Summary of Anytrans: Translate Anytext in the Image with Large Scale Models, by Zhipeng Qian et al.
AnyTrans: Translate AnyText in the Image with Large Scale Models
by Zhipeng Qian, Pei Zhang, Baosong Yang, Kai Fan, Yiwei Ma, Derek F. Wong, Xiaoshuai Sun, Rongrong Ji
First submitted to arxiv on: 17 Jun 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 introduces AnyTrans, an all-encompassing framework for Translate AnyText in the Image (TATI) that combines multilingual text translation and text fusion within images. The framework leverages Large Language Models (LLMs) and text-guided diffusion models to incorporate contextual cues from both textual and visual elements during translation. Few-shot learning capabilities allow for the translation of fragmented texts by considering overall context, while advanced inpainting and editing abilities enable seamless fusion of translated text into original images preserving style and realism. The framework can be constructed entirely using open-source models, requiring no training, making it highly accessible and easily expandable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to translate words and images together called Translate AnyText in the Image (TATI). They made a system called AnyTrans that combines big language models and image editing tools. This helps machines understand the context of what’s being translated, like if it’s part of a bigger text or image. The result is accurate translations that fit seamlessly into the original image. The best part is that this system doesn’t need to be trained from scratch, making it easy for others to use and build upon. |
Keywords
» Artificial intelligence » Few shot » Translation
