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.
@article{arxiv.2311.14665,
title = {Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation},
author = {Paul Engstler and Luke Melas-Kyriazi and Christian Rupprecht and Iro Laina},
journal= {arXiv preprint arXiv:2311.14665},
year = {2023}
}