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 remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot
video and text understanding, without using any labels on downstream tasks. VideoCLIP …

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 …

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 …

We study joint video and language (VL) pre-training to enable cross-modality learning and
benefit plentiful downstream VL tasks. Existing works either extract low-quality video …