Summary of Iw-bench: Evaluating Large Multimodal Models For Converting Image-to-web, by Hongcheng Guo et al.
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
by Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper addresses the lack of robust benchmarks for assessing the image-to-Web conversion proficiency of large multimodal models, particularly ensuring the integrity of web elements. The authors propose Element Accuracy and Layout Accuracy metrics to evaluate the completeness and positional relationships of web elements, respectively. A benchmark called IW-Bench is curated, comprising 1200 pairs of images and corresponding web codes with varying levels of difficulty. The authors also design a five-hop multimodal Chain-of-Thought Prompting approach for better performance. Experimental results on existing large multimodal models provide insights into their performance and areas for improvement in the image-to-web domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well computers can understand images and turn them into web pages. Right now, there’s no good way to check if a computer is doing this correctly. The authors create a special set of examples called IW-Bench, which includes 1200 pairs of images and the correct web code for each one. They also come up with new ways to measure how well computers are doing this task, like checking if all the important parts of the web page are included and if they’re in the right place. |
Keywords
» Artificial intelligence » Prompting
