AI 中文总结
Zero-Fi是一种对比信号-语言对齐框架，可在无需新活动类标注Wi-Fi样本或模型适配的情况下，实现基于Wi-Fi的零样本人体活动识别，在大规模公开基准数据集上验证了有效性。
AI 中文摘要
基于Wi-Fi的人体活动识别已取得显著进展，但现有多数方法假设活动为闭集，且需为每个目标类提供带标注的Wi-Fi样本，限制了其识别未见过活动的能力。我们提出Zero-Fi，一种用于零样本Wi-Fi人体活动识别的对比信号-语言对齐框架。Zero-Fi从互补的Wi-Fi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对齐到共享嵌入空间。这种跨模态对齐使Zero-Fi无需为新活动类提供带标注的Wi-Fi样本或对模型进行适配即可识别新活动类。在大规模公开基准数据集上的实验表明，Zero-Fi对保留的活动类实现了有效的零样本识别，凸显了信号-语言对齐在扩展Wi-Fi传感超出预定义活动类范围方面的潜力。
英文摘要
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.
