用大模型自动生成毫米波数据，解决真实数据难获取问题
mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding
- 用大模型构建数据生成循环，自动合成特定场景的毫米波数据
- 合成数据使下游模型在真实环境中实现零样本泛化，性能显著提升
- 适合毫米波感知、智能人机交互等场景的研究与应用者
毫米波感知技术在以人为中心的应用中具有重要价值，但数据采集与标注成本高昂，限制了其在日常生活中的广泛应用。与此同时，大型语言模型（LLMs）的快速发展为满足复杂人类需求提供了新机遇。本文提出mmExpert，一个创新的毫米波理解框架，包含利用大模型驱动的数据生成飞轮，可自动为特定应用场景生成合成毫米波雷达数据集，从而训练出能在真实环境中实现零样本泛化的模型。大量实验表明，mmExpert生成的数据显著提升了下游模型性能，并推动了大模型在毫米波理解任务中的成功部署。
原文摘要 · Abstract (English)
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.
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