Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks

Understand

Self-supervised learning aims to learn image feature representations without the usage of manually annotated labels.

• It is often used as a precursor step to obtain useful initial network weights which contribute to faster convergence and superior performance of downstream tasks.
• While self-supervision allows one to reduce the domain gap between supervised and unsupervised learning without the usage of labels, the self-supervised objective still requires a strong inductive bias to downstream tasks for effective transfer learning.
• In this work, we present our material and texture based self-supervision method named MATTER (MATerial and TExture Representation Learning), which is inspired by classical material and texture methods.