Summary of A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-language Tasks, by Chia Xin Liang et al.
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
by Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin, Yao Yua, Wei An-Hou, Li Ming, Tianyang Wang, Ziqian Bi, Ming Liu
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 survey and application guide to multimodal large language models (MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. The paper delves into how MLLMs integrate various data types, including text, images, video, and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. The text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. The paper concludes with a discussion of ethical considerations, responsible AI development, and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal large language models (MLLMs) are a new type of AI that can understand and generate different types of data like text, images, videos, and audio. This paper explains how MLLMs work and shows how they’re being used in different fields to improve things like storytelling and accessibility. The authors talk about the challenges of making these models better and more useful, and also discuss some of the ethical considerations that come with developing AI. |
