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 …

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

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 …

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 …

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 …

Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various
video understanding tasks. Leveraging large-scale datasets and powerful models, ViFMs …

The proliferation of video collections and the increased capabilities of machine learning
models have led to a growing desire for video analytics—the process of extracting insights …

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 …