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Cited by 1,516

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Deep Layer Aggregation

Open access
Yu, Fisher, Wang, Dequan, Shelhamer, Evan, Darrell, Trevor·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00255
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Abstract

Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.

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Citations

Cites

Research works printed but not linked: 31

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Metadata

Authors
Yu, F., Wang, D., Shelhamer, E., & Darrell, T.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
2403-2412
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2018.00255
OpenAlex
W2963323244
Cited by
1,516
References
20
FWCI
44.23
Type
conference paper
Language
en
Open access
green

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