用语言描述匹配无线信号，实现无需训练就能识别新动作。
Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
- 通过对比学习对齐信号与语言特征，构建统一表示空间。
- 在未见动作类别上实现有效识别，无需额外标注数据。
- 适合需要快速扩展识别能力的智能场景应用。
基于无线信号的人体活动识别已取得显著进展，但现有方法通常假设活动类别为封闭集，需为每个目标类别提供带标签的无线信号样本，限制了对未知活动的识别能力。本文提出 Zero-Fi，一种基于对比信号-语言对齐的零样本无线信号活动识别框架。Zero-Fi 从互补的无线信号特征中学习统一表示，并将其与自然语言活动描述的语义表示在共享嵌入空间中对齐。这种跨模态对齐使 Zero-Fi 能够在无需目标类别带标签信号样本或模型适配的情况下识别新活动类别。在大规模公开基准数据集上的实验表明，该方法可有效实现未见活动类别的零样本识别，凸显了信号-语言对齐在拓展无线感知能力方面的潜力。
原文摘要 · Abstract (English)
Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities. We present Zero-Fi, a contrastive signal-language alignment framework for zero-shot Wi-Fi-based human activity recognition. Zero-Fi learns unified representations from complementary Wi-Fi signal features and aligns them with the semantic representations of natural-language activity descriptions in a shared embedding space. This cross-modal alignment enables Zero-Fi to recognize new activity classes without requiring labeled Wi-Fi samples or model adaptation for those classes. Experiments on large-scale public benchmark datasets demonstrate effective zero-shot recognition of held-out activity classes, highlighting the potential of signal-language alignment to extend Wi-Fi sensing beyond predefined activity classes.
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