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Dense decoder shortcut connections for single-pass semantic segmentation

Abstract:

We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation. Our approach is based on a cascaded architecture with feature-level long-range skip connections. The encoder incorporates the structure of ResNeXt's residual building blocks and adopts the strategy of repeating a building block that aggregates a set of transformations with the same topology. The decoder features a novel architecture, consisting of blocks, that (i) capture context...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/cvpr.2018.00690

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Institution:
University of Oxford
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Anne's College
Role:
Author
Publisher:
Institute for Electrical and Electronics Engineers Publisher's website
Pages:
6596-6605
Host title:
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publication date:
2018-12-17
Event title:
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Event location:
Salt Lake City, UT, USA
Event website:
https://cvpr2018.thecvf.com/
Event start date:
2018-06-18T00:00:00Z
Event end date:
2018-06-23T00:00:00Z
DOI:
EISBN:
9781538664209
EISSN:
2575-7075
ISSN:
1063-6919
Source identifiers:
953152
ISBN:
9781538664216
Language:
English
Deposit date:
2018-12-18

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