Domain Adaptation for Visual Understanding

Language: English

Published by Springer, Springer Jan 2020, 2020

3030306704 / 9783030306700

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This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.

Seller Inventory # 9783030306700

Title
Domain Adaptation for Visual Understanding
Author
Richa Singh
Publisher
Springer, Springer Jan 2020
Publication year
2020
Condition
Neu
Binding
Buch
Language
English
ISBN 10
3030306704
ISBN 13
9783030306700
Item weight
407 grams
Dimensions
241x160x15 mm

buchversandmimpf2000

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