Domain Adaptation for Visual Understanding - Rilegato

Rilegato

ISBN 10: 3030306704 ISBN 13: 9783030306700

Casa editrice: Springer-Nature New York Inc, 2020

Riassunto

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

Su questo libro

Informazioni sull?autore

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

Dalla quarta di copertina

<p>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.</p><p><b>Topics and features:</b></p><p></p><ul><li>Reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach</li><li>Introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning</li><li>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</li><li>Describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance</li><li>Presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation</li><li>Examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods</li></ul><p></p><p>This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.</p><p><b>Dr. Richa Singh</b> is a Professor at Indraprastha Institute of Information Technology, Delhi, India. <b>Dr. Mayank Vatsa</b> is a Professor at the same institution. <b>Dr. Vishal M. Patel</b> is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. <b>Dr. Nalini Ratha</b> is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.</p>

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030306704
• ISBN 13: 9783030306700
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 156
• Redattore: Singh Richa, Vatsa Mayank, Patel Vishal M., Ratha Nalini
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania