Summary of Empowering Backbone Models For Visual Text Generation with Input Granularity Control and Glyph-aware Training, by Wenbo Li et al.
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
by Wenbo Li, Guohao Li, Zhibin Lan, Xue Xu, Wanru Zhuang, Jiachen Liu, Xinyan Xiao, Jinsong Su
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 several methods to enhance the performance of diffusion-based text-to-image models in generating legible visual texts. The existing backbone models have limitations, such as misspelling, failing to generate texts, and lack of support for Chinese text. By analyzing these issues, the authors design a mixed granularity input strategy and propose three glyph-aware training losses to improve the learning of cross-attention modules. These enhancements enable the models to generate semantic-relevant, aesthetically appealing, and accurate visual text images while maintaining their fundamental image generation quality. The paper demonstrates promising potential in empowering backbone models for English and Chinese text-to-image generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper tries to improve a type of computer program that can create images from texts. Currently, these programs are good at making beautiful pictures but struggle with writing words that make sense. The authors want to fix this problem by making the program better understand what words mean and how they should look in an image. They try different ways to make the program learn and get better at generating text-based images that are easy to read. The result is a more accurate and visually appealing way for computers to create text-based images. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Image generation
