Interpretable Long-term Action Quality Assessment


Xu Dong (University of Surrey), Xinran Liu (University of Surrey), Wanqing Li (University of Wollongong), Anthony Adeyemi-Ejeye (University of Surrey), Andrew Gilbert (University of Surrey)
The 35th British Machine Vision Conference
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Abstract

Long-term Action Quality Assessment (AQA) evaluates the execution of activities in videos. However, the length presents challenges in fine-grained interpretability, with current AQA methods typically producing a single score by averaging clip features, lacking detailed semantic meanings of individual clips. Long-term videos pose additional difficulty due to the complexity and diversity of actions, exacerbating interpretability challenges. While query-based transformer networks offer promising long-term modelling capabilities, their interpretability in AQA remains unsatisfactory due to a phenomenon we term Temporal Skipping, where the model skips self-attention layers to prevent output degradation. To address this, we propose an attention loss function and a query initialization method to enhance performance and interpretability. Additionally, we introduce a weight-score regression module to follow human judges' scoring logic and replace conventional single-score regression, improving the rationality of interpretability. Our approach achieves state-of-the-art results on three real-world, long-term AQA benchmarks. Our code is available at: https://github.com/dx199771/Interpretability-AQA

Citation

@inproceedings{Dong_2024_BMVC,
author    = {Xu Dong and Xinran Liu and Wanqing Li and Anthony Adeyemi-Ejeye and Andrew Gilbert},
title     = {Interpretable Long-term Action Quality Assessment},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0517.pdf}
}


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