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A Comprehensive Review of Few-Shot Action Recognition

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Abstract

Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data in action recognition. It requires accurately classifying human actions in videos using only a few labeled examples per class. Compared to few-shot learning in image scenarios, few-shot action recognition is more challenging due to the intrinsic complexity of video data. Numerous approaches have driven significant advancements in few-shot action recognition, which underscores the need for a comprehensive survey. Unlike early surveys that focus on few-shot image or text classification, we deeply consider the unique challenges of few-shot action recognition. In this survey, we provide a comprehensive review of recent methods and introduce a novel and systematic taxonomy of existing approaches, accompanied by a detailed analysis. We categorize the methods into generative-based and meta-learning frameworks, and further elaborate on the methods within the meta-learning framework, covering aspects: video instance representation, category prototype learning, and generalized video alignment. Additionally, the survey presents the commonly used benchmarks and discusses relevant advanced topics and promising future directions. We hope this survey can serve as a valuable resource for researchers, offering essential guidance to newcomers and stimulating seasoned researchers with fresh insights.

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Data Availability

This survey introduces commonly used datasets for FSAR, summarized in Section 3.3. These publicly available datasets include HMDB (https://serre-lab.clps.brown.edu/resource/hmdb-a-largehuman-motion-database), UCF101 (https://www.crcv.ucf.edu/data/UCF101.php), Kinetics (https://github.com/cvdfoundation/kinetics-dataset), SSv2 (https://20bn.com/datasets/something-something), and EPIC-Kitchens (https://epic-kitchens.github.io/2021).

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants U23A20387, 62322212, in part by Pengcheng Laboratory Research Project under Grant PCL2023A08, in part by Alibaba Innovative Research Program, and also in part by CAS Project for Young Scientists in Basic Research (YSBR-116).

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  1. State Key Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yuyang Wanyan, Xiaoshan Yang, Weiming Dong & Changsheng Xu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 101408, China

    Yuyang Wanyan, Xiaoshan Yang, Weiming Dong & Changsheng Xu

  3. Peng Cheng Laboratory, Shenzhen, 518066, China

    Xiaoshan Yang & Changsheng Xu

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Wanyan, Y., Yang, X., Dong, W. et al. A Comprehensive Review of Few-Shot Action Recognition. Int J Comput Vis 133, 6832–6859 (2025). https://doi.org/10.1007/s11263-025-02503-6

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