We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

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 …

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 …

We wish to automatically predict the" speediness" of moving objects in videos-whether they
move faster, at, or slower than their" natural" speed. The core component in our approach is …

The recent advance in vision-language models is largely attributed to the abundance of
image-text data. We aim to replicate this success for video-language models but there …

As the proportion of elderly individuals in developed countries continues to rise globally,
addressing their healthcare needs, particularly in preserving their autonomy, is of paramount …

We study unsupervised video representation learning that seeks to learn both motion and
appearance features from unlabeled video only, which can be reused for downstream tasks …

Abstract Object State Changes (OSCs) are pivotal for video understanding. While humans
can effortlessly generalize OSC understanding from familiar to unknown objects current …

Surprising videos, eg, funny clips, creative performances, or visual illusions, attract
significant attention. Enjoyment of these videos is not simply a response to visual stimuli; …