  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.
