AceNet: Attention-Guided Context Enhancement for Imbalanced Action Recognition via RF Signals

Although radio frequency (RF)-based activity recognition has made significant progress in recent years, the sensing performance will be significantly degraded under class imbalance conditions, especially when minority and majority classes share semantically similar local motion patterns. Traditional data augmentation approaches in the original sample space may cause semantic deviation and meanwhile bring high computational cost. Instead, in this work we turn to address the issue at feature level, in which we focus on how to distinguish highly similar actions and mitigate imbalance-induced decision boundary bias. To tackle these challenges, we present attention-guided context enhancement network (AceNet), which designs a discriminative feature extractor and develops a feature-augmentation based classifier refinement strategy. Specifically, an attention-guided mechanism is presented to dynamically select the most distinctive temporal segments, and a hierarchical Transformer structure is then proposed to characterize both inner-segment micro-dynamics and inter-segment contextual relationships. Moreover, AceNet synthesizes features via Synthetic Minority Over-sampling Technique (SMOTE) to balance feature distribution for each category and then refine the classifier parameters to mitigate class imbalance bias. Comprehensive experiments on two public datasets with different RF modalities demonstrate that AceNet significantly outperforms existing approaches under various levels of data scarcity at a low cost.