Combatting Data Imbalance and Noise in Micro-Action Recognition

摘要

In contrast to traditional action recognition, Micro-Action Recognition focuses on identifying subtle, low-amplitude movements, which was constrained by two kinds of challenges. The first challenge is the spatial imbalance, where small, critical action regions are easily overwhelmed by vast, irrelevant backgrounds, leading to a low signal-to-noise ratio. The second challenge is the class distribution imbalance, where the natural occurrence of actions follows a long-tailed distribution, causing models to be biased towards common actions. To address these specific issues, our framework introduces two targeted solutions. To mitigate spatial imbalance, a YOLOv12-based detection module has been used to localize and crop salient body parts, forcing the model to focus on action-relevant regions. Concurrently, to mitigate class imbalance, this study implement a dynamic oversampling strategy combined with temporal data augmentation, effectively re-weighting the training process to improve performance on rare categories. Integrated with a V-JEPA2 backbone and a multi-classifier ensemble, our approach demonstrates its efficacy by securing second place in the ACM MM'25 Micro-Action Analysis Challenge with an F1-score of 76.98%.