by Xiaoqing Guo.

This repository is for our CVPR 2022 paper "SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation"(知乎)

Pytorch 1.3 & Pytorch 1.7 are ok

Python 3.6

Clone the repository:

git clone https://github.com/Guo-Xiaoqing/SimT.git
cd SimT 
bash sh_warmup.sh ## Stage of warmup
bash sh_simt.sh ## Stage of training with SimT

The pseudo labels generated from the UDA black box of BAPA-Net [1] can be downloaded from Google Drive

The pseudo labels generated from the SFDA black box of SFDASeg [2] can be downloaded from Google Drive

[1] Yahao Liu, Jinhong Deng, Xinchen Gao, Wen Li, and Lixin Duan. Bapa-net: Boundary adaptation and prototype align- ment for cross-domain semantic segmentation. In ICCV, pages 8801–8811, 2021.

[2] Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh,Varun Jampani, and R Venkatesh Babu. Generalize then adapt: Source-free domain adaptive semantic segmentation. In ICCV, pages 7046–7056, 2021.

You should download the pretrained model, warmup UDA model, and warmup SFDA model from Google Drive, and then put them in the './snapshots' folder for initialization.

You could download the well trained UDA and SFDA models from Google Drive.

Log file can be found here

@inproceedings{guo2022simt,
  title={SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation},
  author={Guo, Xiaoqing and Liu, Jie and Liu, Tongliang and Yuan, Yixuan},
  booktitle= {CVPR},
  year={2022}
}

Please contact "[email protected]"