ML Anthology

Learning a Discriminative Feature Network for Semantic Segmentation

Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang
CVPR 2018
doi:10.1109/CVPR.2018.00199 /cvpr/2018/yu2018cvpr-learning/

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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Cite

Text

Yu et al. "Learning a Discriminative Feature Network for Semantic Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00199

Markdown

[Yu et al. "Learning a Discriminative Feature Network for Semantic Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/yu2018cvpr-learning/) doi:10.1109/CVPR.2018.00199

BibTeX

@inproceedings{yu2018cvpr-learning,
  title     = {{Learning a Discriminative Feature Network for Semantic Segmentation}},
  author    = {Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00199},
  url       = {https://mlanthology.org/cvpr/2018/yu2018cvpr-learning/}
}