Multi-Stage Contrastive Regression for Action Quality Assessment
This work improves action quality assessment for applications like sports or medical training, but it is incremental as it builds on existing contrastive learning and segmentation techniques.
The paper tackles the problem of video-based action quality assessment by addressing the oversight of stage-level characteristics in existing methods, resulting in state-of-the-art performance on a fine-grained dataset.
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.