  The Segment Anything Model (SAM) represents a significant breakthrough into
foundation models for computer vision, providing a large-scale image
segmentation model. However, despite SAM's zero-shot performance, its
segmentation masks lack fine-grained details, particularly in accurately
delineating object boundaries. We have high expectations regarding whether SAM,
as a foundation model, can be improved towards highly accurate object
segmentation, which is known as dichotomous image segmentation (DIS). To
address this issue, we propose DIS-SAM, which advances SAM towards DIS with
extremely accurate details. DIS-SAM is a framework specifically tailored for
highly accurate segmentation, maintaining SAM's promptable design. DIS-SAM
employs a two-stage approach, integrating SAM with a modified IS-Net dedicated
to DIS. Despite its simplicity, DIS-SAM demonstrates significantly enhanced
segmentation accuracy compared to SAM and HQ-SAM.
