# Universal Organizer of SAM for Unsupervised Semantic Segmentation

Source: [Universal Organizer of SAM for Unsupervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa73491e209131cea5b8e4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:46:17.278Z

Authors: Tingting Li, Gensheng Pei, Xinhao Cai, Huafeng Liu, Qiong Wang, Yazhou Yao

Published: 2024-05-20T03:00:49.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.11742>)

[PDF](<https://arxiv.org/pdf/2405.11742.pdf>)