Summary of The Evolution Of Multimodal Model Architectures, by Shakti N. Wadekar et al.
The Evolution of Multimodal Model Architectures
by Shakti N. Wadekar, Abhishek Chaurasia, Aman Chadha, Eugenio Culurciello
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
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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 identifies four prevalent architectural patterns in contemporary multimodal models, categorizing them by their methodologies for integrating multimodal inputs into deep neural networks. The identified types are Type A (standard cross-attention), Type B (custom-designed layers), Type C (modality-specific encoders), and Type D (tokenizers). The study highlights the advantages and disadvantages of each architecture type, including data and compute requirements, complexity, scalability, and any-to-any multimodal generation capability. By characterizing these architectural patterns, this research facilitates monitoring developments in the multimodal domain and aids model selection for any-to-any multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how different types of artificial intelligence (AI) models work together to understand many kinds of data at once. It finds four main ways that these models are built, which it calls “architectures.” Each architecture is good for certain tasks and has its own strengths and weaknesses. The study helps us understand what each architecture can do well, like how much data it needs or how complex it is. This information can help us choose the right model for a particular job. |
Keywords
* Artificial intelligence * Cross attention
