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期刊&会议

Conference on Computer Vision and Pattern Recognition · 会议 · Computer Vision

2025-12-11 至 2025-12-11 共收录 3
2404.10378 2025-12-11 cs.CV cs.AI cs.CY cs.LG

Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data

CVPR 2024 第二届合成数据时代人脸识别挑战赛:合成数据时代的人脸识别挑战

Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zhizhou Zhong, Yuge Huang, Yuxi Mi, Shouhong Ding, Shuigeng Zhou, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Zhihong Xiao, Evgeny Smirnov, Anton Pimenov, Aleksei Grigorev, Denis Timoshenko, Kaleb Mesfin Asfaw, Cheng Yaw Low, Hao Liu, Chuyi Wang, Qing Zuo, Zhixiang He, Hatef Otroshi Shahreza, Anjith George, Alexander Unnervik, Parsa Rahimi, Sébastien Marcel, Pedro C. Neto, Marco Huber, Jan Niklas Kolf, Naser Damer, Fadi Boutros, Jaime S. Cardoso, Ana F. Sequeira, Andrea Atzori, Gianni Fenu, Mirko Marras, Vitomir Štruc, Jiang Yu, Zhangjie Li, Jichun Li, Weisong Zhao, Zhen Lei, Xiangyu Zhu, Xiao-Yu Zhang, Bernardo Biesseck, Pedro Vidal, Luiz Coelho, Roger Granada, David Menotti

AI总结 CVPR 2024第二届挑战赛探讨合成数据在人脸识别中的应用,旨在解决隐私、偏见和泛化能力等技术限制,通过新子任务推动面部生成方法的发展。

Comments arXiv admin note: text overlap with arXiv:2311.10476

Journal ref IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)

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2107.05664 2025-12-11 cs.RO cs.AI

Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic

为合作自主车辆的利他性动作规划使用多智能体优势Actor-Critic

Behrad Toghi, Rodolfo Valiente, Dorsa Sadigh, Ramtin Pedarsani, Yaser P. Fallah

AI总结 本文提出一种多智能体优势Actor-Critic算法,用于自动驾驶车辆在混合交通环境中的利他性动作规划,以提升交通效率与安全。

Comments Accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Workshop on Autonomous Driving: Perception, Prediction and Planning

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1911.10496 2025-12-11 cs.CV cs.CL

Two Causal Principles for Improving Visual Dialog

为改进视觉对话的两个因果原则

Jiaxin Qi, Yulei Niu, Jianqiang Huang, Hanwang Zhang

机构 * Nanyang Technological University(南洋理工大学) Renmin University of China(中国人民大学) Damo Academy, Alibaba Group(阿里达摩院)

AI总结 本文提出两个因果原则以改进视觉对话模型,通过移除对话历史的直接输入和消除未观察到的混杂因素,提升模型性能。

Comments Accepted by CVPR 2020

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