用低秩适配提升雷达信号的视觉模型精度，让跌倒检测更准。
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition
- 用低秩适配在权重空间微调，减少参数量同时保留迁移能力。
- 融合串并联适配器，增强雷达时频特征的细粒度表达能力。
- 专为雷达时频图设计，适合智能健康监测等场景应用。
随着人口老龄化加剧，跌倒检测等人体活动识别（HAR）日益重要，需有效监控系统预防严重伤害。本研究针对基于雷达的时频图信号，对视觉变压器（ViT）模型进行微调。与传统图像数据不同，雷达信号非视觉化且各类活动差异小，全参数微调效果不佳。为此，提出一种新方法：在权重空间采用低秩适配（LoRA）实现知识迁移，并在特征空间引入串并联适配器提取细粒度特征。所提联合微调方法显著提升雷达基HAR准确率，优于该领域现有最优方法。代码已开源：https://github.com/wangyijunlyy/SelaFD。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://github.com/wangyijunlyy/SelaFD.
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