Summary of Image2struct: Benchmarking Structure Extraction For Vision-language Models, by Josselin Somerville Roberts et al.
Image2Struct: Benchmarking Structure Extraction for Vision-Language Models
by Josselin Somerville Roberts, Tony Lee, Chi Heem Wong, Michihiro Yasunaga, Yifan Mai, Percy Liang
First submitted to arxiv on: 29 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 The proposed Image2Struct benchmark evaluates vision-language models (VLMs) in extracting structure from images, mimicking real-world use cases. The benchmark is fully automatic, using a renewable stream of fresh data to generate the underlying structure (e.g., LaTeX code or HTML) from an input image (e.g., webpage screenshot). This round-trip evaluation allows for quantitative assessment of VLMs on tasks with multiple valid structures. The pipeline downloads fresh data from online communities and evaluates VLMs without human intervention, introducing three domains (Webpages, LaTeX, and Musical Scores) and five image metrics (pixel similarity, cosine similarity between Inception vectors, learned perceptual image patch similarity, structural similarity index measure, and earth mover similarity). The proposed benchmark is applied to 14 prominent VLMs, revealing varying scores that differentiate between the performances of different models. Additionally, the best score varies across domains, indicating tasks of differing difficulty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image2Struct is a new way to test how well artificial intelligence (AI) can understand images and turn them into text or instructions. The goal is to see if AI can extract the main points from an image, like the layout of a webpage or the notes in a piece of music. To do this, we made a special tool that takes an image as input and produces text or instructions based on what it sees. We tested this tool with many different AI models and found that some are better than others at doing this task. This means that Image2Struct can help us figure out which AI models are the best at understanding images and turning them into something useful. |
Keywords
» Artificial intelligence » Cosine similarity
