Learning and fusing multiple hidden substages for action quality assessment

Many of the existing methods for action quality assessment implement single-stage score regression networks that lack pertinence and rationality for the evaluation task. In this work, our target is to find a reasonable action quality assessment method for sports competitions that conforms to objective evaluation rules and field experience. To achieve this goal, three assessment scenarios, i.e., the overall-score-guided scenario, execution-score-guided scenario, and difficulty-level-based overall-score-guided scenario, are defined. A learning and fusion network of multiple hidden substages is proposed to assess athletic performance by segmenting videos into five substages by a temporal semantic segmentation. The feature of each video segment is extracted from the five feature backbone networks with shared weights, and a fully-connected-network-based hidden regression model is built to predict the score of each substage, fusing these scores into the overall score. We evaluate the proposed method on the UNLV-Diving dataset. The comparison results show that the proposed method based on objective evaluation rules of sports competitions outperforms the regression model directly trained on the overall score. The proposed multiple-substage network is more accurate than the single-stage score regression network and achieves state-of-the-art performance by leveraging objective evaluation rules and field experience that are beneficial for building an accurate and reasonable action quality assessment model.