Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication
This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images – one of the main ingredients of zero-shot learning – by formulating it as a metric learning prob
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Bastian Leibe Jiri Matas Nicu Sebe Max Welling (Eds.)
Computer Vision – ECCV 2016 14th European Conference Amsterdam, The Netherlands, October 11–14, 2016 Proceedings, Part V
123
Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zurich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/7412
Bastian Leibe Jiri Matas Nicu Sebe Max Welling (Eds.) •
•
Computer Vision – ECCV 2016 14th European Conference Amsterdam, The Netherlands, October 11–14, 2016 Proceedings, Part V
123
Editors Bastian Leibe RWTH Aachen Aachen Germany
Nicu Sebe University of Trento Povo - Trento Italy
Jiri Matas Czech Technical University Prague 2 Czech Republic
Max Welling University of Amsterdam Amsterdam The Netherlands
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-319-46453-4 ISBN 978-3-319-46454-1 (eBook) DOI 10.1007/978-3-319-46454-1 Library of Congress Control Number: 2016951693 LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics © Springer International Publishing AG 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springe
