  This paper challenges the cross-domain semantic segmentation task, aiming to
improve the segmentation accuracy on the unlabeled target domain without
incurring additional annotation. Using the pseudo-label-based unsupervised
domain adaptation (UDA) pipeline, we propose a novel and effective Multiple
Fusion Adaptation (MFA) method. MFA basically considers three parallel
information fusion strategies, i.e., the cross-model fusion, temporal fusion
and a novel online-offline pseudo label fusion. Specifically, the
online-offline pseudo label fusion encourages the adaptive training to pay
additional attention to difficult regions that are easily ignored by offline
pseudo labels, therefore retaining more informative details. While the other
two fusion strategies may look standard, MFA pays significant efforts to raise
the efficiency and effectiveness for integration, and succeeds in injecting all
the three strategies into a unified framework. Experiments on two widely used
benchmarks, i.e., GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes, show that our
method significantly improves the semantic segmentation adaptation, and sets up
new state of the art (58.2% and 62.5% mIoU, respectively). The code will be
available at https://github.com/KaiiZhang/MFA.
