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 …

Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

We introduce LAVILA, a new approach to learning video-language representations by
leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be …

Abstract Video-Language Pretraining (VLP), which aims to learn transferable representation
to advance a wide range of video-text downstream tasks, has recently received increasing …

Video-language pre-training (VLP) has become increasingly important due to its ability to
generalize to various vision and language tasks. However, existing egocentric VLP …

Pre-training a model to learn transferable video-text representation for retrieval has attracted
a lot of attention in recent years. Previous dominant works mainly adopt two separate …

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 …

The task of retrieving video content relevant to natural language queries plays a critical role
in effectively handling internet-scale datasets. Most of the existing methods for this caption-to …

Videos are a rich source of multi-modal supervision. In this work, we learn representations
using self-supervision by leveraging three modalities naturally present in videos: visual …

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-
KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M …