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Pairwise Contrastive Learning Network for Action Quality Assessment

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Considering the complexity of modeling diverse actions of athletes, action quality assessment (AQA) in sports is a challenging task. A common solution is to tackle this problem as a regression task that map the input video to the final score provided by referees. However, it ignores the subtle and critical difference between videos. To address this problem, a new pairwise contrastive learning network (PCLN) is proposed to concern these differences and form an end-to-end AQA model with basic regression network. Specifically, the PCLN encodes video pairs to learn relative scores between videos to improve the performance of basic regression network. Furthermore, a new consistency constraint is defined to guide the training of the proposed AQA model. In the testing phase, only the basic regression network is employed, which makes the proposed method simple but high accuracy. The proposed method is verified on the AQA-7 and MTL-AQA datasets. Several ablation studies are built to verify the effectiveness of each component in the proposed method. The experimental results show that the proposed method achieves the state-of-the-art performance.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 61871196, 62001176); Natural Science Foundation of Fujian Province of China (No. 2019J01082, 2020J01085, 2022J01317); Scientific Research Funds of Huaqiao University (No. 21BS122) and the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No. ZQN-YX601).

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Correspondence to Hong-Bo Zhang .

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Li, M., Zhang, HB., Lei, Q., Fan, Z., Liu, J., Du, JX. (2022). Pairwise Contrastive Learning Network for Action Quality Assessment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_27

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