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Comparator networks

Abstract:

The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1007/978-3-030-01252-6_48

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Brasenose College
ORCID:
0000-0002-8945-8573
Office of the Director of National Intelligence More from this funder
Publisher:
Springer Publisher's website
Publication date:
2018-10-06
Acceptance date:
2018-07-03
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Pubs id:
pubs:935328
URN:
uri:6f568144-2b0e-41ff-a4d9-0367aeff1926
UUID:
uuid:6f568144-2b0e-41ff-a4d9-0367aeff1926
Local pid:
pubs:935328
ISBN:
9783030012519

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