CRII: RI: Learning to Predict the Temporal Interestingness of Videos

Principal Investigator: Eakta Jain

Sponsor: National Science Foundation

Start Date: April 1, 2016

End Date: March 31, 2020

Amount: $182,634


This project examines the role that implicit feedback from viewers can play in learning a temporal interestingness function for videos. The key insight is that by leveraging pupil dilation as ground truth, supervised machine learning approaches can be applied to this problem. The ubiquitous presence of cameras in every phone, and the ability to share content with the entire world have made videos a powerful tool in the hands of everyday people. This project is to address the challenge that viewers have ever-shortening attention spans, and constructing a succinct message is hard. The project provides research and training opportunities for both undergraduate and graduate students in computer vision, machine learning, and human-centered computing.

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