FineCausal：一个用于可解释细粒度动作质量评估的基于因果的框架
FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
AI总结：
提出基于因果的框架FineCausal，通过图注意力因果干预和时间因果注意力模块解耦背景混杂因素并捕捉细粒度时间依赖，在FineDiving-HM数据集上实现最先进性能且提供可解释反馈。
摘要：
动作质量评估（AQA）对于评估运动表现、指导训练策略以及确保竞技体育中的安全至关重要。然而，现有的深度学习方法通常作为黑盒运行，并且容易受到伪相关性的影响，限制了其可靠性和可解释性。在本文中，我们引入了FineCausal，一种新颖的基于因果的框架，在FineDiving-HM数据集上实现了最先进的性能。我们的方法利用基于图注意力网络的因果干预模块，将以人为本的前景线索从背景混杂因素中解耦，并引入时间因果注意力模块，以捕捉跨动作阶段的细粒度时间依赖性。这种双模块策略使FineCausal能够生成详细的时空表示，不仅实现了最先进的评分性能，还提供了关于哪些特征驱动评估的透明、可解释的反馈。尽管性能强劲，FineCausal需要广泛的专家知识来定义因果结构，并依赖高质量标注，这些挑战我们已讨论并作为未来研究方向加以解决。代码可在https://github.com/Harrison21/FineCausal获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
