The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation ability. SAM operates by generating masks based on various input prompts such as text, bounding boxes, points, or masks, introducing a novel methodology to overcome the constraints posed by dataset-specific scarcity. While SAM is trained on an extensive dataset, comprising ~11M images, it mostly consists of natural photographic images with only very limited images from other modalities. Whilst the rapid progress in visual infrared surveillance and X-ray security screening imaging technologies, driven forward by advances in deep learning, has significantly enhanced the ability to detect, classify and segment objects with high accuracy, it is not evident if the SAM zero-shot capabilities can be transferred to such modalities. This work assesses SAM capabilities in segmenting objects of interest in the X-ray/infrared modalities. Our approach reuses the pre-trained SAM with three different prompts: bounding box, centroid and random points. We present quantitative/qualitative results to showcase the performance on selected datasets. Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts. Specifically, SAM performs poorly in segmenting slender objects and organic materials, such as plastic bottles. We find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality. This study shows that while SAM demonstrates outstanding zero-shot capabilities with box prompts, its performance ranges from moderate to poor for point prompts, indicating that special consideration on the cross-modal generalisation of SAM is needed when considering use on X-ray/infrared imagery.

Segment Anything Model (SAM)通过使用不同的输入提示（如文本、边界框、点或掩膜）生成掩膜，克服了特定数据集稀缺性的约束，评估了SAM在X射线/红外模态中分割感兴趣对象的能力，结果表明SAM在给定盒子提示时可以分割X射线模态中的对象，但对于点提示而言，表现不稳定，特别是在分割细长物体和有机材料方面，SAM的性能较差，这表明在考虑在X射线/红外图像上使用SAM时需要特别考虑跨模态泛化的问题。

非可见光谱影像应用的片段任意模型性能评估与变分提示