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Segmenting anything is a ground-breaking step toward artificial generalintelligence, and the Segment Anything Model (SAM) greatly fosters thefoundation models for computer vision. We could not be more excited to probethe performance traits of SAM. In particular, exploring situations in which SAMdoes not perform well is interesting. In this report, we choose three concealedscenes, i.e., camouflaged animals, industrial defects, and medical lesions, toevaluate SAM under unprompted settings. Our main observation is that SAM looksunskilled in concealed scenes.

SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything"