The Segment Anything Model (SAM) has exhibited outstanding performance in
various image segmentation tasks. Despite being trained with over a billion
masks, SAM faces challenges in mask prediction quality in numerous scenarios,
especially in real-world contexts. In this paper, we introduce a novel
prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model
(PA-SAM), aiming to enhance the segmentation mask quality of the original SAM.
By exclusively training the prompt adapter, PA-SAM extracts detailed
information from images and optimizes the mask decoder feature at both sparse
and dense prompt levels, improving the segmentation performance of SAM to
produce high-quality masks. Experimental results demonstrate that our PA-SAM
outperforms other SAM-based methods in high-quality, zero-shot, and open-set
segmentation. We're making the source code and models available at
this https URL

通过引入一种新型的提示驱动适配器（PA-SAM）到 Segment Anything Model (SAM) 中，提高了原始 SAM 的分割掩模质量，通过在稀疏和密集提示级别上优化面罩解码器功能，从图像中提取详细信息，实验结果表明 PA-SAM 在高质量、零样本和开放集分割方面优于其他基于 SAM 的方法。