Multi-Stage Contrastive Regression for Action Quality Assessment

Qi An; Mengshi Qi; Huadong Ma

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arXiv · 2401.02841

Multi-Stage Contrastive Regression for Action Quality Assessment

Qi An·Mengshi Qi·Huadong Ma

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Abstract

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.

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Qi An, Mengshi Qi, Huadong Ma. 2024-01-05. Multi-Stage Contrastive Regression for Action Quality Assessment. https://arxiv.org/abs/2401.02841

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