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.

基于无监督语义分割的 PriMaPs-EM 算法能够通过将图像分解为语义有意义的掩膜，并使用随机期望最大化算法拟合类别原型，实现在各种预训练模型和数据集上竞争性的无监督语义分割结果，优化了当前最先进的无监督语义分割流水线。