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Most existing action quality assessment methods rely on the deep features ofan entire video to predict the score, which is less reliable due to thenon-transparent inference process and poor interpretability. We argue thatunderstanding both high-level semantics and internal temporal structures ofactions in competitive sports videos is the key to making predictions accurateand interpretable. Towards this goal, we construct a new fine-grained dataset,called FineDiving, developed on diverse diving events with detailed annotationson action procedures. We also propose a procedure-aware approach for actionquality assessment, learned by a new Temporal Segmentation Attention module.Specifically, we propose to parse pairwise query and exemplar action instancesinto consecutive steps with diverse semantic and temporal correspondences. Theprocedure-aware cross-attention is proposed to learn embeddings between queryand exemplar steps to discover their semantic, spatial, and temporalcorrespondences, and further serve for fine-grained contrastive regression toderive a reliable scoring mechanism. Extensive experiments demonstrate that ourapproach achieves substantial improvements over state-of-the-art methods withbetter interpretability. The dataset and code are available at\url{https://github.com/xujinglin/FineDiving}.

FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment