  Self-supervision can dramatically cut back the amount of manually-labelled
data required to train deep neural networks. While self-supervision has usually
been considered for tasks such as image classification, in this paper we aim at
extending it to geometry-oriented tasks such as semantic matching and part
detection. We do so by building on several recent ideas in unsupervised
landmark detection. Our approach learns dense distinctive visual descriptors
from an unlabelled dataset of images using synthetic image transformations. It
does so by means of a robust probabilistic formulation that can introspectively
determine which image regions are likely to result in stable image matching. We
show empirically that a network pre-trained in this manner requires
significantly less supervision to learn semantic object parts compared to
numerous pre-training alternatives. We also show that the pre-trained
representation is excellent for semantic object matching.
