  Unsupervised semantic segmentation aims to discover groupings within and
across images that capture object and view-invariance of a category without
external supervision. Grouping naturally has levels of granularity, creating
ambiguity in unsupervised segmentation. Existing methods avoid this ambiguity
and treat it as a factor outside modeling, whereas we embrace it and desire
hierarchical grouping consistency for unsupervised segmentation.
  We approach unsupervised segmentation as a pixel-wise feature learning
problem. Our idea is that a good representation shall reveal not just a
particular level of grouping, but any level of grouping in a consistent and
predictable manner. We enforce spatial consistency of grouping and bootstrap
feature learning with co-segmentation among multiple views of the same image,
and enforce semantic consistency across the grouping hierarchy with clustering
transformers between coarse- and fine-grained features.
  We deliver the first data-driven unsupervised hierarchical semantic
segmentation method called Hierarchical Segment Grouping (HSG). Capturing
visual similarity and statistical co-occurrences, HSG also outperforms existing
unsupervised segmentation methods by a large margin on five major object- and
scene-centric benchmarks. Our code is publicly available at
https://github.com/twke18/HSG .
