This repository contains official implementation of Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation in TCSVT 2022, by Cong Cao, Tianwei Lin, Dongliang He, Fu Li, Huanjing Yue, Jingyu Yang, and Errui Ding. [arxiv] [journal]
- Python >= 3.5
- Pytorch >= 1.1
- NVIDIA Tesla V100
You can download pretrained weights from here (ADS-DGW_Dataset_SemiRatio_iterXXXXX.pth), then run:
bash run_scripts/test_VOC2012.sh
Train baseline:
bash run_scripts/train_baseline_VOC2012.sh
Train Mean-Teacher with DGW augmentation:
bash run_scripts/train_MT_DGW_VOC2012.sh
Train ADS with DGW augmentation:
bash run_scripts/train_ADS_DGW_VOC2012.sh
If you find our paper or code helpful in your research or work, please cite our paper:
@article{cao2022adversarial,
title={Adversarial dual-student with differentiable spatial warping for semi-supervised semantic segmentation},
author={Cao, Cong and Lin, Tianwei and He, Dongliang and Li, Fu and Yue, Huanjing and Yang, Jingyu and Ding, Errui},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={33},
number={2},
pages={793--803},
year={2022},
publisher={IEEE}
}