InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization

TL;DR

This paper introduces InfoSeg, an unsupervised semantic image segmentation method that maximizes mutual information between local and global features, leveraging self-supervised learning to outperform existing approaches on standard benchmarks.

Contribution

We develop a novel two-step learning process that segments images and maximizes mutual information, enabling unsupervised semantic segmentation without labeled data.

Findings

Achieves 26% relative increase in Pixel Accuracy on COCO-Stuff.

Outperforms state-of-the-art unsupervised segmentation methods.

Introduces COCO-Persons as a new challenging benchmark.

Abstract

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.