Table of contents
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 of visual attribute learning and practical computer vision applications
This authoritative work is a must-read for all researchers interested in recognizing visual attributes and using them in real-world applications, and is accessible to the wider research community in visual and semantic understanding.
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.
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-319-50077-5
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Computer Science
- Print ISBN 978-3-319-50075-1
- Online ISBN 978-3-319-50077-5
- Series Print ISSN 2191-6586
- Series Online ISSN 2191-6594
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