  Instance segmentation is an active topic in computer vision that is usually
solved by using supervised learning approaches over very large datasets
composed of object level masks. Obtaining such a dataset for any new domain can
be very expensive and time-consuming. In addition, models trained on certain
annotated categories do not generalize well to unseen objects. The goal of this
paper is to propose a method that can perform unsupervised discovery of
long-tail categories in instance segmentation, through learning instance
embeddings of masked regions. Leveraging rich relationship and hierarchical
structure between objects in the images, we propose self-supervised losses for
learning mask embeddings. Trained on COCO dataset without additional
annotations of the long-tail objects, our model is able to discover novel and
more fine-grained objects than the common categories in COCO. We show that the
model achieves competitive quantitative results on LVIS as compared to the
supervised and partially supervised methods.
