Semi-supervised Action Quality Assessment (AQA) aims to predict action execution scores by utilizing limited labeled and massive unlabeled samples. However, existing approaches often oversimplify the modeling process of scene-invariant and fine-grained action sequences for score prediction, which fails to fully leverage rich information available in unlabeled data. Thus, we propose a Vector Quantized Decoupling representation Network (VQD-Net), which decouples sub-action categories and action execution quality to enable a fine-grained understanding of actions for semi-supervised AQA. The proposed VQD-Net effectively captures and learns discriminative features through common semantic representations between labeled and unlabeled samples within a shared embedding space, enabling accurate AQA score prediction. By leveraging the differences between features and embeddings, we achieve more accurate confidence estimates for unlabeled samples and enhance the model performance by selecting reliable pseudo-labels. Experiments on three public AQA datasets, including MTL-AQA, RG, and FineFS, demonstrate that the proposed VQD-Net achieves state-of-the-art performance. The source code is available at https://github.com/Pix0611/VQD-Net.

Action Quality Assessment (AQA) has wide applications in various scenarios. Regarding the AQA of long-term figure skating, the big challenge lies in semantic context feature learning for Program Component Score (PCS) prediction and fine-grained technical subaction analysis for Technical Element Score (TES) prediction. In this paper, we propose a Localization-assisted Uncertainty Score Disentanglement Network (LUSD-Net) to deal with PCS and TES two predictions. In the LUSD-Net, we design an uncertainty score disentanglement solution, including score disentanglement and uncertainty regression, to decouple PCS-oriented and TES-oriented representations from skating sequences, ensuring learning differential representations for two types of score prediction. For long-term feature learning, a temporal interaction encoder is presented to build temporal context relation learning on PCS-oriented and TES-oriented features. To address subactions in TES prediction, a weakly-supervised temporal subaction localization is adopted to locate technical subactions in long sequences. For evaluation, we collect a large-scale Fine-grained Figure Skating dataset (FineFS) involving RGB videos and estimated skeleton sequences, providing rich annotations for multiple downstream action analysis tasks. The extensive experiments illustrate that our proposed LUSD-Net significantly improves the AQA performance, and the FineFS dataset provides a quantity data source for the AQA. The source code of LUSD-Net and the FineFS dataset is released at https://github.com/yanliji/FineFS-dataset.