Interfaces for exploring human memorability and cognition
Name
1193021351-MIT.pdf
Size
11.35 MB
Format
Adobe PDF
Checksum (MD5)
8295794694e5fe915ed2c04898c932bf
Author(s)
Lee, Allen J.,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Aude Oliva.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Compared to images, videos are more dynamic, complex, and diverse. Analyzing and understanding video content is a tough cognitive challenge for computer vision. These challenges include analyzing the memorability of the video, determining semantic abstractions, and detecting when videos are fake. To better understand these challenges, we look to analyzing human behavior through the use of multiple interfaces. These interfaces are games designed to collect semantic and behavioral information from humans via Amazon Mechanical Turk. We process and analyze the data to incorporate into our models and provide a human baseline for comparison. We find that incorporating semantic attributes improves the capabilities of predicting the memorability of the video, and that our models are able to perform cognitive tasks related to semantic relational abstractions with near human accuracy.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 42-44).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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