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Modeling user interfaces (UIs) from visual information allows systems to makeinferences about the functionality and semantics needed to support use cases inaccessibility, app automation, and testing. Current datasets for trainingmachine learning models are limited in size due to the costly andtime-consuming process of manually collecting and annotating UIs. We crawledthe web to construct WebUI, a large dataset of 400,000 rendered web pagesassociated with automatically extracted metadata. We analyze the composition ofWebUI and show that while automatically extracted data is noisy, most examplesmeet basic criteria for visual UI modeling. We applied several strategies forincorporating semantics found in web pages to increase the performance ofvisual UI understanding models in the mobile domain, where less labeled data isavailable: (i) element detection, (ii) screen classification and (iii) screensimilarity.

WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics