In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

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 …

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 …

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

Web-crawled pretraining datasets underlie the impressive" zero-shot" evaluation
performance of multimodal models, such as CLIP for classification and Stable-Diffusion for …

Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …

Multi-modal large language models (MLLMs) have been shown to efficiently integrate
natural language with visual information to handle multi-modal tasks. However MLLMs still …

With the emergence of pre-trained vision-language models like CLIP how to adapt them to
various downstream classification tasks has garnered significant attention in recent …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

Misinformation has become a pressing issue. Fake media, in both visual and textual forms,
is widespread on the web. While various deepfake detection and text fake news detection …