Unsupervised semantic segmentation aims to achieve high-quality semantic
grouping without human-labeled annotations. With the advent of self-supervised
pre-training, various frameworks utilize the pre-trained features to train
prediction heads for unsupervised dense prediction. However, a significant
challenge in this unsupervised setup is determining the appropriate level of
clustering required for segmenting concepts. To address it, we propose a novel
framework, CAusal Unsupervised Semantic sEgmentation (CAUSE), which leverages
insights from causal inference. Specifically, we bridge intervention-oriented
approach (i.e., frontdoor adjustment) to define suitable two-step tasks for
unsupervised prediction. The first step involves constructing a concept
clusterbook as a mediator, which represents possible concept prototypes at
different levels of granularity in a discretized form. Then, the mediator
establishes an explicit link to the subsequent concept-wise self-supervised
learning for pixel-level grouping. Through extensive experiments and analyses
on various datasets, we corroborate the effectiveness of CAUSE and achieve
state-of-the-art performance in unsupervised semantic segmentation.

无监督语义分割的新框架 CAUSE 基于因果推断，通过前门调整、概念聚类手段实现适当级别的概念分割，从而在无监督语义分割任务中取得了最先进的性能。