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4 results on '"Bodi, Aditya"'

1. NarrationBot and InfoBot: A Hybrid System for Automated Video Description

Author

Ihorn, Shasta, Siu, Yue-Ting, Bodi, Aditya, Narins, Lothar, Castanon, Jose M., Kant, Yash, Das, Abhishek, Yoon, Ilmi, and Fazli, Pooyan

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Video accessibility is crucial for blind and low vision users for equitable engagements in education, employment, and entertainment. Despite the availability of professional and amateur services and tools, most human-generated descriptions are expensive and time consuming. Moreover, the rate of human-generated descriptions cannot match the speed of video production. To overcome the increasing gaps in video accessibility, we developed a hybrid system of two tools to 1) automatically generate descriptions for videos and 2) provide answers or additional descriptions in response to user queries on a video. Results from a mixed-methods study with 26 blind and low vision individuals show that our system significantly improved user comprehension and enjoyment of selected videos when both tools were used in tandem. In addition, participants reported no significant difference in their ability to understand videos when presented with autogenerated descriptions versus human-revised autogenerated descriptions. Our results demonstrate user enthusiasm about the developed system and its promise for providing customized access to videos. We discuss the limitations of the current work and provide recommendations for the future development of automated video description tools., Comment: arXiv admin note: This article has been withdrawn by arXiv administration due to an unresolvable authorship dispute

Published
2021

3. The Potential of a Visual Dialogue Agent In a Tandem Automated Audio Description System for Videos.

Author

Stangl, Abigale, Ihorn, Shasta, Siu, Yue-Ting, Bodi, Aditya, Castanon, Mar, Narins, Lothar D, and Yoon, Ilmi

Subjects
SOUND systems, LOW vision, VIDEO production & direction, ARTIFICIAL intelligence, MACHINE learning
Abstract

The relentless pace of video production exacerbates the digital accessibility gap that individuals who are blind or low vision (BLV) face on a daily basis, resulting in disproportionate exclusion from community opportunities and risk management. Whereas previous automated audio description (AD) systems provide single-tool approaches for delivering minimum viable description (MVD) or delivering on-demand visual question answering (VQA), we present a tandem AI-based AD tool that combines MVD and on-demand VQA. A user study with 26 BLV individuals explored how the tandem system may be used under the conditions of delivering MVD and/or on-demand VQA with AI-only or human-in-the-loop support. When each tool was used in isolation, AI-only conditions scored significantly lower in both user enjoyment and comprehension. When used in tandem, AI-only conditions matched outcomes delivered with human-in-the-loop, which suggests that AI-only AD tools may be most effective when both types of tools are used in tandem. A multimodal analysis of interactions with the tandem system revealed areas for system improvement in terms of the timing of AD delivery and accurate content delivery. We discuss how the use of both types of tools in a tandem system can mitigate some of the digital frictions that have plagued efforts in machine learning and automated tools for accessibility. [ABSTRACT FROM AUTHOR]

Published
2023
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