This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information to SAM. From the initial point prompt, SAM produces the initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We evaluate four point augmentation techniques: random selection, maximum difference entropy, maximum distance, and a saliency model. Experiments on the COCO, Fundus, and Chest X-ray datasets demonstrate that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency model methods. SAMAug underscores the potential of visual prompt engineering to advance interactive computer vision models.

本篇文章介绍了SAMAug，这是一种新颖的视觉点增强方法，用于增强交互式图像分割性能。SAMAug通过生成增强点提示提供更多信息以提高SAM的信息提取能力，从而提高交互式计算机视觉的性能。

SAMAug：点提示增强分段模型