Author: Oscar Mañas
Supervisors: Xavier Giró
Presentation time: 11:15
Virtual Room: 2.4 | Live presentation URL
With the creation of large-scale annotated datasets such as the ImageNet, fully-supervised machine learning methods have become the standard for solving computer vision tasks. These methods require large amounts of annotated data, which is usually obtained with crowdsourcing tools or social media tags. However, these approaches do not scale for specialized domains, such as medical or satellite imaging, where annotations must be provided by experts at a prohibitive cost. Recently, self-supervised learning has emerged as an alternative for obtaining transferable visual representations from unlabeled data. Models based on these representations match the performance of fully-supervised models while only requiring a small fraction of the annotations. In this project, we aim to explore the application of self-supervised learning methods in the remote sensing domain.