{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/samaug-point-prompt-augmentation-for-segment","title":"SAMAug: Point Prompt Augmentation for Segment Anything Model","arxiv_id":"2307.01187","date":"2023-07-03","proceeding":null,"authors":["Haixing Dai","Chong Ma","Zhiling Yan","Zhengliang Liu","Enze Shi","Yiwei Li","Peng Shu","Xiaozheng Wei","Lin Zhao","Zihao Wu","Fang Zeng","Dajiang Zhu","Wei Liu","Quanzheng Li","Lichao Sun","Shu Zhang Tianming Liu","Xiang Li"],"abstract":"This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. 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