  Humans can learn to operate the user interface (UI) of an application by
reading an instruction manual or how-to guide. Along with text, these resources
include visual content such as UI screenshots and images of application icons
referenced in the text. We explore how to leverage this data to learn generic
visio-linguistic representations of UI screens and their components. These
representations are useful in many real applications, such as accessibility,
voice navigation, and task automation. Prior UI representation models rely on
UI metadata (UI trees and accessibility labels), which is often missing,
incompletely defined, or not accessible. We avoid such a dependency, and
propose Lexi, a pre-trained vision and language model designed to handle the
unique features of UI screens, including their text richness and context
sensitivity. To train Lexi we curate the UICaption dataset consisting of 114k
UI images paired with descriptions of their functionality. We evaluate Lexi on
four tasks: UI action entailment, instruction-based UI image retrieval,
grounding referring expressions, and UI entity recognition.
