# Smart Eyes for Silent Threats: VLMs and In-Context Learning for THz Imaging Source: [Smart Eyes for Silent Threats: VLMs and In-Context Learning for THz Imaging]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:57:50.280Z Authors: Nicolas Poggi, Shashank Agnihotri, Margret Keuper Published: 2025-07-21T12:57:49.000Z ## Abstract 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}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()