With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

As transformer evolves, pre-trained models have advanced at a breakneck pace in recent
years. They have dominated the mainstream techniques in natural language processing …

The tremendous success of CLIP (Radford et al., 2021) has promoted the research and
application of contrastive learning for vision-language pretraining. In this work, we construct …

Abstract Vision-Language Pre-training (VLP) models have shown remarkable performance
on various downstream tasks. Their success heavily relies on the scale of pre-trained cross …

In this work, we present a conceptually simple and effective method to train a strong
bilingual/multilingual multimodal representation model. Starting from the pre-trained …

Abstract Large-scale Vision-and-Language (V+ L) pre-training for representation learning
has proven to be effective in boosting various downstream V+ L tasks. However, when it …

This open access book provides a comprehensive overview of the state of the art in research
and applications of Foundation Models and is intended for readers familiar with basic …

People perceive the world with multiple senses (eg, through hearing sounds, reading words
and seeing objects). However, most existing AI systems only process an individual modality …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

Multimodal pretraining has made convincing achievements in various downstream tasks in
recent years. However, since the majority of the existing works construct models based on …