静默威胁的智能之眼：用于太赫兹成像的视觉语言模型与上下文学习
Smart Eyes for Silent Threats: VLMs and In-Context Learning for THz Imaging
AI总结：
本文首次将上下文学习增强的视觉语言模型应用于太赫兹图像分类，通过模态对齐提示框架在零样本和单样本设置下验证了该方法在低数据场景中提升分类性能与可解释性的有效性。
摘要：
太赫兹（THz）成像能够在安全筛查和材料分类等应用中实现非侵入式分析，但由于标注有限、分辨率低和视觉模糊性，有效的图像分类仍然具有挑战性。我们引入基于视觉语言模型（VLMs）的上下文学习（In-Context Learning，ICL），作为一种无需微调的灵活、可解释的替代方案。利用模态对齐的提示框架，我们将两个开放权重的VLM适配到太赫兹领域，并在零样本和单样本设置下对其进行评估。我们的结果表明，ICL在低数据场景下提升了分类性能和可解释性。这是ICL增强的VLM首次应用于太赫兹成像，为资源受限的科学领域提供了一个有前景的方向。代码：\href{https://github.com/Nicolas-Poggi/Project_THz_Classification/tree/main}{GitHub仓库}。
英文摘要：
Terahertz (THz) imaging enables non-invasive analysis for applications such as security screening and material classification, but effective image classification remains challenging due to limited annotations, low resolution, and visual ambiguity. We introduce In-Context Learning (ICL) with Vision-Language Models (VLMs) as a flexible, interpretable alternative that requires no fine-tuning. Using a modality-aligned prompting framework, we adapt two open-weight VLMs to the THz domain and evaluate them under zero-shot and one-shot settings. Our results show that ICL improves classification and interpretability in low-data regimes. This is the first application of ICL-enhanced VLMs to THz imaging, offering a promising direction for resource-constrained scientific domains. Code: \href{https://github.com/Nicolas-Poggi/Project_THz_Classification/tree/main}{GitHub repository}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
