Summary of The Revolution Of Multimodal Large Language Models: a Survey, by Davide Caffagni et al.
The Revolution of Multimodal Large Language Models: A Survey
by Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 paper reviews recent developments in Multimodal Large Language Models (MLLMs), which combine visual and textual modalities to enable dialogue-based interfaces and instruction-following capabilities. The authors analyze the architectural choices, multimodal alignment strategies, and training techniques used in various MLLMs, as well as their performance across tasks such as visual grounding, image generation and editing, visual understanding, and domain-specific applications. They also compile and describe training datasets and evaluation benchmarks, comparing existing models’ performance and computational requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special kinds of computer programs called Multimodal Large Language Models (MLLMs). These programs can understand both words and pictures. The authors study different ways that MLLMs are built and how they work together with text and images. They also test these programs on various tasks, such as recognizing objects in pictures or generating new images. The paper helps us understand what’s currently possible with MLLMs and what we might see in the future. |
Keywords
» Artificial intelligence » Alignment » Grounding » Image generation
