  Being able to learn dense semantic representations of images without
supervision is an important problem in computer vision. However, despite its
significance, this problem remains rather unexplored, with a few exceptions
that considered unsupervised semantic segmentation on small-scale datasets with
a narrow visual domain. In this paper, we make a first attempt to tackle the
problem on datasets that have been traditionally utilized for the supervised
case. To achieve this, we introduce a two-step framework that adopts a
predetermined mid-level prior in a contrastive optimization objective to learn
pixel embeddings. This marks a large deviation from existing works that relied
on proxy tasks or end-to-end clustering. Additionally, we argue about the
importance of having a prior that contains information about objects, or their
parts, and discuss several possibilities to obtain such a prior in an
unsupervised manner.
  Experimental evaluation shows that our method comes with key advantages over
existing works. First, the learned pixel embeddings can be directly clustered
in semantic groups using K-Means on PASCAL. Under the fully unsupervised
setting, there is no precedent in solving the semantic segmentation task on
such a challenging benchmark. Second, our representations can improve over
strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The
code is available.
