A Figure Skating Jumping Dataset for Replay-Guided Action Quality Assessment

In actual competitive sports, judges often scrutinize replay videos from multiple views to adjudicate uncertain or contentious actions, and ultimately ascertain the definitive score. Most existing action quality assessment methods regress from a single video or a pair-wise exemplar and input videos, which are limited by the viewpoint and zoom scale of videos. To end this, we construct a Replay Figure Skating Jumping dataset (RFSJ), containing additional view information provided by the post-match replay video and fine-grained annotations. We also propose a Replay-Guided approach for action quality assessment, learned by a Triple-Stream Contrastive Transformer with a Temporal Concentration Module. Specifically, besides the pairwise input and exemplar, we contrast the input and its replay by an extra contrastive module. Then the consistency of scores guides the model to learn features of the same action under different views and zoom scales. In addition, based on the fact that errors or highlight moments of athletes are crucial factors affecting scoring, these moments are concentrated in parts of the video rather than a uniform distribution. The proposed temporal concentration module encourages the model to concentrate on these features, then cooperates with the contrastive regression module to obtain an effective scoring mechanism. Extensive experiments demonstrate that our method achieves Spearman's Rank Correlation of 0.9346 on the proposed RFSJ dataset, improving over the existing state-of-the-art methods.