  Unsupervised semantic segmentation aims to automatically partition images
into semantically meaningful regions by identifying global categories within an
image corpus without any form of annotation. Building upon recent advances in
self-supervised representation learning, we focus on how to leverage these
large pre-trained models for the downstream task of unsupervised segmentation.
We present PriMaPs - Principal Mask Proposals - decomposing images into
semantically meaningful masks based on their feature representation. This
allows us to realize unsupervised semantic segmentation by fitting class
prototypes to PriMaPs with a stochastic expectation-maximization algorithm,
PriMaPs-EM. Despite its conceptual simplicity, PriMaPs-EM leads to competitive
results across various pre-trained backbone models, including DINO and DINOv2,
and across datasets, such as Cityscapes, COCO-Stuff, and Potsdam-3.
Importantly, PriMaPs-EM is able to boost results when applied orthogonally to
current state-of-the-art unsupervised semantic segmentation pipelines.
