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 |