PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

Autor:	Xie, Zhaozhi, Guan, Bochen, Jiang, Weihao, Yi, Muyang, Ding, Yue, Lu, Hongtao, Zhang, Lei
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Code is available at https://github.com/xzz2/pa-sam
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.13051 Zobrazit plný text záznamu View this record from Arxiv