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 …

The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Understanding verbs is crucial to modelling how people and objects interact with each other
and the environment through space and time. Recently, state-of-the-art video-language …

We present a self-supervised Contrastive Video Representation Learning (CVRL) method to
learn spatiotemporal visual representations from unlabeled videos. Our representations are …

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 …

Micro-expression (ME) is a significant non-verbal communication clue that reveals one
person's genuine emotional state. The development of micro-expression analysis (MEA) has …

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 …

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 …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …