ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition (2504.20193v1)

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.

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

The paper "ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition" outlines an innovative approach to addressing challenges in few-shot learning within the domain of WiFi-based gesture recognition. The proposed ProFi-Net framework adeptly integrates a prototype-based metric learning architecture with a feature attention mechanism designed to refine feature discrimination, and introduces a curriculum-based data augmentation strategy to optimize the learning process further.

Summary and Methodology

ProFi-Net is structured around three key components: representation learning, prototype-based metric learning with attention, and curriculum-guided query augmentation. Representation learning is facilitated by a convolutional neural network extracting feature embeddings from WiFi CSI signals. This setup allows the model to handle sparse training data, a notorious bottleneck in few-shot learning systems, by concentrating on essential gesture-induced variations in wireless signals.

The method employs a prototype-based approach where class prototypes are computed by averaging feature vectors of support samples. A feature-level attention mechanism has been incorporated into this framework, enabling the model to hone in on the most discriminative feature dimensions, thereby enhancing the Euclidean distance calculations used in classification.

The curriculum inspired data augmentation, unique to this work, introduces progressive Gaussian noise to query samples. This approach allows the model to adapt to increasingly complex variations in the input data, bolstering robustness against overfitting. The authors demonstrate their methodology across several environments, showcasing substantial accuracy improvements over traditional prototype networks and competing few-shot learning techniques.

Experimental Results and Implications

The experimental evaluations highlight ProFi-Net's efficacy, with notable improvements in classification accuracy across 5-way 1-shot and 5-way 5-shot scenarios. Specifically, accuracy improvements ranged up to 7.1% in more complex data environments. These achievements underscore the strength of integrating attention mechanisms and curriculum learning to navigate the pitfalls of sparse data and augment feature discrimination.

Such advancements have practical and theoretical implications, particularly in advancing gesture recognition applications in smart environments and healthcare settings. By reducing the reliance on extensive labeled datasets, ProFi-Net enables faster deployment of gesture recognition systems, making them more accessible and scalable across different domains.

Future Directions

There are several avenues for further exploration. Optimizing the curriculum schedule may yield additional performance enhancements, especially in environments where signal fidelity and variances play critical roles in recognition accuracy. Furthermore, integrating ProFi-Net with temporal dynamic analysis techniques could unlock additional layers of gesture interpretation and potentially expand its application range to include more complex scenarios such as multi-gesture recognition sequences.

The implications of this research are promising for the evolution of AI-driven gesture recognition, suggesting avenues for enhanced HCI, improved accessibility, and more efficient training methodologies for AI models facing sparse data conditions.