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.

翻译：在自主驱动中,学习能够适应各种环境条件的分解模型至关重要。 特别是, 复制严重照明变化的分解模型是一个催化需求, 因为日光数据培训模型在夜间效果不佳。 在本文中, 我们研究Domain适应夜间语义分解( DanSS)的问题, 目的是学习一种带有标签的日间数据集和无标签数据集的歧视性夜间模型, 包括粗略地对齐的日间图像组合。 为此, 我们提议为丹SS 复制一个新的双向混合( Bi- Mix) 框架, 这有助于图像翻译和分解适应进程。 具体而言, 在图像翻译阶段, Bi- Mix 利用日间图像配对知识来提高夜间图像重亮质量。 在分解适应阶段, Bi- Mix 有效地弥合了日间和夜间域之间的分布差距, 以适应夜间域。 在这两个过程中, Bi- Mix 只需将两个样本混合在一起操作, 而无需超超超超双双的图像翻译和分解分解过程。 因此, Bi- mix 展示了我们Dreal- deal- deal- laxal