FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment
Jinglin Xu, Yongming Rao, Xumin Yu, Guangyi Chen, Jie Zhou, Jiwen Lu

TL;DR
This paper introduces FineDiving, a detailed dataset with annotations for diving actions, and a procedure-aware model that improves action quality assessment by understanding internal temporal structures, leading to more accurate and interpretable scores.
Contribution
The paper presents a new fine-grained dataset and a novel procedure-aware approach with Temporal Segmentation Attention for more reliable and interpretable action quality assessment.
Findings
Achieves significant improvements over state-of-the-art methods.
Provides better interpretability through internal temporal structure understanding.
Demonstrates effectiveness on diverse diving events.
Abstract
Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability. We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. Towards this goal, we construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures. We also propose a procedure-aware approach for action quality assessment, learned by a new Temporal Segmentation Attention module. Specifically, we propose to parse pairwise query and exemplar action instances into consecutive steps with diverse semantic and temporal correspondences. The procedure-aware cross-attention is proposed to…
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Taxonomy
TopicsHuman Pose and Action Recognition · Sport Psychology and Performance · Anomaly Detection Techniques and Applications
