用原型注意力+渐进增强，让WiFi手势识别在少量数据下更准更快
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition
- 基于原型的度量学习，动态强化关键特征维度
- 在真实场景中准确率超越现有方法，训练效率更高
- 适合数据稀缺的低资源手势识别应用
本文提出ProFi-Net，一种针对WiFi手势识别的少样本学习框架，解决训练数据有限和特征表示稀疏的问题。该方法采用原型基度量学习架构，并引入特征级注意力机制，通过动态增强最具区分性的特征维度来优化欧氏距离计算。此外，提出一种受课程学习启发的数据增强策略，仅对查询集逐步添加高斯噪声，使模型逐步适应更复杂的扰动，提升泛化能力并缓解过拟合。在多种真实环境下的大量实验表明，ProFi-Net在分类准确率和训练效率方面显著优于传统原型网络及其他先进少样本学习方法。
原文摘要 · Abstract (English)
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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