Self-supervised Learning in Computer Vision: A Review

Abstract

In computer vision, data-driven convolutional neural networks could learn increasingly rich semantic features of images. However, manual annotation of images is an expensive and time-consuming task that hinders development. As a branch of unsupervised learning, self-supervised learning does not rely on labels, avoiding the work of labeling the data. This paper provides a comprehensive discussion of the development of self-supervised learning in computer vision. First, we briefly describes the motivation for proposing self-supervised learning and related concepts, introduces the self-supervised learning paradigm from three aspects, describes the applications of self-supervised learning in computer vision, and finally provides a summary and an outlook on its future development.