Domain Adaptation Visual Understanding (22 results)
Domain Adaptation for Visual Understanding
Singh, Richa (EDT); Vatsa, Mayank (EDT); Patel, Vishal M. (EDT); Ratha, Nalini (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Domain Adaptation for Visual Understanding
Singh, Richa (EDT); Vatsa, Mayank (EDT); Patel, Vishal M. (EDT); Ratha, Nalini (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Domain Adaptation for Visual Understanding
Singh, Richa (EDT); Vatsa, Mayank (EDT); Patel, Vishal M. (EDT); Ratha, Nalini (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Domain Adaptation for Visual Understanding
Singh, Richa (EDT); Vatsa, Mayank (EDT); Patel, Vishal M. (EDT); Ratha, Nalini (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Domain Adaptation for Visual Understanding
Singh, Richa (Editor)/ Vatsa, Mayank (Editor)/ Patel, Vishal M. (Editor)/ Ratha, Nalini (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 154 pages. 9.25x6.10x0.37 inches. In Stock.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 156 pages. 9.25x6.10x0.59 inches. In Stock.
More images- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Domain Adaptation for Visual Understanding | Richa Singh (u. a.) | Taschenbuch | x | Englisch | 2021 | Springer | EAN 9783030306731 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 div…erse 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.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 se…lection 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.
- Softcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
- Hardcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer International Publishing Aug 2021, 2021
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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 tex…t 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. 156 pp. Englisch.
Language: English
Published by Springer International Publishing Jan 2020, 2020
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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US$ 128.42
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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 prese…nts 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. 156 pp. Englisch.
- Softcover
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Seller: moluna, Greven, Germanymoluna
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the latest research on domain adaptation for visual understandingProvides perspectives from an international selection of authorities in the fieldReviews a variety of applications and… techniquesDr. Richa Singh&nbs.
- Hardcover
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Seller: moluna, Greven, Germanymoluna
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the latest research on domain adaptation for visual understandingProvides perspectives from an international selection of authorities in the fieldReviews a variety of applications and techniquesDr.…Richa Singh&nbs.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Condition: New. Print on Demand.
- Softcover
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: New. PRINT ON DEMAND.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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US$ 128.42
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Taschenbuch. Condition: Neu. 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 pr…esents 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.
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
US$ 128.42
US$ 69.92 shippingShips from Germany to U.S.A.Quantity: 1 available
Buch. Condition: Neu. 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.
