Overview of Multimodal Large Models

Previously, we introduced the Large Language Models (LLMs) technology principles and applications. LLMs are a type of Foundation model, and besides LLMs, Foundation models also include Large Vision Models and Large Multimodal Models.

Currently popular text-to-image models like Stable Diffusion, DALL-E, text-to-video model Sora, image-text retrieval, and visual content generation all fall under the category of multimodal large models. Today, I would like to recommend an overview paper on multimodal large models, and we will also release papers on visual large models in the future, so please stay tuned.

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Below is a mind map summarizing the document’s content:

Multimodal Foundation Models
Definitions and Background
The Importance of Multimodal Foundation Models
Transition from Expert Models to General Assistants
Research Areas
Visual Understanding
Supervised Pre-training
Contrastive Learning
Self-supervised Learning
Visual Generation
Text-to-Image Generation
Spatially Controlled Generation
Text Prompt Adherence
Concept Customization
Unified Visual Models
From Closed-set to Open-set Models
Task-specific Models to General Models
From Static to Promptable Models
Large Multimodal Models
Background
Pre-training Guidance
Case Studies of Large Multimodal Models
Advanced Topics in Multimodal Agents
Multimodal Agents
Overview of Multimodal Agents
Case Study of Multimodal Agents: MM-REACT
Advanced Topics in Multimodal Agents
Evaluation of Multimodal Agents
Summary and Research Trends
Summary
Building General AI Agents

Overview of Multimodal Large Models

The above diagram illustrates three representative problems that multimodal large models attempt to solve: visual understanding tasks, visual generation tasks, and a general interface with language understanding and generation capabilities.

Overview of Multimodal Large Models

The above diagram describes the development trends of foundation models in the language and visual/multimodal domains, highlighting the trend of evolution from specialized models to general assistants and emphasizing the need for further research to determine how to best achieve this transition.
Overview of Multimodal Large Models
The structure of the entire paper’s sections

Overview of Multimodal Large Models

Overview of Multimodal Large Models

Summary of Visual Understanding
Overview of Multimodal Large Models
Overview of Multimodal Large Models

The development of large language models relies not only on computational power and data factors but also on the advancement of model architectures. Transformer is the foundation of language large models, while ViT is the foundation of visual large models.

Overview of Multimodal Large Models

Summary of Visual Content Generation
Overview of Multimodal Large Models
Timeline of Major Events in Image Generation
Overview of Multimodal Large Models
GANs are adept at generating realistic images that closely resemble those in the training set, while VAEs excel at creating a variety of images. Existing models have not successfully combined these two functionalities until the emergence of Stable Diffusion, which integrates the advantages of GAN and VAE, capable of generating realistic and diverse images.
Overview of Multimodal Large Models
CLIP is a pioneering work that maps language and images into a unified embedding space, serving as the foundation of multimodal large models.
Overview of Multimodal Large Models
Overview of Multimodal Large Models
BeiT, MAE, and IGPT have opened the era of image pre-training, BERT in CV.

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