  Most progress in semantic segmentation reports on daytime images taken under
favorable illumination conditions. We instead address the problem of semantic
segmentation of nighttime images and improve the state-of-the-art, by adapting
daytime models to nighttime without using nighttime annotations. Moreover, we
design a new evaluation framework to address the substantial uncertainty of
semantics in nighttime images. Our central contributions are: 1) a curriculum
framework to gradually adapt semantic segmentation models from day to night via
labeled synthetic images and unlabeled real images, both for progressively
darker times of day, which exploits cross-time-of-day correspondences for the
real images to guide the inference of their labels; 2) a novel
uncertainty-aware annotation and evaluation framework and metric for semantic
segmentation, designed for adverse conditions and including image regions
beyond human recognition capability in the evaluation in a principled fashion;
3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920
unlabeled twilight images with correspondences to their daytime counterparts
plus a set of 151 nighttime images with fine pixel-level annotations created
with our protocol, which serves as a first benchmark to perform our novel
evaluation. Experiments show that our guided curriculum adaptation
significantly outperforms state-of-the-art methods on real nighttime sets both
for standard metrics and our uncertainty-aware metric. Furthermore, our
uncertainty-aware evaluation reveals that selective invalidation of predictions
can lead to better results on data with ambiguous content such as our nighttime
benchmark and profit safety-oriented applications which involve invalid inputs.
