Search Results for author:
Found 8 papers, 2 papers with code
ProgramAlly: Creating Custom Visual Access Programs via Multi-Modal End-User Programming
Existing visual assistive technologies are built for simple and common use cases, and have few avenues for blind people to customize their functionalities.
VRCopilot: Authoring 3D Layouts with Generative AI Models in VR
Through a series of user studies, we evaluated the potential and challenges in manual, scaffolded, and automatic creation in immersive authoring.
EditScribe: Non-Visual Image Editing with Natural Language Verification Loops
To address this, we developed EditScribe, a prototype system that makes image editing accessible using natural language verification loops powered by large multimodal models.
WorldScribe: Towards Context-Aware Live Visual Descriptions
In this work, we develop WorldScribe, a system that generates automated live real-world visual descriptions that are customizable and adaptive to users' contexts: (i) WorldScribe's descriptions are tailored to users' intents and prioritized based on semantic relevance.
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple.
StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible
Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface.
Toward Fairness in AI for People with Disabilities: A Research Roadmap
AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD).
VizWiz Grand Challenge: Answering Visual Questions from Blind People
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings.
