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Gather-excite: Exploiting feature context in convolutional neural networks

Abstract:

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled...

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Publication status:
Published
Peer review status:
Peer Reviewed
Version:
Accepted Manuscript

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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:
New College
Publisher:
Neural Information Processing Systems (NIPS) Foundation Publisher's website
Publication date:
2018-12-31
Acceptance date:
2018-09-05
Pubs id:
pubs:948559
URN:
uri:29272d1a-72d7-4689-b388-f960dff864da
UUID:
uuid:29272d1a-72d7-4689-b388-f960dff864da
Local pid:
pubs:948559

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