spatial cla... |
Fan_More_than_the_Sum_Panorama-Language_Models_for_Adverse_Omni-Scenes_CVPR_2026_paper | More than the Sum: Panorama-Language Models for Adverse Omni-Scenes | [
"Weijia Fan",
"Ruiping Liu",
"Jiale Wei",
"Yufan Chen",
"Junwei Zheng",
"Zichao Zeng",
"Jiaming Zhang",
"Qiufu Li",
"Linlin Shen",
"Rainer Stiefelhagen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Fan_More_than_the_Sum_Panorama-Language_Models_for_Adverse_Omni-Scenes_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fan_More_than_the_Sum_Panorama-Language_Models_for_Adverse_Omni-Scenes_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Fan_More_than_the_CVPR_2026_supplemental.pdf | 2603.09573 | cvf | @InProceedings{Fan_2026_CVPR,
author = {Fan, Weijia and Liu, Ruiping and Wei, Jiale and Chen, Yufan and Zheng, Junwei and Zeng, Zichao and Zhang, Jiaming and Li, Qiufu and Shen, Linlin and Stiefelhagen, Rainer},
title = {More than the Sum: Panorama-Language Models for Adverse Omni-Scenes},
booktitle ... | Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet, such multi-view perception overlooks the holistic spatial and contextual relationships that a single panorama inherently preserves. In this ... |
Xarles_AdaSpot_Spend_Resolution_Where_It_Matters_for_Precise_Event_Spotting_CVPR_2026_paper | AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting | [
"Artur Xarles",
"Sergio Escalera",
"Thomas B. Moeslund",
"Albert Clapés"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xarles_AdaSpot_Spend_Resolution_Where_It_Matters_for_Precise_Event_Spotting_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xarles_AdaSpot_Spend_Resolution_Where_It_Matters_for_Precise_Event_Spotting_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xarles_AdaSpot_Spend_Resolution_CVPR_2026_supplemental.zip | 2602.22073 | cvf | @InProceedings{Xarles_2026_CVPR,
author = {Xarles, Artur and Escalera, Sergio and Moeslund, Thomas B. and Clap\'es, Albert},
title = {AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Precise Event Spotting aims to localize fast-paced actions or events in videos with high temporal precision, a key task for applications in sports analytics, robotics, and autonomous systems. Existing methods typically process all frames uniformly, overlooking the inherent spatio-temporal redundancy in video data. This... |
Wen_Prompt-Anchored_Vision-Text_Distillation_for_Lifelong_Person_Re-identification_CVPR_2026_paper | Prompt-Anchored Vision-Text Distillation for Lifelong Person Re-identification | [
"Wen Wen",
"Hao Chen",
"Shiliang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wen_Prompt-Anchored_Vision-Text_Distillation_for_Lifelong_Person_Re-identification_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wen_Prompt-Anchored_Vision-Text_Distillation_for_Lifelong_Person_Re-identification_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wen_Prompt-Anchored_Vision-Text_Distillation_CVPR_2026_supplemental.pdf | 2605.05027 | cvf | @InProceedings{Wen_2026_CVPR,
author = {Wen, Wen and Chen, Hao and Zhang, Shiliang},
title = {Prompt-Anchored Vision-Text Distillation for Lifelong Person Re-identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Lifelong person re-identification (LReID) aims to train a generalizable model with sequentially collected data. However, such models often suffer from semantic drift, limited adaptability, and catastrophic forgetting as new domains emerge. Existing exemplar-free approaches largely rely on visual-only distillation or pa... |
Pierard_What_Is_the_Optimal_Ranking_Score_Between_Precision_and_Recall_CVPR_2026_paper | What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely F1 | [
"Sébastien Piérard",
"Adrien Deliège",
"Marc Van Droogenbroeck"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Pierard_What_Is_the_Optimal_Ranking_Score_Between_Precision_and_Recall_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Pierard_What_Is_the_Optimal_Ranking_Score_Between_Precision_and_Recall_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Pierard_What_Is_the_CVPR_2026_supplemental.pdf | 2511.22442 | title_judge | @InProceedings{Pierard_2026_CVPR,
author = {Pi\'erard, S\'ebastien and Deli\`ege, Adrien and Van Droogenbroeck, Marc},
title = {What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely F1},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Ranking methods or models based on their performance is of prime importance but is tricky because performance is fundamentally multidimensional. In the case of classification, precision and recall are scores with probabilistic interpretations that are both important to consider and complementary. The rankings induced b... |
Zhang_From_Spots_to_Pixels_Dense_Spatial_Gene_Expression_Prediction_from_CVPR_2026_paper | From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images | [
"Ruikun Zhang",
"Yan Yang",
"Liyuan Pan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_From_Spots_to_Pixels_Dense_Spatial_Gene_Expression_Prediction_from_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_From_Spots_to_Pixels_Dense_Spatial_Gene_Expression_Prediction_from_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_From_Spots_to_CVPR_2026_supplemental.pdf | 2503.01347 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Ruikun and Yang, Yan and Pan, Liyuan},
title = {From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding ge... |
Kong_Rethinking_Pose_Refinement_in_3D_Gaussian_Splatting_under_Pose_Prior_CVPR_2026_paper | Rethinking Pose Refinement in 3D Gaussian Splatting under Pose Prior and Geometric Uncertainty | [
"Mangyu Kong",
"Jaewon Lee",
"Seongwon Lee",
"Euntai Kim"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kong_Rethinking_Pose_Refinement_in_3D_Gaussian_Splatting_under_Pose_Prior_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kong_Rethinking_Pose_Refinement_in_3D_Gaussian_Splatting_under_Pose_Prior_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kong_Rethinking_Pose_Refinement_CVPR_2026_supplemental.pdf | 2603.16538 | cvf | @InProceedings{Kong_2026_CVPR,
author = {Kong, Mangyu and Lee, Jaewon and Lee, Seongwon and Kim, Euntai},
title = {Rethinking Pose Refinement in 3D Gaussian Splatting under Pose Prior and Geometric Uncertainty},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | 3D Gaussian Splatting (3DGS) has recently emerged as a powerful scene representation and is increasingly used for visual localization and pose refinement. However, despite its high-quality differentiable rendering, the robustness of 3DGS-based pose refinement remains highly sensitive to both the initial camera pose and... |
Wu_RetouchIQ_MLLM_Agents_for_Instruction-Based_Image_Retouching_with_Generalist_Reward_CVPR_2026_paper | RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward | [
"Qiucheng Wu",
"Jing Shi",
"Simon Jenni",
"Kushal Kafle",
"Tianyu Wang",
"Shiyu Chang",
"Handong Zhao"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_RetouchIQ_MLLM_Agents_for_Instruction-Based_Image_Retouching_with_Generalist_Reward_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_RetouchIQ_MLLM_Agents_for_Instruction-Based_Image_Retouching_with_Generalist_Reward_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wu_RetouchIQ_MLLM_Agents_CVPR_2026_supplemental.pdf | 2602.17558 | cvf | @InProceedings{Wu_2026_CVPR,
author = {Wu, Qiucheng and Shi, Jing and Jenni, Simon and Kafle, Kushal and Wang, Tianyu and Chang, Shiyu and Zhao, Handong},
title = {RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Recent advances in multimodal large language models (MLLMs) have shown great potential for extending vision-language reasoning to professional tool-based image editing, enabling intuitive and creative editing. A promising direction is to use reinforcement learning (RL) to enable MLLMs to reason about and execute optima... |
Gao_Anomaly-Related_Residual_Fields_for_Cross-domain_Anomaly_Detection_CVPR_2026_paper | Anomaly-Related Residual Fields for Cross-domain Anomaly Detection | [
"Kewei Gao",
"Jiayi Xie",
"Zhengda Shen",
"Weijun Qin",
"Lingxiang Jia",
"Kejia Chen",
"Zunlei Feng",
"Yijun Bei"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Gao_Anomaly-Related_Residual_Fields_for_Cross-domain_Anomaly_Detection_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gao_Anomaly-Related_Residual_Fields_for_Cross-domain_Anomaly_Detection_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Gao_Anomaly-Related_Residual_Fields_CVPR_2026_supplemental.zip | null | null | @InProceedings{Gao_2026_CVPR,
author = {Gao, Kewei and Xie, Jiayi and Shen, Zhengda and Qin, Weijun and Jia, Lingxiang and Chen, Kejia and Feng, Zunlei and Bei, Yijun},
title = {Anomaly-Related Residual Fields for Cross-domain Anomaly Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Label-free image anomaly detection is difficult because anomalies must be separated from intra-normal variability. Diffusion models learn a manifold for normal data, and, under the common assumption that off-manifold anomalies are harder to generate and yield larger prediction errors, many methods build detectors from ... |
Dong_DreamSR_Towards_Ultra-High-Resolution_Image_Super-Resolution_via_a_Receptive-Field_Enhanced_Diffusion_CVPR_2026_paper | DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer | [
"Qingji Dong",
"Hang Dong",
"Mingqin Chen",
"Rui Zhang",
"Yitong Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Dong_DreamSR_Towards_Ultra-High-Resolution_Image_Super-Resolution_via_a_Receptive-Field_Enhanced_Diffusion_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Dong_DreamSR_Towards_Ultra-High-Resolution_Image_Super-Resolution_via_a_Receptive-Field_Enhanced_Diffusion_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Dong_DreamSR_Towards_Ultra-High-Resolution_CVPR_2026_supplemental.pdf | 2605.15682 | cvf | @InProceedings{Dong_2026_CVPR,
author = {Dong, Qingji and Dong, Hang and Chen, Mingqin and Zhang, Rui and Wang, Yitong},
title = {DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Large-scale pre-trained diffusion models have been extensively adopted for real-world image Super-Resolution because of their powerful generative priors through textual guidance. However, when super-resolving high-resolution images with patch-wise inference strategy, most existing diffusion-based SR methods tend to suf... |
Cheng_LongStream_Long-Sequence_Streaming_Autoregressive_Visual_Geometry_CVPR_2026_paper | LongStream: Long-Sequence Streaming Autoregressive Visual Geometry | [
"Chong Cheng",
"Xianda Chen",
"Tao Xie",
"Wei Yin",
"Weiqiang Ren",
"Qian Zhang",
"Xiaoyang Guo",
"Hao Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Cheng_LongStream_Long-Sequence_Streaming_Autoregressive_Visual_Geometry_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cheng_LongStream_Long-Sequence_Streaming_Autoregressive_Visual_Geometry_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Cheng_LongStream_Long-Sequence_Streaming_CVPR_2026_supplemental.zip | 2602.13172 | cvf | @InProceedings{Cheng_2026_CVPR,
author = {Cheng, Chong and Chen, Xianda and Xie, Tao and Yin, Wei and Ren, Weiqiang and Zhang, Qian and Guo, Xiaoyang and Wang, Hao},
title = {LongStream: Long-Sequence Streaming Autoregressive Visual Geometry},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Long-sequence streaming 3D reconstruction remains a significant open challenge. Existing autoregressive models often fail when processing long sequences because they anchor poses to the first frame, leading to attention decay, scale drift, and extrapolation errors. We introduce LongStream, a novel gauge-decoupled strea... |
Ghosal_VisRef_Visual_Refocusing_while_Thinking_Improves_Test-Time_Scaling_in_Multi-Modal_CVPR_2026_paper | VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models | [
"Soumya Suvra Ghosal",
"Youngeun Kim",
"Zhuowei Li",
"Ritwick Chaudhry",
"Linghan Xu",
"Hongjing Zhang",
"Jakub Zablocki",
"Yifan Xing",
"Qin Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ghosal_VisRef_Visual_Refocusing_while_Thinking_Improves_Test-Time_Scaling_in_Multi-Modal_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ghosal_VisRef_Visual_Refocusing_while_Thinking_Improves_Test-Time_Scaling_in_Multi-Modal_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ghosal_VisRef_Visual_Refocusing_CVPR_2026_supplemental.pdf | 2603.00207 | cvf | @InProceedings{Ghosal_2026_CVPR,
author = {Ghosal, Soumya Suvra and Kim, Youngeun and Li, Zhuowei and Chaudhry, Ritwick and Xu, Linghan and Zhang, Hongjing and Zablocki, Jakub and Xing, Yifan and Zhang, Qin},
title = {VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Larg... | Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended inference-time thinking. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can often degrade performance as models progress... |
He_Enhancing_the_Security_of_Visual_Speaker_Authentication_Based_on_Dynamic_CVPR_2026_paper | Enhancing the Security of Visual Speaker Authentication Based on Dynamic Lip-Print Analysis | [
"Yi He",
"Lei Yang",
"Bofan Chen",
"Shilin Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/He_Enhancing_the_Security_of_Visual_Speaker_Authentication_Based_on_Dynamic_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/He_Enhancing_the_Security_of_Visual_Speaker_Authentication_Based_on_Dynamic_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/He_Enhancing_the_Security_CVPR_2026_supplemental.pdf | null | null | @InProceedings{He_2026_CVPR,
author = {He, Yi and Yang, Lei and Chen, Bofan and Wang, Shilin},
title = {Enhancing the Security of Visual Speaker Authentication Based on Dynamic Lip-Print Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | In recent years, face-based authentication methods are gradually replacing traditional methods across various applications, offering enhanced security and user convenience. However, these methods are threatened by the continuously evolving DeepFake techniques. In this paper, a novel Visual Speaker Authentication (VSA) ... |
Hollidt_Ultra_Diffusion_Poser_Diffusion-Based_Human_Motion_Tracking_from_Sparse_Inertial_CVPR_2026_paper | Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking from Sparse Inertial Sensors and Ranging-based Between-sensor Distances | [
"Dominik Hollidt",
"Tommaso Bendinelli",
"Christian Holz"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Hollidt_Ultra_Diffusion_Poser_Diffusion-Based_Human_Motion_Tracking_from_Sparse_Inertial_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Hollidt_Ultra_Diffusion_Poser_Diffusion-Based_Human_Motion_Tracking_from_Sparse_Inertial_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Hollidt_Ultra_Diffusion_Poser_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Hollidt_2026_CVPR,
author = {Hollidt, Dominik and Bendinelli, Tommaso and Holz, Christian},
title = {Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking from Sparse Inertial Sensors and Ranging-based Between-sensor Distances},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Methods using inertial measurement units (IMUs) provide a wearable alternative to camera-based motion capture. To mitigate drift from inertial signals, recent sparse inertial pose estimators integrate inter-sensor distances measured by ultra-wideband (UWB) ranging. So far, UWB distances have only been used as an additi... |
Huang_Beyond_Mimicry_Learning_Whole-Body_Human-Humanoid_Interaction_from_Human-Human_Demonstrations_CVPR_2026_paper | Beyond Mimicry: Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations | [
"Wei-Jin Huang",
"Yue-Yi Zhang",
"Yi-Lin Wei",
"Zhi-Wei Xia",
"Juantao Tan",
"Yuan-Ming Li",
"Zhilin Zhao",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huang_Beyond_Mimicry_Learning_Whole-Body_Human-Humanoid_Interaction_from_Human-Human_Demonstrations_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huang_Beyond_Mimicry_Learning_Whole-Body_Human-Humanoid_Interaction_from_Human-Human_Demonstrations_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huang_Beyond_Mimicry_Learning_CVPR_2026_supplemental.pdf | 2601.09518 | title_judge | @InProceedings{Huang_2026_CVPR,
author = {Huang, Wei-Jin and Zhang, Yue-Yi and Wei, Yi-Lin and Xia, Zhi-Wei and Tan, Juantao and Li, Yuan-Ming and Zhao, Zhilin and Zheng, Wei-Shi},
title = {Beyond Mimicry: Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations},
booktitle = {P... | Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data. While leveraging abundant Human-Human Interaction (HHI) data presents a scalable alternative, we first demonstrate that standard retargetin... |
Ma_Pluggable_Pruning_with_Contiguous_Layer_Distillation_for_Diffusion_Transformers_CVPR_2026_paper | Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers | [
"Jian Ma",
"Qirong Peng",
"Xujie Zhu",
"Peixing Xie",
"Chen Chen",
"Haonan Lu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ma_Pluggable_Pruning_with_Contiguous_Layer_Distillation_for_Diffusion_Transformers_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ma_Pluggable_Pruning_with_Contiguous_Layer_Distillation_for_Diffusion_Transformers_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ma_Pluggable_Pruning_with_CVPR_2026_supplemental.zip | 2511.16156 | cvf | @InProceedings{Ma_2026_CVPR,
author = {Ma, Jian and Peng, Qirong and Zhu, Xujie and Xie, Peixing and Chen, Chen and Lu, Haonan},
title = {Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Diffusion Transformers (DiTs) have shown exceptional performance in image generation, yet their large parameter counts incur high computational costs, impeding deployment in resource-constrained settings. To address this, we propose Pluggable Pruning with Contiguous Layer Distillation (PPCL), a flexible structured prun... |
Nie_PersonaVLM_Long-Term_Personalized_Multimodal_LLMs_CVPR_2026_paper | PersonaVLM: Long-Term Personalized Multimodal LLMs | [
"Chang Nie",
"Chaoyou Fu",
"Yifan Zhang",
"Haihua Yang",
"Caifeng Shan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Nie_PersonaVLM_Long-Term_Personalized_Multimodal_LLMs_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Nie_PersonaVLM_Long-Term_Personalized_Multimodal_LLMs_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Nie_PersonaVLM_Long-Term_Personalized_CVPR_2026_supplemental.pdf | 2604.13074 | cvf | @InProceedings{Nie_2026_CVPR,
author = {Nie, Chang and Fu, Chaoyou and Zhang, Yifan and Yang, Haihua and Shan, Caifeng},
title = {PersonaVLM: Long-Term Personalized Multimodal LLMs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture use... |
Du_APPO_Attention-guided_Perception_Policy_Optimization_for_Video_Reasoning_CVPR_2026_paper | APPO: Attention-guided Perception Policy Optimization for Video Reasoning | [
"Henghui Du",
"Chang Zhou",
"Xi Chen",
"Di Hu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Du_APPO_Attention-guided_Perception_Policy_Optimization_for_Video_Reasoning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Du_APPO_Attention-guided_Perception_Policy_Optimization_for_Video_Reasoning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Du_APPO_Attention-guided_Perception_CVPR_2026_supplemental.pdf | 2602.23823 | cvf | @InProceedings{Du_2026_CVPR,
author = {Du, Henghui and Zhou, Chang and Chen, Xi and Hu, Di},
title = {APPO: Attention-guided Perception Policy Optimization for Video Reasoning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | Complex video reasoning, actually, relies excessively on fine-grained perception rather than on expert (e.g., Ph.D, Science)-level reasoning.Through extensive empirical observation, we have recognized the critical impact of perception.In particular, when perception ability is almost fixed, enhancing reasoning from Qwen... |
Wu_An_Efficient_Token_Compression_Framework_for_Visual_Object_Tracking_CVPR_2026_paper | An Efficient Token Compression Framework for Visual Object Tracking | [
"Weijing Wu",
"Qihua Liang",
"Bineng Zhong",
"Haiying Xia",
"Zhiyi Mo",
"Shuxiang Song"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_An_Efficient_Token_Compression_Framework_for_Visual_Object_Tracking_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_An_Efficient_Token_Compression_Framework_for_Visual_Object_Tracking_CVPR_2026_paper.pdf | null | 2605.08329 | cvf | @InProceedings{Wu_2026_CVPR,
author = {Wu, Weijing and Liang, Qihua and Zhong, Bineng and Xia, Haiying and Mo, Zhiyi and Song, Shuxiang},
title = {An Efficient Token Compression Framework for Visual Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and computational cost of models in visual tracking. To enhance their performance, many contemporary Transformer-based trackers leverage a larger number of historical template ... |
Liu_GraphVLM_Benchmarking_Vision_Language_Models_for_Multimodal_Graph_Learning_CVPR_2026_paper | GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning | [
"Jiajin Liu",
"Dongzhe Fan",
"Chuanhao Ji",
"Daochen Zha",
"Qiaoyu Tan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Liu_GraphVLM_Benchmarking_Vision_Language_Models_for_Multimodal_Graph_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liu_GraphVLM_Benchmarking_Vision_Language_Models_for_Multimodal_Graph_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Liu_GraphVLM_Benchmarking_Vision_CVPR_2026_supplemental.pdf | 2603.13370 | cvf | @InProceedings{Liu_2026_CVPR,
author = {Liu, Jiajin and Fan, Dongzhe and Ji, Chuanhao and Zha, Daochen and Tan, Qiaoyu},
title = {GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected through explicit relational graphs, remains largely underexplored. Unlocking this capability is crucial fo... |
Ahmed_Boundary-Responsive_Differentiable_Gating_for_Superpixel-Based_Segmentation_CVPR_2026_paper | Boundary-Responsive Differentiable Gating for Superpixel-Based Segmentation | [
"Fatmaelzahraa Ahmed",
"Zhihe Lu",
"Gianni Caro",
"Diram Tabaa",
"Mohamed Hamdy",
"Muraam Abdel-Ghani",
"Abdulaziz Al-Ali",
"Muhammad Arsalan",
"Shidin Balakrishnan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ahmed_Boundary-Responsive_Differentiable_Gating_for_Superpixel-Based_Segmentation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ahmed_Boundary-Responsive_Differentiable_Gating_for_Superpixel-Based_Segmentation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ahmed_Boundary-Responsive_Differentiable_Gating_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Ahmed_2026_CVPR,
author = {Ahmed, Fatmaelzahraa and Lu, Zhihe and Caro, Gianni and Tabaa, Diram and Hamdy, Mohamed and Abdel-Ghani, Muraam and Al-Ali, Abdulaziz and Arsalan, Muhammad and Balakrishnan, Shidin},
title = {Boundary-Responsive Differentiable Gating for Superpixel-Based Segmenta... | We present BRDG, a boundary-responsive differentiable gating superpixel framework designed to resolve the trade-off between computational efficiency and segmentation precision in surgical scenes. At its core, the architecture is organized into three cooperative agents within a fully differentiable backbone. The Region ... |
Mori_Rethinking_Asymmetric_Quantization_Hidden_Symmetry_in_Vision_Model_Weights_CVPR_2026_paper | Rethinking Asymmetric Quantization: Hidden Symmetry in Vision Model Weights | [
"Masafumi Mori",
"Shinya Gongyo",
"Mitsuru Ambai"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Mori_Rethinking_Asymmetric_Quantization_Hidden_Symmetry_in_Vision_Model_Weights_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Mori_Rethinking_Asymmetric_Quantization_Hidden_Symmetry_in_Vision_Model_Weights_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Mori_Rethinking_Asymmetric_Quantization_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Mori_2026_CVPR,
author = {Mori, Masafumi and Gongyo, Shinya and Ambai, Mitsuru},
title = {Rethinking Asymmetric Quantization: Hidden Symmetry in Vision Model Weights},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Post-training quantization (PTQ) enables rapid deployment of deep pretrained models. In the low-bit regime, recent PTQ methods for vision models adopt asymmetric quantization (AsymQ), introducing zero-point offsets to mitigate quantization errors. However, these offsets impose substantial hardware overhead and fail to ... |
Lv_Hierarchical_Enhancement_of_Semantic_Priors_for_Disentangled_Text-Driven_Motion_Generation_CVPR_2026_paper | Hierarchical Enhancement of Semantic Priors for Disentangled Text-Driven Motion Generation | [
"Wenhan Lv",
"Shaopan Wang",
"Xiangyu Wu",
"Tianchu Hang",
"Zhongquan Jian",
"Qingqiang Wu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lv_Hierarchical_Enhancement_of_Semantic_Priors_for_Disentangled_Text-Driven_Motion_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lv_Hierarchical_Enhancement_of_Semantic_Priors_for_Disentangled_Text-Driven_Motion_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lv_Hierarchical_Enhancement_of_CVPR_2026_supplemental.zip | null | null | @InProceedings{Lv_2026_CVPR,
author = {Lv, Wenhan and Wang, Shaopan and Wu, Xiangyu and Hang, Tianchu and Jian, Zhongquan and Wu, Qingqiang},
title = {Hierarchical Enhancement of Semantic Priors for Disentangled Text-Driven Motion Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Text-to-motion generation aims to synthesize realistic and semantically aligned 3D human motions from natural language descriptions. Existing diffusion-based methods often rely on isotropic latent priors and shallow cross-modal supervision, which lead to semantic entanglement, limited controllability, and poor interpre... |
Shaar_MovieRecapsQA_A_Multimodal_Open-Ended_Video_Question-Answering_Benchmark_CVPR_2026_paper | MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark | [
"Shaden Shaar",
"Bradon Thymes",
"Sirawut Chaixanien",
"Claire Cardie",
"Bharath Hariharan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shaar_MovieRecapsQA_A_Multimodal_Open-Ended_Video_Question-Answering_Benchmark_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shaar_MovieRecapsQA_A_Multimodal_Open-Ended_Video_Question-Answering_Benchmark_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shaar_MovieRecapsQA_A_Multimodal_CVPR_2026_supplemental.pdf | 2601.02536 | cvf | @InProceedings{Shaar_2026_CVPR,
author = {Shaar, Shaden and Thymes, Bradon and Chaixanien, Sirawut and Cardie, Claire and Hariharan, Bharath},
title = {MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | Understanding real-world videos such as movies requires integrating visual and dialogue cues. Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and, given the difficulty of evaluating free-form answers, largely resort to simple multiple choice questions. We introduce a novel open-ended mult... |
Kabadayi_PhysHead_Simulation-Ready_Gaussian_Head_Avatars_CVPR_2026_paper | PhysHead: Simulation-Ready Gaussian Head Avatars | [
"Berna Kabadayi",
"Vanessa Sklyarova",
"Wojciech Zielonka",
"Justus Thies",
"Gerard Pons-Moll"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kabadayi_PhysHead_Simulation-Ready_Gaussian_Head_Avatars_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kabadayi_PhysHead_Simulation-Ready_Gaussian_Head_Avatars_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kabadayi_PhysHead_Simulation-Ready_Gaussian_CVPR_2026_supplemental.pdf | 2604.06467 | cvf | @InProceedings{Kabadayi_2026_CVPR,
author = {Kabadayi, Berna and Sklyarova, Vanessa and Zielonka, Wojciech and Thies, Justus and Pons-Moll, Gerard},
title = {PhysHead: Simulation-Ready Gaussian Head Avatars},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Realistic digital avatars require expressive and dynamic hair motion; however, most existing head avatar methods assume rigid hair movement. These methods often fail to disentangle hair from the head, representing it as a simple outer shell and failing to capture its natural volumetric behavior. In this paper, we addre... |
Zhou_Scalable_Object_Relation_Encoding_for_Better_3D_Spatial_Reasoning_in_CVPR_2026_paper | Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models | [
"Shengli Zhou",
"Minghang Zheng",
"Feng Zheng",
"Yang Liu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_Scalable_Object_Relation_Encoding_for_Better_3D_Spatial_Reasoning_in_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhou_Scalable_Object_Relation_Encoding_for_Better_3D_Spatial_Reasoning_in_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhou_Scalable_Object_Relation_CVPR_2026_supplemental.pdf | 2603.24721 | cvf | @InProceedings{Zhou_2026_CVPR,
author = {Zhou, Shengli and Zheng, Minghang and Zheng, Feng and Liu, Yang},
title = {Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Spatial reasoning focuses on locating target objects based on spatial relations in 3D scenes, which plays a crucial role in developing intelligent embodied agents. Due to the limited availability of 3D scene-language paired data, it is challenging to train models with strong reasoning ability from scratch. Previous app... |
Foo_Physical_Simulator_In-the-Loop_Video_Generation_CVPR_2026_paper | Physical Simulator In-the-Loop Video Generation | [
"Lin Geng Foo",
"Mark He Huang",
"Alexandros Lattas",
"Stylianos Moschoglou",
"Thabo Beeler",
"Christian Theobalt"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Foo_Physical_Simulator_In-the-Loop_Video_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Foo_Physical_Simulator_In-the-Loop_Video_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Foo_Physical_Simulator_In-the-Loop_CVPR_2026_supplemental.pdf | 2603.06408 | cvf | @InProceedings{Foo_2026_CVPR,
author = {Foo, Lin Geng and Huang, Mark He and Lattas, Alexandros and Moschoglou, Stylianos and Beeler, Thabo and Theobalt, Christian},
title = {Physical Simulator In-the-Loop Video Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Recent advances in diffusion-based video generation have achieved remarkable visual realism but still struggle to obey basic physical laws such as gravity, inertia, and collision. Generated objects often move inconsistently across frames, exhibit implausible dynamics, or violate physical constraints, limiting the reali... |
Wang_Multimodal_Protein_Language_Models_for_Enzyme_Kinetic_Parameters_From_Substrate_CVPR_2026_paper | Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation | [
"Fei Wang",
"Xinye Zheng",
"Kun Li",
"Yanyan Wei",
"Yuxin Liu",
"Ganpeng Hu",
"Tong Bao",
"Jingwen Yang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wang_Multimodal_Protein_Language_Models_for_Enzyme_Kinetic_Parameters_From_Substrate_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wang_Multimodal_Protein_Language_Models_for_Enzyme_Kinetic_Parameters_From_Substrate_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wang_Multimodal_Protein_Language_CVPR_2026_supplemental.pdf | 2603.12845 | cvf | @InProceedings{Wang_2026_CVPR,
author = {Wang, Fei and Zheng, Xinye and Li, Kun and Wei, Yanyan and Liu, Yuxin and Hu, Ganpeng and Bao, Tong and Yang, Jingwen},
title = {Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation},
booktit... | Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canonical parameters such as the turnover number (k_\text cat ), Michaelis constant (K_\text m ), and inhibition constant (K_\text i ) depend jointly on the enzyme sequence, the... |
Choi_PR-IQA_Partial-Reference_Image_Quality_Assessment_for_Diffusion-Based_Novel_View_Synthesis_CVPR_2026_paper | PR-IQA: Partial-Reference Image Quality Assessment for Diffusion-Based Novel View Synthesis | [
"Inseong Choi",
"Siwoo Lee",
"Seung-Hun Nam",
"Soohwan Song"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Choi_PR-IQA_Partial-Reference_Image_Quality_Assessment_for_Diffusion-Based_Novel_View_Synthesis_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Choi_PR-IQA_Partial-Reference_Image_Quality_Assessment_for_Diffusion-Based_Novel_View_Synthesis_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Choi_PR-IQA_Partial-Reference_Image_CVPR_2026_supplemental.pdf | 2604.04576 | cvf | @InProceedings{Choi_2026_CVPR,
author = {Choi, Inseong and Lee, Siwoo and Nam, Seung-Hun and Song, Soohwan},
title = {PR-IQA: Partial-Reference Image Quality Assessment for Diffusion-Based Novel View Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Diffusion models are promising for sparse-view novel view synthesis (NVS), as they can generate pseudo-ground-truth views to aid 3D reconstruction pipelines like 3D Gaussian Splatting (3DGS). However, these synthesized images often contain photometric and geometric inconsistencies, and their direct use for supervision ... |
Huang_FloVerse_Floor_Plan-Guided_Multi-Modal_Navigation_CVPR_2026_paper | FloVerse: Floor Plan-Guided Multi-Modal Navigation | [
"Weiqi Huang",
"Shuangyi Dong",
"Jiaxin Li",
"Yifei Guo",
"Zan Wang",
"Wei Liang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huang_FloVerse_Floor_Plan-Guided_Multi-Modal_Navigation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huang_FloVerse_Floor_Plan-Guided_Multi-Modal_Navigation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huang_FloVerse_Floor_Plan-Guided_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Huang_2026_CVPR,
author = {Huang, Weiqi and Dong, Shuangyi and Li, Jiaxin and Guo, Yifei and Wang, Zan and Liang, Wei},
title = {FloVerse: Floor Plan-Guided Multi-Modal Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Floor plans encapsulate compact spatial priors, enabling agents to navigate unseen scenes more efficiently. While prior work has explored floor plan-guided navigation, it has focused mainly on PointNav and a limited set of environments. To bridge this gap, we introduce FloVerse, a new task for floor plan-guided embodie... |
Kuang_ConceptPose_Training-Free_Zero-Shot_Object_Pose_Estimation_using_Concept_Vectors_CVPR_2026_paper | ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors | [
"Liming Kuang",
"Yordanka Velikova",
"Mahdi Saleh",
"Jan-Nico Zaech",
"Danda Pani Paudel",
"Benjamin Busam"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kuang_ConceptPose_Training-Free_Zero-Shot_Object_Pose_Estimation_using_Concept_Vectors_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kuang_ConceptPose_Training-Free_Zero-Shot_Object_Pose_Estimation_using_Concept_Vectors_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kuang_ConceptPose_Training-Free_Zero-Shot_CVPR_2026_supplemental.pdf | 2512.09056 | cvf | @InProceedings{Kuang_2026_CVPR,
author = {Kuang, Liming and Velikova, Yordanka and Saleh, Mahdi and Zaech, Jan-Nico and Paudel, Danda Pani and Busam, Benjamin},
title = {ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors},
booktitle = {Proceedings of the IEEE/CVF Confer... | Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurrently, large-scale vision language models show remarkable zero-shot capabilities. In this work, we bridge these two worlds by introducing ConceptPose, a framework for objec... |
Yang_JoPPO_Hierarchical_Photography_Assessment_via_Contrastive_Joint_Conditional_Probabilistic_Reinforcement_CVPR_2026_paper | JoPPO: Hierarchical Photography Assessment via Contrastive Joint Conditional Probabilistic Reinforcement Learning | [
"Yifan Yang",
"Juntuo Wang",
"Yuming Qiao",
"Xudong Zhang",
"Chunyang Yu",
"Yan Li",
"Xiao Lin",
"Liang Luo",
"Dan Meng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Yang_JoPPO_Hierarchical_Photography_Assessment_via_Contrastive_Joint_Conditional_Probabilistic_Reinforcement_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yang_JoPPO_Hierarchical_Photography_Assessment_via_Contrastive_Joint_Conditional_Probabilistic_Reinforcement_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Yang_JoPPO_Hierarchical_Photography_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Yang_2026_CVPR,
author = {Yang, Yifan and Wang, Juntuo and Qiao, Yuming and Zhang, Xudong and Yu, Chunyang and Li, Yan and Lin, Xiao and Luo, Liang and Meng, Dan},
title = {JoPPO: Hierarchical Photography Assessment via Contrastive Joint Conditional Probabilistic Reinforcement Learning},
... | With the advancement of Vision-Language Models (VLMs), employing VLM-as-a-Judge for visual evaluation has become a widely adopted metric in vision research. However, existing VLM-as-a-Judge approaches suffer from biased scoring outcomes with low discrimination and lack the capacity for unified multi-attribute compositi... |
Zhang_Fine-VAD_Towards_Fine-Grained_Video_Anomaly_Detection_via_Progressive_Cross-Granularity_Learning_CVPR_2026_paper | Fine-VAD: Towards Fine-Grained Video Anomaly Detection via Progressive Cross-Granularity Learning | [
"Menghao Zhang",
"Yiyan Zhu",
"Pengfei Ren",
"Haifeng Sun",
"Qi Qi",
"Zirui Zhuang",
"Huazheng Wang",
"Lei Zhang",
"Jianxin Liao",
"Jingyu Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_Fine-VAD_Towards_Fine-Grained_Video_Anomaly_Detection_via_Progressive_Cross-Granularity_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_Fine-VAD_Towards_Fine-Grained_Video_Anomaly_Detection_via_Progressive_Cross-Granularity_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_Fine-VAD_Towards_Fine-Grained_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Menghao and Zhu, Yiyan and Ren, Pengfei and Sun, Haifeng and Qi, Qi and Zhuang, Zirui and Wang, Huazheng and Zhang, Lei and Liao, Jianxin and Wang, Jingyu},
title = {Fine-VAD: Towards Fine-Grained Video Anomaly Detection via Progressive Cross-Granularity L... | In this paper, we explore video anomaly detection (VAD) from a fine-grained perspective, which aims not only to detect anomalous events but also to identify their specific categories. Due to the limited number of examples per category, existing methods either fail to handle intra-class variation across diverse contexts... |
Li_Functional_Mean_Flow_in_Hilbert_Space_CVPR_2026_paper | Functional Mean Flow in Hilbert Space | [
"Zhiqi Li",
"Yuchen Sun",
"Greg Turk",
"Bo Zhu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_Functional_Mean_Flow_in_Hilbert_Space_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_Functional_Mean_Flow_in_Hilbert_Space_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_Functional_Mean_Flow_CVPR_2026_supplemental.pdf | 2511.12898 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Zhiqi and Sun, Yuchen and Turk, Greg and Zhu, Bo},
title = {Functional Mean Flow in Hilbert Space},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
pa... | We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domains by providing a theoretical formulation for Functional Flow Matching and a practical implementation for efficient training and sampling. W... |
Tang_CausalVAD_De-confounding_End-to-End_Autonomous_Driving_via_Causal_Intervention_CVPR_2026_paper | CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention | [
"Jiacheng Tang",
"Zhiyuan Zhou",
"Zhuolin He",
"Jia Zhang",
"Kai Zhang",
"Jian Pu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Tang_CausalVAD_De-confounding_End-to-End_Autonomous_Driving_via_Causal_Intervention_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Tang_CausalVAD_De-confounding_End-to-End_Autonomous_Driving_via_Causal_Intervention_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Tang_CausalVAD_De-confounding_End-to-End_CVPR_2026_supplemental.pdf | 2603.18561 | cvf | @InProceedings{Tang_2026_CVPR,
author = {Tang, Jiacheng and Zhou, Zhiyuan and He, Zhuolin and Zhang, Jia and Zhang, Kai and Pu, Jian},
title = {CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads to causal confusion, where models exploit dataset biases as shortcuts, critically harming their reliability and safety in complex scenarios... |
Tenison_AdaBet_Gradient-free_Layer_Selection_for_Efficient_Training_of_Deep_Neural_CVPR_2026_paper | AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks | [
"Irene Tenison",
"Soumyajit Chatterjee",
"Fahim Kawsar",
"Mohammad Malekzadeh"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Tenison_AdaBet_Gradient-free_Layer_Selection_for_Efficient_Training_of_Deep_Neural_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Tenison_AdaBet_Gradient-free_Layer_Selection_for_Efficient_Training_of_Deep_Neural_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Tenison_AdaBet_Gradient-free_Layer_CVPR_2026_supplemental.pdf | 2510.03101 | cvf | @InProceedings{Tenison_2026_CVPR,
author = {Tenison, Irene and Chatterjee, Soumyajit and Kawsar, Fahim and Malekzadeh, Mohammad},
title = {AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited compute and memory resources. On-device retraining with a target dataset can facilitate such adaptations; however, it remains impractical due... |
Kim_Dual_Ascent_Diffusion_for_Inverse_Problems_CVPR_2026_paper | Dual Ascent Diffusion for Inverse Problems | [
"Minseo Kim",
"Axel Levy",
"Gordon Wetzstein"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kim_Dual_Ascent_Diffusion_for_Inverse_Problems_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kim_Dual_Ascent_Diffusion_for_Inverse_Problems_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kim_Dual_Ascent_Diffusion_CVPR_2026_supplemental.pdf | 2505.17353 | cvf | @InProceedings{Kim_2026_CVPR,
author = {Kim, Minseo and Levy, Axel and Wetzstein, Gordon},
title = {Dual Ascent Diffusion for Inverse Problems},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
... | Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a powerful prior for solving these problems. Existing maximum-a-posteriori (MAP) or posterior sampling approaches, however, rely on different computational approximations, leading ... |
Bianchi_One_Patch_to_Caption_Them_All_A_Unified_Zero-Shot_Captioning_CVPR_2026_paper | One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework | [
"Lorenzo Bianchi",
"Giacomo Pacini",
"Fabio Carrara",
"Nicola Messina",
"Giuseppe Amato",
"Fabrizio Falchi"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Bianchi_One_Patch_to_Caption_Them_All_A_Unified_Zero-Shot_Captioning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Bianchi_One_Patch_to_Caption_Them_All_A_Unified_Zero-Shot_Captioning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Bianchi_One_Patch_to_CVPR_2026_supplemental.pdf | 2510.02898 | cvf | @InProceedings{Bianchi_2026_CVPR,
author = {Bianchi, Lorenzo and Pacini, Giacomo and Carrara, Fabio and Messina, Nicola and Amato, Giuseppe and Falchi, Fabrizio},
title = {One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually decoding a text-aligned image feature, but they limit their scope to global representations and whole-im... |
Xu_ReGenHOI_Unifying_Reconstruction_and_Generation_for_3D_Human-Object_Interaction_Understanding_CVPR_2026_paper | ReGenHOI: Unifying Reconstruction and Generation for 3D Human-Object Interaction Understanding | [
"Miao Xu",
"Xiangyu Zhu",
"Zidu Wang",
"Xusheng Liang",
"Bao Li",
"Jinlin Wu",
"Zelin Zang",
"Zhen Lei"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xu_ReGenHOI_Unifying_Reconstruction_and_Generation_for_3D_Human-Object_Interaction_Understanding_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xu_ReGenHOI_Unifying_Reconstruction_and_Generation_for_3D_Human-Object_Interaction_Understanding_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xu_ReGenHOI_Unifying_Reconstruction_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Xu_2026_CVPR,
author = {Xu, Miao and Zhu, Xiangyu and Wang, Zidu and Liang, Xusheng and Li, Bao and Wu, Jinlin and Zang, Zelin and Lei, Zhen},
title = {ReGenHOI: Unifying Reconstruction and Generation for 3D Human-Object Interaction Understanding},
booktitle = {Proceedings of the IEEE/... | Understanding 3D human-object interaction (HOI) involves two highly-related abilities: reconstruction, which perceives observed geometry, and generation, which imagines plausible future interactions. However, most existing methods treat these abilities as separate tasks, limiting their capacity to capture the unified n... |
Tang_Masked_Region_Transformer_for_Layered_Image_Generation_and_Editing_at_CVPR_2026_paper | Masked Region Transformer for Layered Image Generation and Editing at Scale | [
"Zhicong Tang",
"Jingye Chen",
"Zhao Zhang",
"Mohan Zhou",
"Yuchi Liu",
"Yifan Pu",
"Yalong Bai",
"Ethan Smith",
"Yuhui Yuan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Tang_Masked_Region_Transformer_for_Layered_Image_Generation_and_Editing_at_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Tang_Masked_Region_Transformer_for_Layered_Image_Generation_and_Editing_at_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Tang_Masked_Region_Transformer_CVPR_2026_supplemental.pdf | 2605.27235 | title_judge | @InProceedings{Tang_2026_CVPR,
author = {Tang, Zhicong and Chen, Jingye and Zhang, Zhao and Zhou, Mohan and Liu, Yuchi and Pu, Yifan and Bai, Yalong and Smith, Ethan and Yuan, Yuhui},
title = {Masked Region Transformer for Layered Image Generation and Editing at Scale},
booktitle = {Proceedings of th... | Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, analogous to word-level editing in natural language. Despite its importance, this remains an underexplored area at scale. To address this gap, we present MRT, a 20B-param... |
Cheng_Grounded_3D-Aware_Spatial_Vision-Language_Modeling_CVPR_2026_paper | Grounded 3D-Aware Spatial Vision-Language Modeling | [
"An-Chieh Cheng",
"Yang Fu",
"Yatai Ji",
"Ligeng Zhu",
"Guanqi Zhan",
"Zhuoyang Zhang",
"Zhaojing Yang",
"Song Han",
"Yao Lu",
"Pavlo Molchanov",
"Vidya Nariyambut Murali",
"Jan Kautz",
"Xiaolong Wang",
"Hongxu Yin",
"Sifei Liu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Cheng_Grounded_3D-Aware_Spatial_Vision-Language_Modeling_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cheng_Grounded_3D-Aware_Spatial_Vision-Language_Modeling_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Cheng_Grounded_3D-Aware_Spatial_CVPR_2026_supplemental.pdf | 2605.30307 | title_snapshot | @InProceedings{Cheng_2026_CVPR,
author = {Cheng, An-Chieh and Fu, Yang and Ji, Yatai and Zhu, Ligeng and Zhan, Guanqi and Zhang, Zhuoyang and Yang, Zhaojing and Han, Song and Lu, Yao and Molchanov, Pavlo and Murali, Vidya Nariyambut and Kautz, Jan and Wang, Xiaolong and Yin, Hongxu and Liu, Sifei},
title ... | We present GR3D, a spatial vision language model equipped with three complementary grounding capabilities--explicit 2D grounding, implicit 2D grounding, and monocular 3D grounding--within a single framework. GR3D introduces an implicit grounding mechanism that identifies entity mentions during generation and inserts th... |
Xiang_Fine-Grained_Post-Training_Quantization_for_Large_Vision_Language_Models_with_Quantization-Aware_CVPR_2026_paper | Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients | [
"Ziwei Xiang",
"Fanhu Zeng",
"Hongjian Fang",
"Rui-Qi Wang",
"Renxing Chen",
"Yanan Zhu",
"Yi Chen",
"Peipei Yang",
"Xu-Yao Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xiang_Fine-Grained_Post-Training_Quantization_for_Large_Vision_Language_Models_with_Quantization-Aware_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xiang_Fine-Grained_Post-Training_Quantization_for_Large_Vision_Language_Models_with_Quantization-Aware_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xiang_Fine-Grained_Post-Training_Quantization_CVPR_2026_supplemental.pdf | 2603.17809 | cvf | @InProceedings{Xiang_2026_CVPR,
author = {Xiang, Ziwei and Zeng, Fanhu and Fang, Hongjian and Wang, Rui-Qi and Chen, Renxing and Zhu, Yanan and Chen, Yi and Yang, Peipei and Zhang, Xu-Yao},
title = {Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrate... | Large Vision Language Models (LVLMs) have achieved remarkable success in a wide range of downstream tasks that require multimodal interaction, but their powerful capabilities come with substantial computational and memory overhead, which hinders practical deployment. Among numerous acceleration techniques, post-trainin... |
Sun_Dr._Seg_Revisiting_GRPO_Training_for_Visual_Large_Language_Models_CVPR_2026_paper | Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design | [
"Haoxiang Sun",
"Tao Wang",
"Chenwei Tang",
"Li Yuan",
"Jiancheng Lv"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Sun_Dr._Seg_Revisiting_GRPO_Training_for_Visual_Large_Language_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Sun_Dr._Seg_Revisiting_GRPO_Training_for_Visual_Large_Language_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Sun_Dr._Seg_Revisiting_CVPR_2026_supplemental.pdf | 2603.00152 | cvf | @InProceedings{Sun_2026_CVPR,
author = {Sun, Haoxiang and Wang, Tao and Tang, Chenwei and Yuan, Li and Lv, Jiancheng},
title = {Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Following the success of Group Relative Policy Optimization (GRPO) in foundation LLMs, an increasing number of works have sought to adapt GRPO to Visual Large Language Models (VLLMs) for visual perception tasks (e.g., detection and segmentation). However, much of this line of research rests on a long-standing yet unexa... |
Wang_GR-Gauge_Cost-efficient_Training_Configuration_By_Gauging_the_Gradient_Redundancy_CVPR_2026_paper | GR-Gauge: Cost-efficient Training Configuration By Gauging the Gradient Redundancy | [
"Guanjie Wang",
"Chen Chen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wang_GR-Gauge_Cost-efficient_Training_Configuration_By_Gauging_the_Gradient_Redundancy_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wang_GR-Gauge_Cost-efficient_Training_Configuration_By_Gauging_the_Gradient_Redundancy_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wang_GR-Gauge_Cost-efficient_Training_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Wang_2026_CVPR,
author = {Wang, Guanjie and Chen, Chen},
title = {GR-Gauge: Cost-efficient Training Configuration By Gauging the Gradient Redundancy},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | The recent success of artificial intelligence motivates many non-professional users to train their own models. Those users often resort to cloud training services, seeking to obtain a sufficiently accurate model at a modest cost, for which properly setting up the learning rate and batch size is crucial. While various H... |
Li_DROID-SLAM_in_the_Wild_CVPR_2026_paper | DROID-SLAM in the Wild | [
"Moyang Li",
"Zihan Zhu",
"Marc Pollefeys",
"Daniel Barath"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_DROID-SLAM_in_the_Wild_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_DROID-SLAM_in_the_Wild_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_DROID-SLAM_in_the_CVPR_2026_supplemental.pdf | 2603.19076 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Moyang and Zhu, Zihan and Pollefeys, Marc and Barath, Daniel},
title = {DROID-SLAM in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
pages... | We present a robust, real-time RGB SLAM system that handles dynamic environments by leveraging differentiable Uncertainty-aware Bundle Adjustment. Traditional SLAM methods typically assume static scenes, leading to tracking failures in the presence of motion. Recent dynamic SLAM approaches attempt to address this chall... |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 210