50 citations · 93 across the 14 of their papers we have counts for
11 papers · 1 filter
Improving User Interface Generation Models from Designer Feedback
Jason Wu, Amanda Swearngin, Arun Krishna Vajjala +3
Despite being trained on vast amounts of data, most LLMs are unable to reliably generate well-designed UIs. Designer feedback is essential to improving performance on UI generation…
From Interaction to Impact: Towards Safer AI Agents Through Understanding and Evaluating Mobile UI Operation Impacts
Zhuohao Jerry Zhang, Eldon Schoop, Jeffrey Nichols +2
With advances in generative AI, there is increasing work towards creating autonomous agents that can manage daily tasks by operating user interfaces (UIs). While prior research has…
Misty: UI Prototyping Through Interactive Conceptual Blending
Yuwen Lu, Alan Leung, Amanda Swearngin +2
UI prototyping often involves iterating and blending elements from examples such as screenshots and sketches, but current tools offer limited support for incorporating these exampl…
UIClip: A Data-driven Model for Assessing User Interface Design
Jason Wu, Yi-Hao Peng, Amanda Li +3
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-…
Towards Automated Accessibility Report Generation for Mobile Apps
Amanda Swearngin, Jason Wu, Xiaoyi Zhang +8
Many apps have basic accessibility issues, like missing labels or low contrast. Automated tools can help app developers catch basic issues, but can be laborious or require writing…
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations
Yue Jiang, Eldon Schoop, Amanda Swearngin +1
Multimodal Vision-Language Models (VLMs) enable powerful applications from their fused understanding of images and language, but many perform poorly on UI tasks due to the lack of…
AXNav: Replaying Accessibility Tests from Natural Language
Maryam Taeb, Amanda Swearngin, Eldon Schoop +3
Developers and quality assurance testers often rely on manual testing to test accessibility features throughout the product lifecycle. Unfortunately, manual testing can be tedious,…
Never-ending Learning of User Interfaces
Jason Wu, Rebecca Krosnick, Eldon Schoop +3
Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most…
Screen Correspondence: Mapping Interchangeable Elements between UIs
Jason Wu, Amanda Swearngin, Xiaoyi Zhang +2
Understanding user interface (UI) functionality is a useful yet challenging task for both machines and people. In this paper, we investigate a machine learning approach for screen…
Extracting Replayable Interactions from Videos of Mobile App Usage
Jieshan Chen, Amanda Swearngin, Jason Wu +3
Screen recordings of mobile apps are a popular and readily available way for users to share how they interact with apps, such as in online tutorial videos, user reviews, or as atta…
Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels
Xiaoyi Zhang, Lilian de Greef, Amanda Swearngin +9
Many accessibility features available on mobile platforms require applications (apps) to provide complete and accurate metadata describing user interface (UI) components. Unfortuna…