Domain Adaptation for Visual Understanding

Richa Singh

ISBN 10: 3030306704 / ISBN 13: 9783030306700

Editore: Springer International Publishing, Springer Nature Switzerland Jan 2020, 2020

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Neuware -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; presentsa 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.

Codice articolo 9783030306700

Dati bibliografici

• Titolo: Domain Adaptation for Visual Understanding
• Editore: Springer International Publishing, Springer Nature Switzerland Jan 2020
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030306704
• ISBN 13: 9783030306700
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 15 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 15 millimetri di profondità
• Peso: 407 grammi

Descrizione articolo

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>

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>