Interpretable Long-term Action Quality Assessment
<|reference_start|>Interpretable Long-term Action Quality Assessment: 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 modeling 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 designed to approximate the scoring patterns observed in human judgments 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<|reference_end|>
@article{dong2024interpretable,
    title={Interpretable Long-term Action Quality Assessment},
    author={Xu Dong, Xinran Liu, Wanqing Li, Anthony Adeyemi-Ejeye, Andrew Gilbert},
    journal={arXiv preprint arXiv:2408.11687},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11687},
    primaryClass={cs.CV}
}