Understanding Self-Supervised Features for Learning Unsupervised
  Instance Segmentation
Paul Engstler Luke Melas-Kyriazi Christian Rupprecht Iro Laina
Self-supervised learning (SSL) can be used to solve complex visual tasks
without human labels. Self-supervised representations encode useful semantic
information about images, and as a result, they have already been used for
tasks such as unsupervised semantic segmentation. In this paper, we investigate
self-supervised representations for instance segmentation without any manual
annotations. We find that the features of different SSL methods vary in their
level of instance-awareness. In particular, DINO features, which are known to
be excellent semantic descriptors, lack behind MAE features in their
sensitivity for separating instances.