  In recent years, there has been growing interest in the video-based action
quality assessment (AQA). Most existing methods typically solve AQA problem by
considering the entire video yet overlooking the inherent stage-level
characteristics of actions. To address this issue, we design a novel
Multi-stage Contrastive Regression (MCoRe) framework for the AQA task. This
approach allows us to efficiently extract spatial-temporal information, while
simultaneously reducing computational costs by segmenting the input video into
multiple stages or procedures. Inspired by the graph contrastive learning, we
propose a new stage-wise contrastive learning loss function to enhance
performance. As a result, MCoRe demonstrates the state-of-the-art result so far
on the widely-adopted fine-grained AQA dataset.
