DANIM: Domain adaptation network with intermediate domain masking for night-time scene parsing

Night-time scene parsing is important for practical applications such as autonomous driving and robot vision. Since annotating is time-consuming, Unsupervised Domain Adaptation (UDA) is an effective solution for night-time scene parsing. Due to the low illumination, over/under-exposure, and motion blur in night-time scenes, existing methods can not connect daytime scenes and night-time scenes well, limiting their performance. Some methods rely on day-night paired images, which are costly to collect and therefore impractical. In this paper, we propose DANIM, a self-training UDA network for night-time scene parsing. We introduce an intermediate domain that explicitly models the connection between daytime scenes and night-time scenes from lighting and structure. The intermediate domain shares similar structure information with the night-time target domain and similar lighting information with the daytime source domain. By harnessing the rich prior knowledge of a pre-trained text-driven generative model, the intermediate domain can be generated, and we propose a scoring mechanism for selecting the high-quality one for training. Besides, we propose intermediate domain masking to address the inconsistency between the intermediate domain and the target domain. We further design a coupled mask strategy to make the mask more effective. Extensive experiments show that DANIM has achieved first place on the DarkZurich leaderboard and outperforms state-of-the-art methods on other widely used night-time scene parsing benchmarks, i.e., ACDC-night, NightCity, and NighttimeDriving.