Summary of Large Model For Small Data: Foundation Model For Cross-modal Rf Human Activity Recognition, by Yuxuan Weng et al.
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition
by Yuxuan Weng, Guoquan Wu, Tianyue Zheng, Yanbing Yang, Jun Luo
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces FM-Fi, a cross-modal framework for enhancing Radio-Frequency (RF)-based Human Activity Recognition (HAR) systems. The framework leverages foundation models’ (FMs’) deep semantic insights from unlabeled visual data to improve RF-based HAR performance. FM-Fi employs a novel contrastive knowledge distillation mechanism, enabling the RF encoder to inherit FMs’ interpretative power for zero-shot learning. Additionally, it utilizes FM and RF’s intrinsic capabilities to remove extraneous features and refine the framework through metric-based few-shot learning techniques. The paper showcases comprehensive evaluations that demonstrate FM-Fi’s effectiveness in rivaling vision-based methodologies, with empirical validation of its generalizability across various environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FM-Fi is a new way to make computers recognize human activities using radio waves! Currently, this technology has limited data because radio signals can’t be easily read. Foundation models are really good at understanding pictures, but they don’t work well with small amounts of radio data. To fix this, the researchers created FM-Fi, which helps the computer learn from radio signals by translating what it knows about pictures. This makes the computer better at recognizing human activities without needing lots of labeled training data. |
Keywords
» Artificial intelligence » Activity recognition » Encoder » Few shot » Knowledge distillation » Zero shot
