Summary of Automatic Layout Planning For Visually-rich Documents with Instruction-following Models, by Wanrong Zhu et al.
Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models
by Wanrong Zhu, Jennifer Healey, Ruiyi Zhang, William Yang Wang, Tong Sun
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 novel multimodal instruction-following framework introduced in this work enables non-professional users to easily create visually appealing layouts by specifying canvas size and design purpose. The framework is designed for graphic design, where limited skills and resources often hinder the creation of tailored layouts. To train the model, three layout reasoning tasks were developed to understand and execute layout instructions. Experimental results show that the method simplifies the design process and outperforms few-shot GPT-4V models on two benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easy for people who aren’t professional designers to create nice-looking layouts by following simple instructions. For example, you can tell the model what kind of layout you want (like a book cover or menu) and it will take care of the rest. The researchers created special tasks to teach the model how to understand and follow these instructions. The results show that this method makes design more accessible and even beats other powerful models at creating good layouts. |
Keywords
» Artificial intelligence » Few shot » Gpt
