  Meta AI Research has recently released SAM (Segment Anything Model) which is
trained on a large segmentation dataset of over 1 billion masks. As a
foundation model in the field of computer vision, SAM (Segment Anything Model)
has gained attention for its impressive performance in generic object
segmentation. Despite its strong capability in a wide range of zero-shot
transfer tasks, it remains unknown whether SAM can detect things in challenging
setups like transparent objects. In this work, we perform an empirical
evaluation of two glass-related challenging scenarios: mirror and transparent
objects. We found that SAM often fails to detect the glass in both scenarios,
which raises concern for deploying the SAM in safety-critical situations that
have various forms of glass.
