Hierarchical NeuroSymbolic Approach for Action Quality Assessment

arXiv:2403.13798v1 Announce Type: cross
Abstract: 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. …

abstract arxiv assessment computer computer vision cs.ai cs.cv cs.lg cs.sc current ground-truth hierarchical human performance quality transparency truth type vision