Unsupervised semantic segmentation requires assigning a label to every pixel
without any human annotations. Despite recent advances in self-supervised
representation learning for individual images, unsupervised semantic
segmentation with pixel-level representations is still a challenging task and
remains underexplored. In this work, we propose a self-supervised pixel
representation learning method for semantic segmentation by using visual
concepts (i.e., groups of pixels with semantic meanings, such as parts,
objects, and scenes) extracted from images. To guide self-supervised learning,
we leverage three types of relationships between pixels and concepts, including
the relationships between pixels and local concepts, local and global concepts,
as well as the co-occurrence of concepts. We evaluate the learned pixel
embeddings and visual concepts on three datasets, including PASCAL VOC 2012,
COCO 2017, and DAVIS 2017. Our results show that the proposed method gains
consistent and substantial improvements over recent unsupervised semantic
segmentation approaches, and also demonstrate that visual concepts can reveal
insights into image datasets.

本文提出了一种自监督的像素表示学习方法，通过使用从图像中提取的视觉概念（例如部件、物体和场景等具有语义意义的像素组）实现语义分割，评估了在三个数据集上学习到的像素嵌入和视觉概念。作者的实验结果表明，该方法在非监督语义分割方面取得了持续和显著的改进，并且视觉概念可以揭示图像数据集的洞见。