As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In this report, we try SAM on an unexplored popular task: shadow detection. Specifically, four benchmarks were chosen and evaluated with widely used metrics. The experimental results show that the performance for shadow detection using SAM is not satisfactory, especially when comparing with the elaborate models. Code is available at https://github.com/LeipingJie/SAMSh.

翻译：作为一种可提示的通用物体分割模型，分割万物模型（SAM）近期引起了广泛关注，并展现出强大的性能。然而，在面对某些任务（如医学图像分割、伪装目标检测等）时，它仍然遭遇了滑铁卢。在本报告中，我们尝试将SAM应用于一个尚未被探索的热门任务：阴影检测。具体而言，我们选取了四个基准数据集，并使用广泛采用的指标进行了评估。实验结果表明，SAM在阴影检测任务上的表现并不令人满意，尤其是与精心设计的模型相比。代码已开源在 https://github.com/LeipingJie/SAMSh。