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%3A21730%2F18%3A00321876" target="_blank" >RIV/68407700:21730/18:00321876 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/7937898" target="_blank" >https://ieeexplore.ieee.org/document/7937898</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2017.2711011" target="_blank" >10.1109/TPAMI.2017.2711011</a>
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 four 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 create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
40
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1437-1451
UT code for WoS article
000431524700012
EID of the result in the Scopus database
2-s2.0-85021711204