Lokoč, Jakub ORCID: 0000-0002-3558-4144, Schoeffmann, Klaus ORCID: 0000-0002-9218-1704, Bailer, Werner ORCID: 0000-0003-2442-4900, Rossetto, Luca ORCID: 0000-0002-5389-9465 and Gurrin, Cathal ORCID: 0000-0003-2903-3968 (2019) Interactive video retrieval in the age of deep learning. In: ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval, 10-13 June2019, Ottawa, Canada. ISBN 978-3-030-28577-7
Abstract
We present a tutorial focusing on video retrieval tasks, where stateof-the-art deep learning approaches still benefit from interactive
decisions of users. The tutorial covers general introduction to the
interactive video retrieval research area, state-of-the-art video retrieval systems, evaluation campaigns and recently observed results.
Moreover, a significant part of the tutorial is dedicated to a practical
exercise with three selected state-of-the-art systems in the form
of an interactive video retrieval competition. Participants of this
tutorial will gain a practical experience and also a general insight of
the interactive video retrieval topic, which is a good start to focus
their research on unsolved challenges in this area.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | evaluation campaigns; deep learning; interactive video retrieval |
Subjects: | Computer Science > Interactive computer systems Computer Science > Digital video Computer Science > Lifelog |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Initiatives and Centres > INSIGHT Centre for Data Analytics |
Published in: | ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval. . Association for Computing Machinery (ACM). ISBN 978-3-030-28577-7 |
Publisher: | Association for Computing Machinery (ACM) |
Official URL: | https://doi.org/10.1145/3323873.3326588 |
Copyright Information: | 2019 The Authors |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Czech Science Foundation (GAČR) project Nr. 19-22071Y, Science Foundation Ireland (SFI) under grant Nr. SFI/12/RC/2289. |
ID Code: | 24677 |
Deposited On: | 23 Jun 2020 10:58 by Cathal Gurrin . Last Modified 15 Dec 2021 15:52 |
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