Self-training-based methods have achieved superior performance on unsupervised domain adaptive semantic segmentation task. However, these methods severely suffer from noisy pseudo label assignment. In this paper, we propose a simple yet effective dual pseudo label updating method that employs both online and offline mechanisms to dynamically update the two groups of pseudo labels. The online updating module employs a mean model to generate pseudo labels on-the-fly while the offline updating module capitalizes on the temporal consistency information to correct noisy labels. Furthermore, we present an online-offline dual regularization to further improve the noise-tolerant ability of the model. Combining the online-offline dual updating and online-offline dual regularization, we propose a novel mean-teacher based framework dubbed \textit{Online-Offline Dual Domain Adaption} (DUDA). Experiments show the proposed DUDA brings large performance gain and achieves state-of-the-art performance on two challenging benchmarks: GTA-to-Cityscapes and SYNTHIA-to-Cityscapes (
- Download Citysacpe, GTAV and Synthia datasets
ln -s your-cityscapes-dataset-path ./data/Cityscapes
cd dataset/Cityscapes
cp -r ./leftImg8bit/train/*/*leftImg8bit.png ./train/masks/
cp -r ./gtFine/train/*/*labelTrainIds.png ./train/masks/
cp -r ./leftImg8bit/valid/*/*leftImg8bit.png ./valid/masks/
cp -r ./gtFine/valid/*/*labelTrainIds.png ./valid/masks/-
The pseudo label and warmup model are upload at Baidu Cloud and the code is r26y.
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training:
python train.py --cfg ./configs/syn2cityscapes/IAST/self_train_res101_update.yaml- evaluation
python eval.py --cfg ./configs/syn2cityscapes/IAST/self_train_res101_update.yaml