Summary of Multi-modal Generative Ai: Multi-modal Llm, Diffusion and Beyond, by Hong Chen et al.
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond
by Hong Chen, Xin Wang, Yuwei Zhou, Bin Huang, Yipeng Zhang, Wei Feng, Houlun Chen, Zeyang Zhang, Siao Tang, Wenwu Zhu
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to unify two dominant families of techniques in multi-modal generative AI: multi-modal large language models (MLLMs) like GPT-4V and diffusion models like Sora. The study reviews the probabilistic modeling procedures, architectures, and applications of both MLLMs and diffusion models, focusing on their capabilities in image/video generation and text-to-image/video tasks. The authors then explore the design choices for a unified model, considering whether to adopt auto-regressive or diffusion probabilistic modeling, as well as architecture designs that support both generation and understanding. The paper also discusses strategies for building a unified model and analyzes its potential advantages and disadvantages. Finally, it summarizes existing large-scale multi-modal datasets for future model pretraining and outlines challenging directions for ongoing advancements in multi-modal generative AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-modal generative AI is a new field that combines different forms of data, like text, images, and videos, to create new content. The goal is to make one model that can understand and generate all these types of data. The paper looks at two main approaches: large language models and diffusion models. It reviews how these models work, what they’re used for, and their strengths and weaknesses. Then, it discusses the challenges of creating a single model that can do both understanding and generation. The authors also suggest ways to build this unified model and highlight some limitations. Finally, they talk about big datasets that could be used to train future models. |
Keywords
» Artificial intelligence » Diffusion » Gpt » Multi modal » Pretraining
