We propose a novel method for unsupervised semantic image segmentation based
on mutual information maximization between local and global high-level image
features. The core idea of our work is to leverage recent progress in
self-supervised image representation learning. Representation learning methods
compute a single high-level feature capturing an entire image. In contrast, we
compute multiple high-level features, each capturing image segments of one
particular semantic class. To this end, we propose a novel two-step learning
procedure comprising a segmentation and a mutual information maximization step.
In the first step, we segment images based on local and global features. In the
second step, we maximize the mutual information between local features and
high-level features of their respective class. For training, we provide solely
unlabeled images and start from random network initialization. For quantitative
and qualitative evaluation, we use established benchmarks, and COCO-Persons,
whereby we introduce the latter in this paper as a challenging novel benchmark.
InfoSeg significantly outperforms the current state-of-the-art, e.g., we
achieve a relative increase of 26% in the Pixel Accuracy metric on the
COCO-Stuff dataset.

本文提出了一种基于自监督学习的无监督语义图像分割的新方法，通过局部和全局高级别图像特征间的互信息最大化计算多个高级别特征，实现对不同语义类别的图像分割和互信息最大化的两阶段学习过程，并在已有数据集和引入的 COCO-Persons 数据集上相对推进了 26%（基于像素准确率评估）。