Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
Quick Answer
Zero-Fi introduces a novel framework for zero-shot Wi-Fi-based human activity recognition by aligning Wi-Fi signal features with natural-language descriptions.
Quick Take
This approach enables the recognition of unseen activities without requiring labeled samples, demonstrating effective performance on large-scale benchmark datasets.
Key Points
- Zero-Fi utilizes contrastive signal-language alignment for activity recognition.
- It recognizes new activities without labeled Wi-Fi samples or model adaptation.
- The framework shows effective zero-shot recognition on public benchmark datasets.
- This method extends Wi-Fi sensing beyond predefined activity classes.
- Significant advancements in human activity recognition are achieved with Zero-Fi.
Paper Resources
Source Excerpt
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 nat
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