Multimodal Foundation Models: From Specialists to General-Purpose
  Assistants
Chunyuan Li Zhe Gan Zhengyuan Yang Jianwei Yang Linjie Li Lijuan Wang Jianfeng Gao
This paper presents a comprehensive survey of the taxonomy and evolution of
multimodal foundation models that demonstrate vision and vision-language
capabilities, focusing on the transition from specialist models to
general-purpose assistants. The research landscape encompasses five core
topics, categorized into two classes. (i) We start with a survey of
well-established research areas: multimodal foundation models pre-trained for
specific purposes, including two topics -- methods of learning vision backbones
for visual understanding and text-to-image generation. (ii) Then, we present
recent advances in exploratory, open research areas: multimodal foundation
models that aim to play the role of general-purpose assistants, including three
topics -- unified vision models inspired by large language models (LLMs),
end-to-end training of multimodal LLMs, and chaining multimodal tools with
LLMs. The target audiences of the paper are researchers, graduate students, and
professionals in computer vision and vision-language multimodal communities who
are eager to learn the basics and recent advances in multimodal foundation
models.