UIClip: A Data-driven Model for Assessing User Interface Design
arxiv(2024)
摘要
User interface (UI) design is a difficult yet important task for ensuring the
usability, accessibility, and aesthetic qualities of applications. In our
paper, we develop a machine-learned model, UIClip, for assessing the design
quality and visual relevance of a UI given its screenshot and natural language
description. To train UIClip, we used a combination of automated crawling,
synthetic augmentation, and human ratings to construct a large-scale dataset of
UIs, collated by description and ranked by design quality. Through training on
the dataset, UIClip implicitly learns properties of good and bad designs by i)
assigning a numerical score that represents a UI design's relevance and quality
and ii) providing design suggestions. In an evaluation that compared the
outputs of UIClip and other baselines to UIs rated by 12 human designers, we
found that UIClip achieved the highest agreement with ground-truth rankings.
Finally, we present three example applications that demonstrate how UIClip can
facilitate downstream applications that rely on instantaneous assessment of UI
design quality: i) UI code generation, ii) UI design tips generation, and iii)
quality-aware UI example search.
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