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

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arxiv 2304.06022 v4 pith:QFBY26ES submitted 2023-04-12 cs.CV

Ge-Peng Ji , Deng-Ping Fan , Peng Xu , Ming-Ming Cheng , Bowen Zhou , Luc Van Gool This is my paper · ORCID
classification cs.CV
keywords anythingconcealedscenessegmentanimalsartificialcamouflagedchoose
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Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision. We could not be more excited to probe the performance traits of SAM. In particular, exploring situations in which SAM does not perform well is interesting. In this report, we choose three concealed scenes, i.e., camouflaged animals, industrial defects, and medical lesions, to evaluate SAM under unprompted settings. Our main observation is that SAM looks unskilled in concealed scenes.

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Cited by 1 Pith paper

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  1. Segment Concealed Objects with Incomplete Supervision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SEE is a unified mean-teacher framework that derives SAM prompts from coarse teacher masks to generate pseudo-labels, and reports state-of-the-art results for weakly and semi-supervised concealed object segmentation.

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