Uncertainty-Aware Score Distribution Learning for Action Quality Assessment

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where finer-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distribution learning (MUSDL) method to explore the disentangled components of a score. In order to demonstrate the effectiveness of our proposed methods, We conduct experiments on two AQA datasets containing various Olympic actions. Our approaches set new state-of-the-arts under the Spearman's Rank Correlation (i.e., 0.8102 on AQA-7 and 0.9273 on MTL-AQA).