This is the official implementation of our ECCV 2022 paper "TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation"

Prerequisite

• CUDA/CUDNN
• Python3
• Packages found in requirements.txt

Dataset Preparation

GTAV, SYNTHIA, Cityscapes, Synscapes

Open Taxonomy Setting

Before Relabeling: python3 trainTACS_open_adduncertaincontrastive.py --config ./configs/configUDA_euler.json --name TACS_open_uncertaincontrast_beforerelabel --numsamples 30

After Relabeling: python3 trainTACS_open_adduncertaincontrastive_relabel.py --config ./configs/configUDA_euler_resume.json --name TACS_open_uncertaincontrast_afterrelabel --numsamples 30 --resume <Path to CheckPoint Before Relabeling>

Coarse-to-Fine Taxonomy Setting

Before Relabeling: python3 trainTACS_coarsetofine_adduncertaincontrastive.py --config ./configs/configUDA_euler.json --name TACS_coarsetofine_uncertaincontrast_beforerelabel --numsamples 30

After Relabeling: python3 trainTACS_coarsetofine_adduncertaincontrastive_relabel.py --config ./configs/configUDA_euler_resume.json --name TACS_coarsetofine_uncertaincontrast_afterrelabel --numsamples 30 --resume <Path to CheckPoint Before Relabeling>

Implicitly Overlapping Taxonomy Setting

Before Relabeling: python3 trainTACS_implicitoverlapping_adduncertaincontrastive.py --config ./configs/configUDA_euler.json --name TACS_implicitoverlapping_uncertaincontrast_beforerelabel --numsamples 15

After Relabeling: python3 trainTACS_implicitoverlapping_adduncertaincontrastive_relabel.py --config ./configs/configUDA_euler_resume.json --name TACS_implicitoverlapping_uncertaincontrast_afterrelabel --numsamples 15 --resume <Path to CheckPoint Before Relabeling>

Model Testing

If you would like to drectly use our trained model, please access our checkpoints folder.

Open, Coarse-to-Fine: python3 evaluateTACS.py --model-path <Path to Checkpoint>

Implicitly-Overlapping: python3 evaluateTACS_16classes.py --model-path <Path to Checkpoint>

Acknowledgements

The implementation is based on the following open-source projects. We thank their authors for making the source code publicly available.

• DACS
• ContrastiveSeg

Citation

If this helps you, please cite our TACS work:

@inproceedings{gong2022tacs,
  title={TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation},
  author={Rui Gong, Martin Danelljan, Dengxin Dai, Danda Pani Paudel, Ajad Chhatkuli, Fisher Yu, Luc Van Gool},
  booktitle={ECCV},
  year={2022}
}