Summary of From Pixels to Tokens: Byte-pair Encoding on Quantized Visual Modalities, by Wanpeng Zhang et al.
From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities
by Wanpeng Zhang, Zilong Xie, Yicheng Feng, Yijiang Li, Xingrun Xing, Sipeng Zheng, Zongqing Lu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 paper introduces a novel image tokenizer that applies Byte-Pair Encoding (BPE) to visual data, enabling large language models to better integrate textual and visual information. The proposed method incorporates structural prior information into image tokens, similar to successful tokenization strategies in text-only models. This approach allows Transformer models to learn and reason across modalities more effectively. Theoretical analysis and experiments demonstrate that the BPE Image Tokenizer enhances multimodal understanding capabilities, even with limited training data. A model developed using this method, Being-VL-0, shows superior performance on various benchmarks and has potential for scalability, potentially paving the way for more efficient foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal language models have made great progress in combining text and images, but they often struggle to understand these elements together. The paper presents a new way to divide up visual information into small pieces that can be easily processed by computers. This approach is inspired by how we process written words and mirrors successful strategies used in text-only models. By directly incorporating structural prior information into image tokens, the method helps Transformer models learn and reason better across different types of data. The paper shows that this new tokenizer enhances a model’s ability to understand both text and images, even with limited training data. |
Keywords
» Artificial intelligence » Tokenization » Tokenizer » Transformer
