Summary of Dreamtext: High Fidelity Scene Text Synthesis, by Yibin Wang and Weizhong Zhang and Honghui Xu and Cheng Jin
DreamText: High Fidelity Scene Text Synthesis
by Yibin Wang, Weizhong Zhang, Honghui Xu, Cheng Jin
First submitted to arxiv on: 23 May 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 DreamText for high-fidelity scene text synthesis by reconstructing the diffusion training process. The key idea is to introduce refined guidance at the character level, exposing and rectifying the model’s attention to strengthen its learning of text regions. A hybrid optimization challenge involving both discrete and continuous variables is posed, which is tackled using a heuristic alternate optimization strategy. Jointly training the text encoder and generator enables comprehensive learning and utilization of diverse font styles in the training dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better write words on pictures. Right now, computers have trouble writing words on different types of images because they don’t understand how to make characters look good. The authors came up with a new way to train computers to write words that looks more realistic. They did this by giving the computer hints about what makes a character look right. This helps the computer learn from its mistakes and get better at writing words on different types of images. |
Keywords
» Artificial intelligence » Attention » Diffusion » Encoder » Optimization
