Universal Organizer of SAM for Unsupervised Semantic Segmentation

TL;DR

This paper introduces UO-SAM, a universal framework leveraging SAM to refine unsupervised semantic segmentation masks, significantly improving boundary accuracy and achieving state-of-the-art results.

Contribution

The paper presents a novel universal organizer that enhances USS masks using SAM with local and global optimization, a new approach for mask refinement.

Findings

UO-SAM outperforms existing USS methods in accuracy.

Refined masks have sharper and more precise boundaries.

State-of-the-art performance on benchmark datasets.

Abstract

Unsupervised semantic segmentation (USS) aims to achieve high-quality segmentation without manual pixel-level annotations. Existing USS models provide coarse category classification for regions, but the results often have blurry and imprecise edges. Recently, a robust framework called the segment anything model (SAM) has been proven to deliver precise boundary object masks. Therefore, this paper proposes a universal organizer based on SAM, termed as UO-SAM, to enhance the mask quality of USS models. Specifically, using only the original image and the masks generated by the USS model, we extract visual features to obtain positional prompts for target objects. Then, we activate a local region optimizer that performs segmentation using SAM on a per-object basis. Finally, we employ a global region optimizer to incorporate global image information and refine the masks to obtain the final fine-grained masks. Compared to existing methods, our UO-SAM achieves state-of-the-art performance.