  In autonomous driving, learning a segmentation model that can adapt to
various environmental conditions is crucial. In particular, copying with severe
illumination changes is an impelling need, as models trained on daylight data
will perform poorly at nighttime. In this paper, we study the problem of Domain
Adaptive Nighttime Semantic Segmentation (DANSS), which aims to learn a
discriminative nighttime model with a labeled daytime dataset and an unlabeled
dataset, including coarsely aligned day-night image pairs. To this end, we
propose a novel Bidirectional Mixing (Bi-Mix) framework for DANSS, which can
contribute to both image translation and segmentation adaptation processes.
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of
day-night image pairs to improve the quality of nighttime image relighting. On
the other hand, in the segmentation adaptation stage, Bi-Mix effectively
bridges the distribution gap between day and night domains for adapting the
model to the night domain. In both processes, Bi-Mix simply operates by mixing
two samples without extra hyper-parameters, thus it is easy to implement.
Extensive experiments on Dark Zurich and Nighttime Driving datasets demonstrate
the advantage of the proposed Bi-Mix and show that our approach obtains
state-of-the-art performance in DANSS. Our code is available at
https://github.com/ygjwd12345/BiMix.
