Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Self-supervised representation learning (SSRL) methods aim to provide powerful, deep
feature learning without the requirement of large annotated data sets, thus alleviating the …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

The objective of this paper is visual-only self-supervised video representation learning. We
make the following contributions:(i) we investigate the benefit of adding semantic-class …

MoCo is effective for unsupervised image representation learning. In this paper, we propose
VideoMoCo for unsupervised video representation learning. Given a video sequence as an …

This paper addresses the problem of self-supervised video representation learning from a
new perspective–by video pace prediction. It stems from the observation that human visual …

The objective of this paper is self-supervised learning from video, in particular for
representations for action recognition. We make the following contributions:(i) We propose a …

Video self-supervised learning is a challenging task, which requires significant expressive
power from the model to leverage rich spatial-temporal knowledge and generate effective …

Contrastive learning has nearly closed the gap between supervised and self-supervised
learning of image representations, and has also been explored for videos. However, prior …

Skeleton-based action recognition has been widely investigated considering their strong
adaptability to dynamic circumstances and complicated backgrounds. To recognize different …