In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …

Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Developing similar techniques for satellite …

The goal of self-supervised learning from images is to construct image representations that
are semantically meaningful via pretext tasks that do not require semantic annotations. Many …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen
classes with the help of only a few labeled examples. A recent regularization technique …

We present a self-supervised learning approach to learn audio-visual representations from
video and audio. Our method uses contrastive learning for cross-modal discrimination of …

This paper is concerned with self-supervised learning for small models. The problem is
motivated by our empirical studies that while the widely used contrastive self-supervised …

In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good
representations by encouraging them to be invariant under meaningful transformations …

Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos.
The deep generative model (DGM)-based method learns the regular patterns on normal …