Summary of Matryoshka Multimodal Models, by Mu Cai et al.
Matryoshka Multimodal Models
by Mu Cai, Jianwei Yang, Jianfeng Gao, Yong Jae Lee
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 proposes M3: Matryoshka Multimodal Models, a novel approach for Large Multimodal Models (LMMs) like LLaVA. These models excel in visual-linguistic reasoning tasks but suffer from excessive tokenization for dense visual scenarios like high-resolution images and videos. The authors draw inspiration from Matryoshka Dolls to develop M3, which represents visual content as nested sets of tokens that capture information across multiple granularities. This design allows for explicit control over the granularity per test instance during inference, enabling adjustments based on anticipated complexity or simplicity. Additionally, M3 provides a framework for analyzing the required granularity for existing datasets and exploring the best trade-off between performance and token length at the sample level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary M3 is a new way to help Large Multimodal Models understand pictures and words better. These models are great at some tasks, but they use too many “words” (called tokens) when dealing with very detailed images or videos. The creators of M3 got their idea from Russian dolls, which have layers that fit inside each other. They used this idea to make a new kind of model that can represent pictures in different levels of detail. This lets the model adjust its level of detail based on what it’s looking at and makes it better for certain tasks. |
Keywords
» Artificial intelligence » Inference » Token » Tokenization
