Instance-Specific Learning for Skeleton-Based Action Recognition with Varying Data Quality

Skeleton-based action recognition technology has gained significant attention and made great progress in recent years. However, the performance of existing methods declines significantly when the quality of skeleton data extracted by pose estimation algorithms varies. To address this issue, this study proposes an instance-specific learning method aimed at enhancing the model’s ability to learn discriminative features when handling skeleton data of varying quality. We introduce a Dynamic Instance Discriminability Assessment (DIDA) mechanism and a Staged Instance Weighting (SIW) strategy. The DIDA mechanism dynamically evaluates the discriminability of instances by combining prior knowledge with feedback from the model during the training process. The SIW strategy adjusts the weights of instances at different training stages based on their discriminability. Notably, our method requires only a minimal increase in computational cost during training and incurs no additional computational overhead during testing compared to baseline models. We utilized Pifpaf and HR-Net pose estimation methods to extract skeleton data of varying quality from the NTU60, NTU120, and HMDB51 video datasets and conducted extensive experimental validation. The results indicate that the proposed method significantly enhances the action recognition performance while maintaining computational efficiency.