Learning and aggregating deep local descriptors for instance-level recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345514" target="_blank" >RIV/68407700:21230/20:00345514 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-58452-8_27" target="_blank" >https://doi.org/10.1007/978-3-030-58452-8_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58452-8_27" target="_blank" >10.1007/978-3-030-58452-8_27</a>
Alternative languages
Result language
angličtina
Original language name
Learning and aggregating deep local descriptors for instance-level recognition
Original language description
We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the activations of internal components of the network. We demonstrate why such an approach learns local descriptors that work well for image similarity estimation with classical efficient match kernel methods. The experimental validation studies the trade-off between performance and memory requirements of the state-of-the-art image search approach based on match kernels. Compared to existing local descriptors, the proposed ones perform better in two instance-level recognition tasks and keep memory requirements lower. We experimentally show that global descriptors are not effective enough at large scale and that local descriptors are essential. We achieve state-of-the-art performance, in some cases even with a backbone network as small as ResNet18.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LL1901" target="_blank" >LL1901: Generalized Image Retrieval and Relation Discovery</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision - ECCV 2020, Part I
ISBN
978-3-030-58451-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
18
Pages from-to
460-477
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Glasgow
Event date
Aug 23, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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