Decoupling Representations with Quantized Vectors for Semi-Supervised Action Quality Assessment

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.