Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …

Conversation agents fueled by Large Language Models (LLMs) are providing a new way to
interact with visual data. While there have been initial attempts for image-based …

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 Foundation Models (VFMs) have received limited exploration due to high
computational costs and data scarcity. Previous VFMs rely on Image Foundation Models …

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP
model. Since training on a similar scale for videos is infeasible, recent approaches focus on …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

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 …

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have
demonstrated impressive transferability on various visual tasks. Transferring knowledge …