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Bottom-up top-down cues for weakly-supervised semantic segmentation

Abstract:

We consider the task of learning a classifier for semantic segmentation using weak supervision in the form of image labels specifying objects present in the image. Our method uses deep convolutional neural networks (cnns) and adopts an Expectation-Maximization (EM) based approach. We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step). We show that saliency and attention maps, bottom-up and top-down ...

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Publication status:
Published
Peer review status:
Peer reviewed

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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
ORCID:
0000-0001-5550-8758
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Publisher:
Springer, Cham Publisher's website
Volume:
10746
Pages:
263-277
Series:
Lecture Notes in Computer Science
Publication date:
2018-03-22
Acceptance date:
2017-09-25
DOI:
ISSN:
0302-9743
Pubs id:
pubs:842223
URN:
uri:3cc3f562-d6a3-4bf3-9e10-ee8d8d811eff
UUID:
uuid:3cc3f562-d6a3-4bf3-9e10-ee8d8d811eff
Local pid:
pubs:842223
ISBN:
9783319781983

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