SAMAug: Point Prompt Augmentation for Segment Anything Model

TL;DR

SAMAug is a new point augmentation technique that improves the interactive segmentation capabilities of the Segment Anything Model by generating additional prompts to refine segmentation masks.

Contribution

The paper introduces SAMAug, a novel method for augmenting point prompts to enhance SAM's segmentation accuracy across various datasets.

Findings

SAMAug improves segmentation performance on multiple datasets.

Maximum distance and saliency strategies yield the best results.

Prompt augmentation significantly boosts SAM's effectiveness.

Abstract

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