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 …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables
learning powerful and transferable video-text representations for multimodal understanding …

Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

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

Vision and text have been fully explored in contemporary video-text foundational models,
while other modalities such as audio and subtitles in videos have not received sufficient …

Recent years have witnessed a big convergence of language, vision, and multi-modal
pretraining. In this work, we present mPLUG-2, a new unified paradigm with modularized …

We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical
computer vision tasks, including pose estimation, object detection, depth estimation and …

Recently, the contrastive language-image pre-training, eg, CLIP, has demonstrated
promising results on various downstream tasks. The pre-trained model can capture enriched …