Predictive vision
Name
1006381602-MIT.pdf
Description
Full printable version
Size
25.68 MB
Format
Adobe PDF
Checksum (MD5)
8ccea5b6e4a0d85ee11928a6c3bde14b
Author(s)
Vondrick, Carl (Carl Martin)
Advisor(s)
Antonio Torralba.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Anticipating outcomes is the root of intelligence. This thesis investigates Predictive Vision with the goal to develop robust methods that anticipate the next events that may happen in images or videos. Importantly, we develop methods for eciently scaling learning algorithms to learn an extensive set of rules that enable richer visual understanding. While large annotated datasets fuel progress in object recognition, the knowledge required for event understanding is vast and potentially ambiguous. To tackle this challenge, we develop predictive vision algorithms that instead learn these rules directly from large amounts of raw, unlabeled data. Capitalizing on millions of natural videos, this work develops algorithms that learn to anticipate the visual future, forecast human actions, and recognize ambient sounds.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 95-106).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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