Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive Regression

TL;DR

This paper introduces a hierarchical pose-guided multi-stage contrastive regression method for action quality assessment, effectively capturing fine-grained features and sub-action segmentation to improve performance on diving and athletic datasets.

Contribution

The paper proposes a novel multi-stage contrastive regression framework with a multi-scale visual-skeleton encoder and sub-action segmentation, advancing AQA accuracy.

Findings

Outperforms existing methods on FineDiving and MTL-AQA datasets.

Effectively captures fine-grained pose differences and sub-action variations.

Demonstrates the benefit of hierarchical pose guidance and contrastive learning.

Abstract

Action Quality Assessment (AQA), which aims at automatic and fair evaluation of athletic performance, has gained increasing attention in recent years. However, athletes are often in rapid movement and the corresponding visual appearance variances are subtle, making it challenging to capture fine-grained pose differences and leading to poor estimation performance. Furthermore, most common AQA tasks, such as diving in sports, are usually divided into multiple sub-actions, each of which contains different durations. However, existing methods focus on segmenting the video into fixed frames, which disrupts the temporal continuity of sub-actions resulting in unavoidable prediction errors. To address these challenges, we propose a novel action quality assessment method through hierarchically pose-guided multi-stage contrastive regression. Firstly, we introduce a multi-scale dynamic visual-skeleton encoder to capture fine-grained spatio-temporal visual and skeletal features. Then, a procedure segmentation network is introduced to separate different sub-actions and obtain segmented features. Afterwards, the segmented visual and skeletal features are both fed into a multi-modal fusion module as physics structural priors, to guide the model in learning refined activity similarities and variances. Finally, a multi-stage contrastive learning regression approach is employed to learn discriminative representations and output prediction results. In addition, we introduce a newly-annotated FineDiving-Pose Dataset to improve the current low-quality human pose labels. In experiments, the results on FineDiving and MTL-AQA datasets demonstrate the effectiveness and superiority of our proposed approach. Our source code and dataset are available at https://github.com/Lumos0507/HP-MCoRe.