用因果模型提升体育动作质量评估的可解释性与准确性
FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
- 基于图注意力网络构建因果干预模块，分离人体特征与背景干扰
- 引入时序因果注意力，捕捉动作各阶段的细微时间依赖关系
- 输出透明评分依据，适合需要可解释性的运动分析场景
动作质量评估对竞技体育中的表现评价、训练优化和安全监控至关重要。现有深度学习方法常作为黑箱模型，易受虚假相关影响，降低可靠性和可解释性。本文提出FineCausal，一种基于因果推理的新框架，在FineDiving-HM数据集上达到当前最优性能。该方法通过图注意力网络构建的因果干预模块，分离人体主导的前景特征与背景混杂因素，并引入时序因果注意力模块，捕捉动作不同阶段间的精细时序依赖。双重机制生成细粒度时空表征，不仅实现领先评分效果，还能提供透明、可解释的评估依据。尽管表现优异，该方法需依赖专家定义因果结构且依赖高质量标注，这被列为未来研究方向。代码已开源。
原文摘要 · Abstract (English)
Action quality assessment (AQA) is critical for evaluating athletic performance, informing training strategies, and ensuring safety in competitive sports. However, existing deep learning approaches often operate as black boxes and are vulnerable to spurious correlations, limiting both their reliability and interpretability. In this paper, we introduce FineCausal, a novel causal-based framework that achieves state-of-the-art performance on the FineDiving-HM dataset. Our approach leverages a Graph Attention Network-based causal intervention module to disentangle human-centric foreground cues from background confounders, and incorporates a temporal causal attention module to capture fine-grained temporal dependencies across action stages. This dual-module strategy enables FineCausal to generate detailed spatio-temporal representations that not only achieve state-of-the-art scoring performance but also provide transparent, interpretable feedback on which features drive the assessment. Despite its strong performance, FineCausal requires extensive expert knowledge to define causal structures and depends on high-quality annotations, challenges that we discuss and address as future research directions. Code is available at https://github.com/Harrison21/FineCausal.
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