The ScreenAI model is trained in two stages: a pre-training stage and a fine-tuning stage. The pre-training stage involves self-supervised learning to automatically generate data labels, while the fine-tuning stage uses data manually labeled by human raters. The article also discusses the creation of a pre-training dataset for ScreenAI, the use of a layout annotator to identify and label UI elements, and the application of an optical character recognition (OCR) engine to extract and annotate textual content on screen.
Key takeaways:
- ScreenAI, a vision-language model for user interfaces and infographics, has been introduced, achieving state-of-the-art results on UI and infographics-based tasks.
- ScreenAI is based on the PaLI architecture and uses a flexible patching strategy from pix2struct, allowing it to understand, reason, and interact with complex and varied UIs and infographics.
- Three new datasets, Screen Annotation, ScreenQA Short and Complex ScreenQA, have been released for a comprehensive evaluation of ScreenAI's layout understanding and QA capabilities.
- Despite achieving competitive performance on a number of public benchmarks, ScreenAI still lags behind larger models, indicating a need for further research.