This paper explores feature prediction as a stand-alone objective for unsupervised learning
from video and introduces V-JEPA, a collection of vision models trained solely using a …

We introduce VideoPrism, a general-purpose video encoder that tackles diverse video
understanding tasks with a single frozen model. We pretrain VideoPrism on a …

Video Foundation Models (ViFMs) aim to develop general-purpose representations for
various video understanding tasks by leveraging large-scale datasets and powerful models …

The analysis of traffic patterns is crucial for enhancing safety and optimizing flow within
urban cities. While urban cities possess extensive camera networks for monitoring the raw …

Humans use multiple senses to comprehend the environment. Vision and language are two
of the most vital senses since they allow us to easily communicate our thoughts and …

This paper shows that the masked-modelling principle driving the success of large
foundational language models can be effectively applied to video by making predictions in …

With the growth of high-quality data and advancement in visual pre-training paradigms,
Video Foundation Models (VFMs) have made significant progress recently, demonstrating …

As video understanding (VU) promises unprecedented capabilities in the era of video data
explosion, its efficient computation plays a critical role in practicalizing the algorithmic …

Computational approaches leveraging computer vision and machine learning have
transformed the quantification of animal behavior from video. However, existing methods …

We explore a novel video creation experience, namely Video Creation by Demonstration.
Given a demonstration video and a context image from a different scene, we generate a …