Summary of Unicode: Learning a Unified Codebook For Multimodal Large Language Models, by Sipeng Zheng et al.
UniCode: Learning a Unified Codebook for Multimodal Large Language Models
by Sipeng Zheng, Bohan Zhou, Yicheng Feng, Ye Wang, Zongqing Lu
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed UniCode approach for multimodal large language models (MLLMs) learns a unified codebook to efficiently tokenize visual, text, and other signals. This addresses the limitation of existing MLLMs relying on text-only codebooks, restricting their ability to generate images and texts in multimodal contexts. The paper proposes a language-driven iterative training paradigm with an in-context pre-training task called “image decompression,” enabling the model to interpret compressed visual data and generate high-quality images. The unified codebook empowers the model to extend visual instruction tuning to non-linguistic generation tasks. Unicode also demonstrates adaptability to diverse stacked quantization approaches, compressing visual signals into a more compact token representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UniCode is a new way for computers to understand and work with different types of data, like pictures and words. Right now, machines are good at working with just one type of data at a time. UniCode helps fix this by teaching the machine how to look at all kinds of data in the same way. This lets the machine create new images and texts that are really good. It also lets the machine learn from smaller amounts of data, which is helpful for big tasks like recognizing objects in pictures. |
Keywords
» Artificial intelligence » Instruction tuning » Quantization » Token
