This repository details the book "Multimodal Large Models: A New Paradigm for Artificial Intelligence Technology." It offers a comprehensive, accessible introduction to multimodal large models, targeting advanced undergraduates, graduate students, and IT professionals. The book aims to demystify complex concepts with intuitive explanations and practical examples, guiding readers toward understanding the path to Artificial General Intelligence (AGI).

How It Works

The book systematically explains multimodal large models by detailing their key technologies, foundational architectures, and diverse applications. It adopts an accessible, in-depth yet straightforward approach, breaking down complex technical points with intuitive examples and analyzing classic model structures. The content progresses from foundational large models to core multimodal technologies, specific models, applications like VQA and embodied AI, and finally, the frontier of achieving AGI.

Quick Start & Requirements

This section pertains to accessing the book and its associated resources.

• Purchase: Available via 京东官方旗舰店 (JD.com Official Flagship Store).
• Related Resources: Includes links to open-source frameworks like CausalVLR (visual-language causal inference) and HCP-Diffusion (unified code framework), as well as an Embodied AI Paper List.
• Feedback: Via the GitHub Issues page.

Highlighted Details

• Covers foundational large models, core multimodal technologies, specific models, and applications such as Visual Question Answering (VQA), AIGC, and Embodied AI.
• Explores advanced topics for achieving Artificial General Intelligence (AGI), including causal inference, world models, embodied intelligence, and multi-agent systems.
• Authored by prominent researchers: Liu Yang (Sun Yat-sen University) and Lin Jing (IEEE Fellow, Sun Yat-sen University, Pengcheng Lab).
• Provides practical guidance on technical methods, open-source platforms, and application scenarios.

Maintenance & Community

• Associated with the Human-Computer-Physical Fusion Intelligence Lab (HCP-Lab) at Sun Yat-sen University.
• Feedback and suggestions are welcomed via the GitHub Issues page.
• Authors are affiliated with Pengcheng Lab and Sun Yat-sen University.

Licensing & Compatibility

• The README does not specify a license for the book's content.
• It references open-source frameworks (e.g., CausalVLR, HCP-Diffusion), whose individual licenses require separate verification.
• Information regarding commercial use or closed-source linking compatibility for the book's material is not provided.

Limitations & Caveats

• Chapter 5, "Multimodal Large Models Towards AGI," delves into cutting-edge research areas demanding significant reader engagement and practical application.
• The book is structured as a textbook and reference, implying a need for foundational knowledge rather than immediate, standalone implementation guidance.