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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.16521 |
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| _version_ | 1866908549157748736 |
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| author | Yan, Yifan Yang, Shuai Guo, Xiuzhen Wang, Xiangguang Chow, Wei Shu, Yuanchao He, Shibo |
| author_facet | Yan, Yifan Yang, Shuai Guo, Xiuzhen Wang, Xiangguang Chow, Wei Shu, Yuanchao He, Shibo |
| contents | Millimeter-wave (mmWave) sensing technology holds significant value in human-centric applications, yet the high costs associated with data acquisition and annotation limit its widespread adoption in our daily lives. Concurrently, the rapid evolution of large language models (LLMs) has opened up opportunities for addressing complex human needs. This paper presents mmExpert, an innovative mmWave understanding framework consisting of a data generation flywheel that leverages LLMs to automate the generation of synthetic mmWave radar datasets for specific application scenarios, thereby training models capable of zero-shot generalization in real-world environments. Extensive experiments demonstrate that the data synthesized by mmExpert significantly enhances the performance of downstream models and facilitates the successful deployment of large models for mmWave understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding Yan, Yifan Yang, Shuai Guo, Xiuzhen Wang, Xiangguang Chow, Wei Shu, Yuanchao He, Shibo Machine Learning Millimeter-wave (mmWave) sensing technology holds significant value in human-centric applications, yet the high costs associated with data acquisition and annotation limit its widespread adoption in our daily lives. Concurrently, the rapid evolution of large language models (LLMs) has opened up opportunities for addressing complex human needs. This paper presents mmExpert, an innovative mmWave understanding framework consisting of a data generation flywheel that leverages LLMs to automate the generation of synthetic mmWave radar datasets for specific application scenarios, thereby training models capable of zero-shot generalization in real-world environments. Extensive experiments demonstrate that the data synthesized by mmExpert significantly enhances the performance of downstream models and facilitates the successful deployment of large models for mmWave understanding. |
| title | mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.16521 |