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This monograph 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 focus encompasses five core topics, categorized into two classes; (i) 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) 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 audience of the monograph is 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.

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