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arxiv: 2504.10685 · v1 · pith:AA77K6SZ · submitted 2025-04-14 · cs.CV · cs.AI

NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

Yuqian Fu , Xingyu Qiu , Bin Ren , Yanwei Fu , Radu Timofte , Nicu Sebe , Ming-Hsuan Yang , Luc Van Gool
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Kaijin Zhang Qingpeng Nong Xiugang Dong Hong Gao Xiangsheng Zhou Jiancheng Pan Yanxing Liu Xiao He Jiahao Li Yuze Sun Xiaomeng Huang Zhenyu Zhang Ran Ma Yuhan Liu Zijian Zhuang Shuai Yi Yixiong Zou Lingyi Hong Mingxi Chen Runze Li Xingdong Sheng Wenqiang Zhang Weisen Chen Yongxin Yan Xinguo Chen Yuanjie Shao Zhengrong Zuo Nong Sang Hao Wu Haoran Sun Shuming Hu Yan Zhang Zhiguang Shi Yu Zhang Chao Chen Tao Wang Da Feng Linhai Zhuo Ziming Lin Yali Huang Jie Me Yiming Yang Mi Guo Mingyuan Jiu Mingliang Xu Maomao Xiong Qunshu Zhang Xinyu Cao Yuqing Yang Dianmo Sheng Xuanpu Zhao Zhiyu Li Xuyang Ding Wenqian Li
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classification cs.CV cs.AI
keywords challengedetectionobjectcd-fsodfew-shotntireparticipantsresults
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Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st CD-FSOD Challenge, aiming to advance the performance of current object detectors on entirely novel target domains with only limited labeled data. The challenge attracted 152 registered participants, received submissions from 42 teams, and concluded with 13 teams making valid final submissions. Participants approached the task from diverse perspectives, proposing novel models that achieved new state-of-the-art (SOTA) results under both open-source and closed-source settings. In this report, we present an overview of the 1st NTIRE 2025 CD-FSOD Challenge, highlighting the proposed solutions and summarizing the results submitted by the participants.

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