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Unsupervised semantic segmentation (USS) aims to discover and recognizemeaningful categories without any labels. For a successful USS, two keyabilities are required: 1) information compression and 2) clusteringcapability. Previous methods have relied on feature dimension reduction forinformation compression, however, this approach may hinder the process ofclustering. In this paper, we propose a novel USS framework calledExpand-and-Quantize Unsupervised Semantic Segmentation (EQUSS), which combinesthe benefits of high-dimensional spaces for better clustering and productquantization for effective information compression. Our extensive experimentsdemonstrate that EQUSS achieves state-of-the-art results on three standardbenchmarks. In addition, we analyze the entropy of USS features, which is thefirst step towards understanding USS from the perspective of informationtheory.

Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization