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.

本研究提出了一种名为语义关注网络（Semantic Attention Network，SAN）的模型，在无监督的图像语义分割（UISS）任务中采用 “语义关注（Semantic Attention）” 模块生成像素级和语义级特征，该模型具有较好的特征一致性和特征匹配性能，在多个数据集上超过已有方法的分割效果。