  Mixup provides interpolated training samples and allows the model to obtain
smoother decision boundaries for better generalization. The idea can be
naturally applied to the domain adaptation task, where we can mix the source
and target samples to obtain domain-mixed samples for better adaptation.
However, the extension of the idea from classification to segmentation (i.e.,
structured output) is nontrivial. This paper systematically studies the impact
of mixup under the domain adaptaive semantic segmentation task and presents a
simple yet effective mixup strategy called Bidirectional Domain Mixup (BDM). In
specific, we achieve domain mixup in two-step: cut and paste. Given the warm-up
model trained from any adaptation techniques, we forward the source and target
samples and perform a simple threshold-based cut out of the unconfident regions
(cut). After then, we fill-in the dropped regions with the other domain region
patches (paste). In doing so, we jointly consider class distribution, spatial
structure, and pseudo label confidence. Based on our analysis, we found that
BDM leaves domain transferable regions by cutting, balances the dataset-level
class distribution while preserving natural scene context by pasting. We
coupled our proposal with various state-of-the-art adaptation models and
observe significant improvement consistently. We also provide extensive
ablation experiments to empirically verify our main components of the
framework. Visit our project page with the code at
https://sites.google.com/view/bidirectional-domain-mixup
