  Segment anything model (SAM) has achieved great success in the field of
natural image segmentation. Nevertheless, SAM tends to consider shadows as
background and therefore does not perform segmentation on them. In this paper,
we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to
detect shadows. Besides, by combining it with long short-term attention
mechanism, we extend its capability for efficient video shadow detection.
Specifically, we first fine-tune SAM on ViSha training dataset by utilizing the
bounding boxes obtained from the ground truth shadow mask. Then during the
inference stage, we simulate user interaction by providing bounding boxes to
detect a specific frame (e.g., the first frame). Subsequently, using the
detected shadow mask as a prior, we employ a long short-term network to learn
spatial correlations between distant frames and temporal consistency between
adjacent frames, thereby achieving precise shadow information propagation
across video frames. Extensive experimental results demonstrate the
effectiveness of our method, with notable margin over the state-of-the-art
approaches in terms of MAE and IoU metrics. Moreover, our method exhibits
accelerated inference speed compared to previous video shadow detection
approaches, validating the effectiveness and efficiency of our method. The
source code is now publicly available at
https://github.com/harrytea/Detect-AnyShadow.
