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Domain Adaptation for Visual Understanding

Editors: Singh, R., Vatsa, M., Patel, V.M., Ratha, N. (Eds.)

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  • Presents the latest research on domain adaptation for visual understanding
  • Provides perspectives from an international selection of authorities in the field
  • Reviews a variety of applications and techniques
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eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-030-30671-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: January 8, 2020
  • ISBN 978-3-030-30670-0
  • Free shipping for individuals worldwide
About this book

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition.

Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods.

This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

About the authors

Dr. Richa Singh is a Professor at Indraprastha Institute of Information Technology, Delhi, India. Dr. Mayank Vatsa is a Professor at the same institution. Dr. Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. Dr. Nalini Ratha is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.

Table of contents (9 chapters)

Table of contents (9 chapters)

Buy this book

eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-030-30671-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: January 8, 2020
  • ISBN 978-3-030-30670-0
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Domain Adaptation for Visual Understanding
Editors
  • Richa Singh
  • Mayank Vatsa
  • Vishal M. Patel
  • Nalini Ratha
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-30671-7
DOI
10.1007/978-3-030-30671-7
Hardcover ISBN
978-3-030-30670-0
Edition Number
1
Number of Pages
X, 144
Number of Illustrations
6 b/w illustrations, 56 illustrations in colour
Topics