  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
https://github.com/xzz2/pa-sam.
