Transfer learning by borrowing examples for multiclass object detection
Name
834089202-MIT.pdf
Description
Full printable version
Size
4.11 MB
Format
Adobe PDF
Checksum (MD5)
e03ef09916673ede5f190117b79049d5
Author(s)
Lim, Joseph J. (Joseph Jaewhan)
Advisor(s)
Antonio Torralba.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
Despite the recent trend of increasingly large datasets for object detection, there still exist many classes with few training examples. To overcome this lack of training data for certain classes, we propose a novel way of augmenting the training data for each class by borrowing and transforming examples from other classes. Our model learns which training instances from other classes to borrow and how to transform the borrowed examples so that they become more similar to instances from the target class. Our experimental results demonstrate that our new object detector, with borrowed and transformed examples, improves upon the current state-of-the-art detector on the challenging SUN09 object detection dataset.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 31-33).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
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