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 about the user's intention to SAM. Starting with an initial point prompt, SAM produces an 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 both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAug

翻译：本文提出SAMAug，一种用于Segment Anything Model（SAM）的新型视觉点增强方法，该方法能够提升交互式图像分割性能。SAMAug通过生成增强的点提示，向SAM提供更多关于用户意图的信息。从初始点提示出发，SAM生成初始掩码，该掩码随后被输入至我们提出的SAMAug以生成增强的点提示。通过融入这些额外的点，SAM能够基于增强后的点提示与初始提示生成增强的分割掩码，从而改善分割性能。我们采用四种不同的点增强策略进行评估：随机采样、基于最大差异熵的采样、最大距离采样以及显著性采样。在COCO、Fundus、COVID QUEx和ISIC2018数据集上的实验结果表明，SAMAug能够提升SAM的分割结果，其中最大距离与显著性策略效果尤为显著。SAMAug展示了视觉提示增强在计算机视觉中的潜力。SAMAug的代码已开源至github.com/yhydhx/SAMAug。