Self-supervised learning has gained popularity because of its ability to avoid the cost of
annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as …

Although deep learning algorithms have achieved significant progress in a variety of
domains, they require costly annotations on huge datasets. Self-supervised learning (SSL) …

Machine learning has become an increasingly ubiquitous technology, as big data continues
to inform and influence everyday life and decision-making. Currently, in medicine and …

Deep learning methods have become an integral part of computer vision and machine
learning research by providing significant improvement performed in many tasks such as …

In recent years, the development of convolutional neural networks (CNNs) has promoted
continuous progress in scene classification of remote sensing images. Compared with …

Self-supervised pre-training for 3D vision has drawn increasing research interest in recent
years. In order to learn informative representations, a lot of previous works exploit …

Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …

Driven by the urgent demand for flood monitoring, water resource management and
environmental protection, water-body detection in remote sensing imagery has attracted …

The use of machine learning (ML) techniques in affective computing applications focuses on
improving the user experience in emotion recognition. The collection of input data (eg …

This paper provides insights into the interpretation beyond simply combining self-supervised
learning (SSL) with remote sensing (RS). Inspired by the improved representation ability …