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Expectation-Maximization Attention Networks for Semantic Segmentation

Introduction

[ALGORITHM]

@inproceedings{li2019expectation,
  title={Expectation-maximization attention networks for semantic segmentation},
  author={Li, Xia and Zhong, Zhisheng and Wu, Jianlong and Yang, Yibo and Lin, Zhouchen and Liu, Hong},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={9167--9176},
  year={2019}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
EMANet R-50-D8 512x1024 80000 5.4 4.58 77.59 79.44 model | log
EMANet R-101-D8 512x1024 80000 6.2 2.87 79.10 81.21 model | log
EMANet R-50-D8 769x769 80000 8.9 1.97 79.33 80.49 model | log
EMANet R-101-D8 769x769 80000 10.1 1.22 79.62 81.00 model | log