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Many-shot from low-shot: learning to annotate using mixed supervision for object detection

Abstract:

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals often result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1007/978-3-030-58598-3_3

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Institution:
University of Oxford
Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Role:
Author
Publisher:
Springer Nature Publisher's website
Host title:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series:
Lecture Notes in Computer Science
Journal:
Proceedings of the 16th European Conference on Computer Vision (ECCV 2020) Journal website
Volume:
12353
Pages:
35-50
Publication date:
2020-11-07
Acceptance date:
2020-07-02
Event title:
European Conference on Computer Vision (ECCV 2020)
Event location:
Online
Event website:
https://eccv2020.eu/
Event start date:
2021-08-23
Event end date:
2021-08-28
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
EISBN:
978-3-030-58598-3
ISBN:
9783030585976
Language:
English
Keywords:
Pubs id:
1151096
Local pid:
pubs:1151096
Deposit date:
2021-01-05

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