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Full Description
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.
Contents
1. Introduction
2. Visual Understanding
3. Visual Generation
4. Unified Vision Models
5. Large Multimodal Models: Training with LLMs
6. Multimodal Agents: Chaining Tools with LLM
7. Conclusion and Research Trends
Acknowledgments
References
