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.

无监督语义分割是发现和识别有意义类别而不使用任何标签的方法之一，本文提出了一种新的无监督语义分割框架（EQUSS），它结合了高维空间的好处以进行更好的聚类和产品量化以实现有效的信息压缩，并在三个标准基准测试中取得了最先进的结果。此外，我们还分析了 USS 特征的熵，这是从信息理论的角度理解 USS 的第一步。