  Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt
a segmentation model trained on the labeled source domain to the unlabeled
target domain. Existing methods try to learn domain invariant features while
suffering from large domain gaps that make it difficult to correctly align
discrepant features, especially in the initial training phase. To address this
issue, we propose a novel Dual Soft-Paste (DSP) method in this paper.
Specifically, DSP selects some classes from a source domain image using a
long-tail class first sampling strategy and softly pastes the corresponding
image patch on both the source and target training images with a fusion weight.
Technically, we adopt the mean teacher framework for domain adaptation, where
the pasted source and target images go through the student network while the
original target image goes through the teacher network. Output-level alignment
is carried out by aligning the probability maps of the target fused image from
both networks using a weighted cross-entropy loss. In addition, feature-level
alignment is carried out by aligning the feature maps of the source and target
images from student network using a weighted maximum mean discrepancy loss. DSP
facilitates the model learning domain-invariant features from the intermediate
domains, leading to faster convergence and better performance. Experiments on
two challenging benchmarks demonstrate the superiority of DSP over
state-of-the-art methods. Code is available at
\url{https://github.com/GaoLii/DSP}.
