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

• URL: http://arxiv.org/abs/2504.10685v1
• Date: Mon, 14 Apr 2025 20:17:27 GMT
• Title: NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
• Authors: Yuqian Fu, Xingyu Qiu, Bin Ren, Yanwei Fu, Radu Timofte, Nicu Sebe, Ming-Hsuan Yang, Luc Van Gool, 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,
• Abstract summary: Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection models when applied across domains.<n>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.<n>We present an overview of the 1st NTIRE 2025 CD-FSOD Challenge, highlighting the proposed solutions and summarizing the results submitted by the participants.
• Score: 191.59142290750043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results [162.7095344078484]
  We present an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.
  arXiv  Detail & Related papers  (2025-04-16T18:06:16Z)
• The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report [170.81876816944754]
  The NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR) aims to advance the development of models that optimize key computational metrics.<n>This paper meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
  arXiv  Detail & Related papers  (2025-04-14T20:18:21Z)
• Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks [62.443665295250035]
  We present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023) In total, 32 competing teams register for the challenge, from which we received 11 successful submissions.
  arXiv  Detail & Related papers  (2024-07-20T10:13:54Z)
• Few-Shot Object Detection: Research Advances and Challenges [15.916463121997843]
  Few-shot object detection (FSOD) combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years.
  arXiv  Detail & Related papers  (2024-04-07T03:37:29Z)
• NICE: CVPR 2023 Challenge on Zero-shot Image Captioning [149.28330263581012]
  NICE project is designed to challenge the computer vision community to develop robust image captioning models. Report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries.
  arXiv  Detail & Related papers  (2023-09-05T05:32:19Z)
• MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results [24.73863448239332]
  This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.
  arXiv  Detail & Related papers  (2023-07-18T10:52:24Z)
• ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition [20.33359041243155]
  The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts. We provide a dataset of 2647 artworks annotated with 20 120 tightly fit bounding boxes. A test set containing 1140 artworks and 15 480 annotations is kept private for the challenge evaluation.
  arXiv  Detail & Related papers  (2023-01-24T09:35:43Z)
• DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results [144.5252578415748]
  This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, with 60,000 videos constituted by a total of 17.6 million frames. A total of 115 participants registered for the competition, and 25 teams made valid submissions.
  arXiv  Detail & Related papers  (2021-02-18T16:48:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.