Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
This addresses the problem of explainable and objective scoring in sports like diving for judges, trainers, and athletes, though it is incremental as it adapts neuro-symbolic methods to a specific domain.
The paper tackles the lack of transparency and bias in action quality assessment (AQA) by introducing a neuro-symbolic paradigm that uses neural networks to extract interpretable symbols and applies rules for scoring, achieving state-of-the-art action recognition and temporal segmentation in diving as a case study.
Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://github.com/laurenok24/NSAQA.