ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition

This paper presents ProFi-Net, a novel few-shot learning framework for WiFi-based gesture recognition that overcomes the chal- lenges of limited training data and sparse feature representations. ProFi- Net employs a prototype-based metric learning architecture enhanced with a feature-level attention mechanism, which dynamically refines the Euclidean distance by emphasizing the most discriminative feature di- mensions. Additionally, our approach introduces a curriculum-inspired data augmentation strategy exclusively on the query set. By progressively incorporating Gaussian noise of increasing magnitude, the model is ex- posed to a broader range of challenging variations, thereby improving its generalization and robustness to overfitting. Extensive experiments con- ducted across diverse real-world environments demonstrate that ProFi- Net significantly outperforms conventional prototype networks and other state-of-the-art few-shot learning methods in terms of classification ac- curacy and training efficiency.

@article{cui2025_2504.20193,
  title={ ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition },
  author={ Zhe Cui and Shuxian Zhang and Kangzhi Lou and Le-Nam Tran },
  journal={arXiv preprint arXiv:2504.20193},
  year={ 2025 }
}