Summary of Tokenflow: Unified Image Tokenizer For Multimodal Understanding and Generation, by Liao Qu et al.
TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
by Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Ye, Daniel K. Du, Zehuan Yuan, Xinglong Wu
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents TokenFlow, a novel unified image tokenizer that bridges the gap between multimodal understanding and generation. The authors identify that previous attempts to use a single Vector Quantization (VQ) encoder for these tasks compromise performance in understanding tasks. TokenFlow addresses this challenge through a dual-codebook architecture that decouples semantic and pixel-level feature learning while maintaining alignment via a shared mapping mechanism. This design enables access to both high-level semantic representations crucial for understanding tasks and fine-grained visual features essential for generation through shared indices. The paper demonstrates TokenFlow’s superiority across multiple dimensions, achieving state-of-the-art performance in autoregressive image generation and surpassing LLaVA-1.5 13B in understanding performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TokenFlow is a new way to understand and generate images. Right now, computers have trouble doing both tasks at the same time because they need different levels of detail. The authors created TokenFlow to fix this problem by using two separate parts that work together. One part looks at big-picture ideas, while the other part focuses on tiny details. This helps computers do a better job of understanding and generating images. |
Keywords
» Artificial intelligence » Alignment » Autoregressive » Encoder » Image generation » Quantization » Tokenizer
