61 citations · 76 across the 6 of their papers we have counts for
7 papers · 1 filter
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
Fred Hohman, Chaoqun Wang, Jinmook Lee +7
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on…
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…
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 Parsing: Towards Reverse Engineering of UI Models from Screenshots
Jason Wu, Xiaoyi Zhang, Jeff Nichols +1
Automated understanding of user interfaces (UIs) from their pixels can improve accessibility, enable task automation, and facilitate interface design without relying on developers…
When Can Accessibility Help?: An Exploration of Accessibility Feature Recommendation on Mobile Devices
Jason Wu, Gabriel Reyes, Sam C. White +2
Numerous accessibility features have been developed and included in consumer operating systems to provide people with a variety of disabilities additional ways to access computing…