SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integrated into their surroundings and has numerous practical applications in fields such as medicine, art, and agriculture. In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation. We also compare SAM's performance with 22 state-of-the-art COD methods. Our results indicate that while SAM shows promise in generic object segmentation, its performance on the COD task is limited. This presents an opportunity for further research to explore how to build a stronger SAM that may address the COD task. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.

翻译：SAM是Meta AI Research近期发布的分割模型，因其在通用目标分割中的出色表现而迅速受到关注。然而，其在伪装场景等特定场景中的泛化能力尚不明确。伪装目标检测（COD）旨在识别与周围环境无缝融合的目标，在医学、艺术和农业等领域具有广泛的实际应用。本研究尝试探究SAM能否解决COD任务，并通过最大分割评估和伪装定位评估，在COD基准上评估SAM的性能。我们还将SAM的性能与22种最先进的COD方法进行比较。结果表明，尽管SAM在通用目标分割中展现出潜力，但其在COD任务上的表现有限。这为进一步研究如何构建更强大的SAM以应对COD任务提供了契机。本文结果可在\url{https://github.com/luckybird1994/SAMCOD}获取。