  Just like other few-shot learning problems, few-shot segmentation aims to
minimize the need for manual annotation, which is particularly costly in
segmentation tasks. Even though the few-shot setting reduces this cost for
novel test classes, there is still a need to annotate the training data. To
alleviate this need, we propose a self-supervised training approach for
learning few-shot segmentation models. We first use unsupervised saliency
estimation to obtain pseudo-masks on images. We then train a simple prototype
based model over different splits of pseudo masks and augmentations of images.
Our extensive experiments show that the proposed approach achieves promising
results, highlighting the potential of self-supervised training. To the best of
our knowledge this is the first work that addresses unsupervised few-shot
segmentation problem on natural images.
