Visual Attributes
Herausgeber: Feris, Rogerio Schmidt, Lampert, Christoph, Parikh, Devi (Eds.)
Vorschau- 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
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- Über dieses Buch
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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.
- Über die Autor*innen
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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.
- Inhaltsverzeichnis (13 Kapitel)
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Introduction to Visual Attributes
Seiten 1-7
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An Embarrassingly Simple Approach to Zero-Shot Learning
Seiten 11-30
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In the Era of Deep Convolutional Features: Are Attributes Still Useful Privileged Data?
Seiten 31-48
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Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing
Seiten 49-85
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Attributes for Image Retrieval
Seiten 89-117
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Inhaltsverzeichnis (13 Kapitel)
- Download Vorwort 1 PDF (38.9 KB)
- Download Probeseiten 2 PDF (350.6 KB)
- Download Inhaltsverzeichnis PDF (59.2 KB)
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Bibliografische Information
- Bibliographic Information
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- Buchtitel
- Visual Attributes
- Herausgeber
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- Rogerio Schmidt Feris
- Christoph Lampert
- Devi Parikh
- Titel der Buchreihe
- Advances in Computer Vision and Pattern Recognition
- Copyright
- 2017
- Verlag
- Springer International Publishing
- Copyright Inhaber
- Springer International Publishing AG
- eBook ISBN
- 978-3-319-50077-5
- DOI
- 10.1007/978-3-319-50077-5
- Hardcover ISBN
- 978-3-319-50075-1
- Softcover ISBN
- 978-3-319-84311-7
- Buchreihen ISSN
- 2191-6586
- Auflage
- 1
- Seitenzahl
- VIII, 364
- Anzahl der Bilder
- 5 schwarz-weiß Abbildungen, 137 Abbildungen in Farbe
- Themen
