  Unsupervised semantic segmentation is a challenging task that segments images
into semantic groups without manual annotation. Prior works have primarily
focused on leveraging prior knowledge of semantic consistency or priori
concepts from self-supervised learning methods, which often overlook the
coherence property of image segments. In this paper, we demonstrate that the
smoothness prior, asserting that close features in a metric space share the
same semantics, can significantly simplify segmentation by casting unsupervised
semantic segmentation as an energy minimization problem. Under this paradigm,
we propose a novel approach called SmooSeg that harnesses self-supervised
learning methods to model the closeness relationships among observations as
smoothness signals. To effectively discover coherent semantic segments, we
introduce a novel smoothness loss that promotes piecewise smoothness within
segments while preserving discontinuities across different segments.
Additionally, to further enhance segmentation quality, we design an asymmetric
teacher-student style predictor that generates smoothly updated pseudo labels,
facilitating an optimal fit between observations and labeling outputs. Thanks
to the rich supervision cues of the smoothness prior, our SmooSeg significantly
outperforms STEGO in terms of pixel accuracy on three datasets: COCOStuff
(+14.9%), Cityscapes (+13.0%), and Potsdam-3 (+5.7%).
