Open Compound Domain Adaptation with Object Style Compensation for
  Semantic Segmentation
Tingliang Feng Hao Shi Xueyang Liu Wei Feng Liang Wan Yanlin Zhou Di Lin
Many methods of semantic image segmentation have borrowed the success of open
compound domain adaptation. They minimize the style gap between the images of
source and target domains, more easily predicting the accurate pseudo
annotations for target domain's images that train segmentation network. The
existing methods globally adapt the scene style of the images, whereas the
object styles of different categories or instances are adapted improperly. This
paper proposes the Object Style Compensation, where we construct the
Object-Level Discrepancy Memory with multiple sets of discrepancy features. The
discrepancy features in a set capture the style changes of the same category's
object instances adapted from target to source domains. We learn the
discrepancy features from the images of source and target domains, storing the
discrepancy features in memory. With this memory, we select appropriate
discrepancy features for compensating the style information of the object
instances of various categories, adapting the object styles to a unified style
of source domain. Our method enables a more accurate computation of the pseudo
annotations for target domain's images, thus yielding state-of-the-art results
on different datasets.