This paper presents a method called PriMaPs to decompose images into semantically meaningful masks using principal components of self-supervised image features. These masks serve as proposals to guide an expectation-maximization algorithm, PriMaPs-EM, to realize unsupervised semantic segmentation by fitting class prototypes. Despite simplicity, PriMaPs-EM leads to compe...