Summary of Efficient Multimodal Large Language Models: a Survey, by Yizhang Jin et al.
Efficient Multimodal Large Language Models: A Survey
by Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu, Kai Wu, Zhengkai Jiang, Muyang He, Bo Zhao, Xin Tan, Zhenye Gan, Yabiao Wang, Chengjie Wang, Lizhuang Ma
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This survey provides a comprehensive review of efficient Multimodal Large Language Models (MLLMs), which have shown remarkable performance in tasks such as visual question answering and visual understanding. However, their large size and high training costs limit their widespread adoption. The paper summarizes the development timeline, structural strategies, and applications of efficient MLLMs, highlighting their potential in edge computing scenarios. It also discusses the limitations of current research and proposes future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at special kinds of language models called Multimodal Large Language Models (MLLMs). These models are very good at answering questions and understanding pictures. But they’re also really big and use a lot of energy, which makes it hard to use them everywhere. The authors of this paper want to help make MLLMs smaller and cheaper so we can use them more easily. They look at how other people have made MLLMs smaller and what kinds of problems they’ve solved with these models. |
Keywords
» Artificial intelligence » Question answering
