  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 fine-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 distributions learning (MUSDL) method to
explore the disentangled components of a score. We conduct experiments on three
AQA datasets containing various Olympic actions and surgical activities, where
our approaches set new state-of-the-arts under the Spearman's Rank Correlation.
