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Cited by 870

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

Learning a Discriminative Feature Network for Semantic Segmentation

Open access
Yu, Changqian, Wang, Jingbo, Peng, Chao, Gao, Changxin, Yu, Gang, Sang, Nong·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00199
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Abstract

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

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Research works printed but not linked: 24

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Metadata

Authors
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
1857-1866
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2018.00199
OpenAlex
W2799166040
Cited by
870
References
20
FWCI
37.08
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Learning a Discriminative Feature Network for Semantic Segmentation. https://app.cochat.ai/papers/10.1109/cvpr.2018.00199
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Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018). Learning a Discriminative Feature Network for Semantic Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00199

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