  Semantic segmentation of nighttime images plays an equally important role as
that of daytime images in autonomous driving, but the former is much more
challenging due to poor illuminations and arduous human annotations. In this
paper, we propose a novel domain adaptation network (DANNet) for nighttime
semantic segmentation without using labeled nighttime image data. It employs an
adversarial training with a labeled daytime dataset and an unlabeled dataset
that contains coarsely aligned day-night image pairs. Specifically, for the
unlabeled day-night image pairs, we use the pixel-level predictions of static
object categories on a daytime image as a pseudo supervision to segment its
counterpart nighttime image. We further design a re-weighting strategy to
handle the inaccuracy caused by misalignment between day-night image pairs and
wrong predictions of daytime images, as well as boost the prediction accuracy
of small objects. The proposed DANNet is the first one stage adaptation
framework for nighttime semantic segmentation, which does not train additional
day-night image transfer models as a separate pre-processing stage. Extensive
experiments on Dark Zurich and Nighttime Driving datasets show that our method
achieves state-of-the-art performance for nighttime semantic segmentation.
