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
this https URL .

本文提出了一种基于像素特征学习的数据驱动的无监督分层语义分割方法，使用同一图像的多视角协同分割来启动特征学习，加入粗细层次间的聚类变换器以确保组织层次间的语义一致性，该方法被称作 'Hierarchical Segment Grouping'，在五个主流目标和场景测评基准上表现良好。