VRP-SAM: SAM with Visual Reference Prompt
Authors: Yanpeng Sun, Jiahui Chen, Shan Zhang, Xinyu Zhang, Qiang Chen, Gang Zhang, Errui Ding, Jingdong Wang, Zechao Li

In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation creating the VRP-SAM model. In essence VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images including point box scribble and mask. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore VRP-SAM demonstrates strong generalization capabilities allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM