NetVLAD: CNN architecture for weakly supervised place recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00304272" target="_blank" >RIV/68407700:21230/16:00304272 - isvavai.cz</a>
Result on the web
<a href="http://80.ieeexplore.ieee.org.dialog.cvut.cz/document/7780941/" target="_blank" >http://80.ieeexplore.ieee.org.dialog.cvut.cz/document/7780941/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
NetVLAD: CNN architecture for weakly supervised place recognition
Original language description
We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end-to-end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current stateofthe-art compact image representations on standard image retrieval benchmarks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/7E13015" target="_blank" >7E13015: Planetary Robotics Data Exploitation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CVPR 2016: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition
ISBN
978-1-4673-8851-1
ISSN
1063-6919
e-ISSN
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Number of pages
11
Pages from-to
5297-5307
Publisher name
IEEE Computer Society Press
Place of publication
Los Alamitos
Event location
Las Vegas
Event date
Jun 26, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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