Multi-Stage Action Quality Assessment Method.

In most of the existing mainstream action quality assessment methods, the score regression is performed on a complete action video to obtain the predicted score, which may prevent us from fully exploiting the multi-stage information in action video. In this paper, we attempt to divide a complete action video into clips according to the multiple phases it contains, and predict scores for each segment individually. In order to validate the effectiveness of the method, the FineDiving dataset is further divided into several action categories as the experimental dataset, and the improvement is applied to the mainstream USDL and CoRe methods. The proposed method achieves a significant performance enhancement in the metric of Spearman's correlation, which is commonly used in AQA tasks, thus validating the effectiveness of our proposed method.