This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.

本文提出了一种基于黄昏的方法，通过渐进适应白天场景训练的语义模型，从而实现对夜间场景的语义图像分割。同时，我们构建了一个道路场景数据集，包含从白天到黄昏和到夜晚的3.5万张图像，并密集标注了部分夜晚图像，实验证明该方法可以有效地将白天场景下的模型适应到夜晚场景，无需额外的人工标注。

暗模型适应：从白天到黑夜的语义图像分割