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Contribution Details

Type Book Chapter
Scope Discipline-based scholarship
Title Deep Learning-Based Concept Detection in vitrivr
Organization Unit
  • Contribution from another University/Organization than University of Zurich
  • Luca Rossetto
  • Mahnaz Amiri Parian
  • Ralph Gasser
  • Ivan Giangreco
  • Silvan Heller
  • Heiko Schuldt
  • International Conference on Multimedia Modeling
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Booktitle MultiMedia Modeling
ISBN 978-3-030-05715-2
Place of Publication Heidelberg
Publisher Springer
Page Range 616 - 621
Date 2019-01-11
Abstract Text This paper presents the most recent additions to the vitrivr retrieval stack, which will be put to the test in the context of the 2019 Video Browser Showdown (VBS). The vitrivr stack has been extended by approaches for detecting, localizing, or describing concepts and actions in video scenes using various convolutional neural networks. Leveraging those additions, we have added support for searching the video collection based on semantic sketches. Furthermore, vitrivr offers new types of labels for text-based retrieval. In the same vein, we have also improved upon vitrivr’s pre-existing capabilities for extracting text from video through scene text recognition. Moreover, the user interface has received a major overhaul so as to make it more accessible to novice users, especially for query formulation and result exploration.
Digital Object Identifier 10.1007/978-3-030-05716-9_55
Other Identification Number merlin-id:18148
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