  In this paper, we present a direct adaptation strategy (ADAS), which aims to
directly adapt a single model to multiple target domains in a semantic
segmentation task without pretrained domain-specific models. To do so, we
design a multi-target domain transfer network (MTDT-Net) that aligns visual
attributes across domains by transferring the domain distinctive features
through a new target adaptive denormalization (TAD) module. Moreover, we
propose a bi-directional adaptive region selection (BARS) that reduces the
attribute ambiguity among the class labels by adaptively selecting the regions
with consistent feature statistics. We show that our single MTDT-Net can
synthesize visually pleasing domain transferred images with complex driving
datasets, and BARS effectively filters out the unnecessary region of training
images for each target domain. With the collaboration of MTDT-Net and BARS, our
ADAS achieves state-of-the-art performance for multi-target domain adaptation
(MTDA). To the best of our knowledge, our method is the first MTDA method that
directly adapts to multiple domains in semantic segmentation.
