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.

本文介绍了自监督学习在减少人工标注数据用于训练深度神经网络方面的应用，并指出了该方法对于几何定向任务，如语义匹配和部件检测的可扩展性，同时展示了自监督预训练网络在学习语义对象部件方面相较于其它预训练选项所需较少的监督，并且这种预训练对于语义对象匹配非常优秀。