Editors:
- The first book to introduce the topic of visual attributes, and cover emerging concepts such as zero-shot learning
- Covers theoretical aspects of visual attribute learning, as well as practical computer vision applications
- Includes contributions from world-renowned scientists in machine learning and computer vision, and at the intersection with computational linguistics and human-machine interaction
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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Table of contents (13 chapters)
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Front Matter
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Relative Attributes and Their Application to Image Search
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Front Matter
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Describing People Based on Attributes
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Front Matter
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Defining a Vocabulary of Attributes
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Front Matter
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Attributes and Language
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Front Matter
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Back Matter
About this book
This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects with other disciplines, such as computational linguistics and human-machine interaction. Topics and features: presents attribute-based methods for zero-shot classification, learning using privileged information, and methods for multi-task attribute learning; describes the concept of relative attributes, and examines the effectiveness of modeling relative attributes in image search applications; reviews state-of-the-art methods for estimation of human attributes, and describes their use in a range of different applications; discusses attempts to build a vocabulary of visual attributes; explores the connections between visual attributes and natural language; provides contributions from an international selection of world-renowned scientists, covering both theoretical aspects and practical applications.
Editors and Affiliations
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IBM T.J. Watson Research Center, Yorktown Heights, USA
Rogerio Schmidt Feris
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IST Austria Computer Vision and Machine Learning, Klosterneuburg, Austria
Christoph Lampert
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Virginia Tech Electrical and Computer Engineering, Blacksburg, USA
Devi Parikh
About the editors
Dr. Rogerio Schmidt Feris is a manager at IBM T.J. Watson Research Center, New York, USA, where he leads research in computer vision and machine learning.
Dr. Christoph H. Lampert is a professor at the Institute of Science and Technology Austria, where he serves as the Principal Investigator of the Computer Vision and Machine Learning Group.
Dr. Devi Parikh is an assistant professor in the School of Interactive Computing at Georgia Tech, USA, where she leads the Computer Vision Lab.
Bibliographic Information
Book Title: Visual Attributes
Editors: Rogerio Schmidt Feris, Christoph Lampert, Devi Parikh
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-319-50077-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-50075-1Published: 30 March 2017
Softcover ISBN: 978-3-319-84311-7Published: 20 July 2018
eBook ISBN: 978-3-319-50077-5Published: 21 March 2017
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: VIII, 364
Number of Illustrations: 5 b/w illustrations, 137 illustrations in colour
Topics: Image Processing and Computer Vision, Artificial Intelligence, User Interfaces and Human Computer Interaction