LDBE
Pytorch implementation for two papers (the paper will be released soon):
"Domain Adaptive Semantic Segmentation without Source Data", ACM MM2021.
"Challenging Source-free Domain Adaptive Semantic Segmentation", submitted to TPAMI.
Method
Result
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 |
Data
Download GTA5.
Download SYNTHIA. Please use SYNTHIA-RAND-CITYSCAPES
Download Cityscapes.
Make sure the data path is consistent with the path in config file.
Training (TODO)
Stage 0: Training on the source domain data.
Run "run_so.py". The trained model is available at ...
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 ...
Stage 2: Boundary enhancement
Set method:"be" in config/ldbe_config.yml. Then, run "run.py". The trained model is available at ...
Acknowledgement
https://github.com/Solacex/CCM
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