  Segment anything model (SAM) has shown its spectacular performance in
segmenting universal objects, especially when elaborate prompts are provided.
However, the drawback of SAM is twofold. On the first hand, it fails to segment
specific targets, e.g., shadow images or lesions in medical images. On the
other hand, manually specifying prompts is extremely time-consuming. To
overcome the problems, we propose AdapterShadow, which adapts SAM model for
shadow detection. To adapt SAM for shadow images, trainable adapters are
inserted into the frozen image encoder of SAM, since the training of the full
SAM model is both time and memory consuming. Moreover, we introduce a novel
grid sampling method to generate dense point prompts, which helps to
automatically segment shadows without any manual interventions. Extensive
experiments are conducted on four widely used benchmark datasets to demonstrate
the superior performance of our proposed method. Codes will are publicly
available at https://github.com/LeipingJie/AdapterShadow.
