  Unsupervised semantic segmentation (USS) aims to discover and recognize
meaningful categories without any labels. For a successful USS, two key
abilities are required: 1) information compression and 2) clustering
capability. Previous methods have relied on feature dimension reduction for
information compression, however, this approach may hinder the process of
clustering. In this paper, we propose a novel USS framework called
Expand-and-Quantize Unsupervised Semantic Segmentation (EQUSS), which combines
the benefits of high-dimensional spaces for better clustering and product
quantization for effective information compression. Our extensive experiments
demonstrate that EQUSS achieves state-of-the-art results on three standard
benchmarks. In addition, we analyze the entropy of USS features, which is the
first step towards understanding USS from the perspective of information
theory.
