  In unsupervised domain adaptive (UDA) semantic segmentation, the distillation
based methods are currently dominant in performance. However, the distillation
technique requires complicate multi-stage process and many training tricks.
  In this paper, we propose a simple yet effective method that can achieve
competitive performance to the advanced distillation methods. Our core idea is
to fully explore the target-domain information from the views of boundaries and
features.
  First, we propose a novel mix-up strategy to generate high-quality
target-domain boundaries with ground-truth labels. Different from the
source-domain boundaries in previous works, we select the high-confidence
target-domain areas and then paste them to the source-domain images. Such a
strategy can generate the object boundaries in target domain (edge of
target-domain object areas) with the correct labels. Consequently, the boundary
information of target domain can be effectively captured by learning on the
mixed-up samples.
  Second, we design a multi-level contrastive loss to improve the
representation of target-domain data, including pixel-level and prototype-level
contrastive learning.
  By combining two proposed methods, more discriminative features can be
extracted and hard object boundaries can be better addressed for the target
domain.
  The experimental results on two commonly adopted benchmarks (\textit{i.e.},
GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes) show that
our method achieves competitive performance to complicated distillation
methods. Notably, for the SYNTHIA$\rightarrow$ Cityscapes scenario, our method
  achieves the state-of-the-art performance with $57.8\%$ mIoU and $64.6\%$
mIoU on 16 classes and 13 classes. Code is available at
https://github.com/ljjcoder/EHTDI.
