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Semi-supervised Teacher-Reference-Student Architecture for Action Quality Assessment

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15132))

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Abstract

Existing action quality assessment (AQA) methods often require a large number of label annotations for fully supervised learning, which are laborious and expensive. In practice, the labeled data are difficult to obtain because the AQA annotation process requires domain-specific expertise. In this paper, we propose a novel semi-supervised method, which can be utilized for better assessment of the AQA task by exploiting a large amount of unlabeled data and a small portion of labeled data. Differing from the traditional teacher-student network, we propose a teacher-reference-student architecture to learn both unlabeled and labeled data, where the teacher network and the reference network are used to generate pseudo-labels for unlabeled data to supervise the student network. Specifically, the teacher predicts pseudo-labels by capturing high-level features of unlabeled data. The reference network provides adequate supervision of the student network by referring to additional action information. Moreover, we introduce confidence memory to improve the reliability of pseudo-labels by storing the most accurate ever output of the teacher network and reference network. To validate our method, we conduct extensive experiments on three AQA benchmark datasets. Experimental results show that our method achieves significant improvements and outperforms existing semi-supervised AQA methods. Our source code is available at https://github.com/wuli55555/TRS.

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Acknowledgements

This work is partly supported by the Funds for the Innovation Research Group Project of the NSFC under Grant 61921003, the NSFC Project under Grant 62202063, Beijing Natural Science Foundation (No.L243027) and 111 Project under Grant B18008.

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Wulian Yun, Mengshi Qi, Fei Peng & Huadong Ma

Authors
  1. Wulian Yun
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  2. Mengshi Qi
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  3. Fei Peng
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  4. Huadong Ma
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Corresponding author

Correspondence to Wulian Yun .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Yun, W., Qi, M., Peng, F., Ma, H. (2025). Semi-supervised Teacher-Reference-Student Architecture for Action Quality Assessment. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15132. Springer, Cham. https://doi.org/10.1007/978-3-031-72904-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-72904-1_10

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