RANet: Region Attention Network for Semantic Segmentation

Paper Links: RANet: Region Attention Network for Semantic Segmentation.

By Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin.

Citing RANet

If you find RANet useful in your research, please consider citing:

@article{shen2020ranet,
  title={RANet: Region Attention Network for Semantic Segmentation},
  author={Shen, Dingguo and Ji, Yuanfeng and Li, Ping and Wang, Yi and Lin, Di},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Requirements

Python 3.7
PyTorch 1.1
GCC 4.8.5
CUDA 9.0

Installing and Running:

Following the pytorch-segmentation-toolbox.

Result

The probability of results on the three datasets can be download.

Cityscapes, code:votw

Pascal Context, code:6p0z

COCO-Stuff, code:6nc2

Thanks to the Third Party Libs

pytorch-segmentation-toolbox