Pulling Target to Source: A New Perspective on Domain Adaptive Semantic
  Segmentation
Haochen Wang Yujun Shen Jingjing Fei Wei Li Liwei Wu Yuxi Wang Zhaoxiang Zhang
Domain adaptive semantic segmentation aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. However, existing methods
primarily focus on directly learning qualified target features, making it
challenging to guarantee their discrimination in the absence of target labels.
This work provides a new perspective. We observe that the features learned with
source data manage to keep categorically discriminative during training,
thereby enabling us to implicitly learn adequate target representations by
simply \textbf{pulling target features close to source features for each
category}. To this end, we propose T2S-DA, which we interpret as a form of
pulling Target to Source for Domain Adaptation, encouraging the model in
learning similar cross-domain features. Also, considering the pixel categories
are heavily imbalanced for segmentation datasets, we come up with a dynamic
re-weighting strategy to help the model concentrate on those underperforming
classes. Extensive experiments confirm that T2S-DA learns a more discriminative
and generalizable representation, significantly surpassing the
state-of-the-art. We further show that our method is quite qualified for the
domain generalization task, verifying its domain-invariant property.