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

• URL: http://arxiv.org/abs/2504.20193v1
• Date: Mon, 28 Apr 2025 18:52:45 GMT
• Title: ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition
• Authors: Zhe Cui, Shuxian Zhang, Kangzhi Lou, Le-Nam Tran,
• Abstract summary: ProFi-Net is a novel few-shot learning framework for WiFi-based gesture recognition.<n>ProFi-Net employs a prototype-based metric learning architecture enhanced with a feature-level attention mechanism.<n>Our approach introduces a curriculum-inspired data augmentation strategy exclusively on the query set.
• Score: 10.338367151489027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents ProFi-Net, a novel few-shot learning framework for WiFi-based gesture recognition that overcomes the challenges 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 dimensions. 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 exposed to a broader range of challenging variations, thereby improving its generalization and robustness to overfitting. Extensive experiments conducted 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 accuracy and training efficiency.

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