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Class-agnostic counting

Abstract:

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems.

We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-a...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 3.2MB)
Publisher copy:
10.1007/978-3-030-20893-6_42

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
Publisher:
Springer, Cham Publisher's website
Journal:
14th Asian Conference on Computer Vision (ACCV 2018) Journal website
Volume:
11363
Pages:
669-684
Series:
Lecture Notes in Computer Science
Host title:
ACCV 2018: Computer Vision – ACCV 2018
Publication date:
2019-05-29
Acceptance date:
2018-09-21
DOI:
ISSN:
0302-9743
Source identifiers:
944830
ISBN:
9783030208936
Keywords:
Pubs id:
pubs:944830
UUID:
uuid:0700b0af-1b14-4f4e-a7bc-8f38e93b4a51
Local pid:
pubs:944830
Deposit date:
2018-11-21

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