UIClip: A Data-driven Model for Assessing User Interface Design

TL;DR

UIClip is a machine learning model that assesses UI design quality and relevance from screenshots and descriptions, enabling applications like code generation and design tips with high agreement to human judgments.

Contribution

We introduce UIClip, a novel data-driven model for UI design assessment that learns from a large-scale dataset and provides practical applications for improving UI development.

Findings

UIClip outperforms baselines in agreement with human ratings.

The model can generate design suggestions and relevance scores.

Applications include UI code generation and quality-aware search.

Abstract

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.