Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation

• URL: http://arxiv.org/abs/2401.00248v2
• Date: Fri, 22 Mar 2024 07:25:03 GMT
• Title: Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
• Authors: Xianjie Liu, Keren Fu, Qijun Zhao,
• Abstract summary: We propose DIS-SAM, which advances SAM towards highly accurate object segmentation. 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.
• Score: 12.03947802006261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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