  Unsupervised image semantic segmentation(UISS) aims to match low-level visual
features with semantic-level representations without outer supervision. In this
paper, we address the critical properties from the view of feature alignments
and feature uniformity for UISS models. We also make a comparison between UISS
and image-wise representation learning. Based on the analysis, we argue that
the existing MI-based methods in UISS suffer from representation collapse. By
this, we proposed a robust network called Semantic Attention Network(SAN), in
which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise
and semantic features dynamically. Experimental results on multiple semantic
segmentation benchmarks show that our unsupervised segmentation framework
specializes in catching semantic representations, which outperforms all the
unpretrained and even several pretrained methods.
