  We create a framework for bootstrapping visual representation learning from a
primitive visual grouping capability. We operationalize grouping via a contour
detector that partitions an image into regions, followed by merging of those
regions into a tree hierarchy. A small supervised dataset suffices for training
this grouping primitive. Across a large unlabeled dataset, we apply this
learned primitive to automatically predict hierarchical region structure. These
predictions serve as guidance for self-supervised contrastive feature learning:
we task a deep network with producing per-pixel embeddings whose pairwise
distances respect the region hierarchy. Experiments demonstrate that our
approach can serve as state-of-the-art generic pre-training, benefiting
downstream tasks. We additionally explore applications to semantic region
search and video-based object instance tracking.
