  This paper focuses on the unsupervised domain adaptation of transferring the
knowledge from the source domain to the target domain in the context of
semantic segmentation. Existing approaches usually regard the pseudo label as
the ground truth to fully exploit the unlabeled target-domain data. Yet the
pseudo labels of the target-domain data are usually predicted by the model
trained on the source domain. Thus, the generated labels inevitably contain the
incorrect prediction due to the discrepancy between the training domain and the
test domain, which could be transferred to the final adapted model and largely
compromises the training process. To overcome the problem, this paper proposes
to explicitly estimate the prediction uncertainty during training to rectify
the pseudo label learning for unsupervised semantic segmentation adaptation.
Given the input image, the model outputs the semantic segmentation prediction
as well as the uncertainty of the prediction. Specifically, we model the
uncertainty via the prediction variance and involve the uncertainty into the
optimization objective. To verify the effectiveness of the proposed method, we
evaluate the proposed method on two prevalent synthetic-to-real semantic
segmentation benchmarks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, as
well as one cross-city benchmark, i.e., Cityscapes -> Oxford RobotCar. We
demonstrate through extensive experiments that the proposed approach (1)
dynamically sets different confidence thresholds according to the prediction
variance, (2) rectifies the learning from noisy pseudo labels, and (3) achieves
significant improvements over the conventional pseudo label learning and yields
competitive performance on all three benchmarks.
