Pytorch implementation for:
"Domain Adaptive Semantic Segmentation without Source Data", ACM MM2021. Paper link: http://arxiv.org/abs/2110.06484
GTA5 -> Cityscapes:
| Methods | Source-only | LD | LDBE |
|---|---|---|---|
| mIoU | 35.7 | 45.5 | 49.2 |
SYNTHIA -> Cityscapes:
| Methods | Source-only | LD | LDBE |
|---|---|---|---|
| mIoU (16-classes) | 32.5 | 42.6 | 43.5 |
| mIoU (13-classes) | 37.6 | 50.1 | 51.1 |
Download GTA5.
Download SYNTHIA. Please use SYNTHIA-RAND-CITYSCAPES
Download Cityscapes.
Make sure the data path is consistent with the path in config file.
Stage 0: Training on the source domain data.
Run "run_so.py". The trained model is available at google drive.
Stage 1: Label denoising (both positive learning and negative learning).
Set method:"ld" in config/ldbe_config.yml. Then, run "run.py". The trained model is available at google drive.
Stage 2: Boundary enhancement
Set method:"be" in config/ldbe_config.yml. Then, run "run.py". The trained model is available at google drive.
If you find our repository is helpful, please consider citing our paper
@inproceedings{you2021domain,
title={Domain Adaptive Semantic Segmentation without Source Data},
author={You, Fuming and Li, Jingjing and Zhu, Lei and Chen, Zhi and Huang, Zi},
booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
pages={3293--3302},
year={2021}
}