ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy of pix2struct and is trained on a unique mixture of datasets. At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements. We use these text annotations to describe screens to Large Language Models and automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. We run ablation studies to demonstrate the impact of these design choices. At only 5B parameters, ScreenAI achieves new state-of-the-artresults on UI- and infographics-based tasks (Multi-page DocVQA, WebSRC, MoTIF and Widget Captioning), and new best-in-class performance on others (Chart QA, DocVQA, and InfographicVQA) compared to models of similar size. Finally, we release three new datasets: one focused on the screen annotation task and two others focused on question answering.
PDF Abstract
Datasets
Introduced in the Paper:
ScreenQA ShortUsed in the Paper:
DocVQA ChartQA
InfographicVQA
Screen2Words
MP-DocVQA
OCR-VQA
Results from the Paper
Ranked #3 on
Visual Question Answering (VQA)
on InfographicVQA
(using extra training data)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Chart Question Answering | ChartQA | ScreenAI 5B (4.62 B params, w/ OCR) | 1:1 Accuracy | 76.7 | # 5 | ||
| Visual Question Answering (VQA) | DocVQA test | ScreenAI 5B (4.62 B params, w/OCR) | ANLS | 0.8988 | # 5 | ||
| Visual Question Answering (VQA) | InfographicVQA | ScreenAI 5B (4.62 B params, w/ OCR) | ANLS | 65.90 | # 3 |
