  Night-time scene parsing aims to extract pixel-level semantic information in
night images, aiding downstream tasks in understanding scene object
distribution. Due to limited labeled night image datasets, unsupervised domain
adaptation (UDA) has become the predominant method for studying night scenes.
UDA typically relies on paired day-night image pairs to guide adaptation, but
this approach hampers dataset construction and restricts generalization across
night scenes in different datasets. Moreover, UDA, focusing on network
architecture and training strategies, faces difficulties in handling classes
with few domain similarities. In this paper, we leverage Prompt Images Guidance
(PIG) to enhance UDA with supplementary night knowledge. We propose a
Night-Focused Network (NFNet) to learn night-specific features from both target
domain images and prompt images. To generate high-quality pseudo-labels, we
propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with
fewer domain similarities are predicted by NFNet, which excels in parsing night
features, while classes with more domain similarities are predicted by UDA,
which has rich labeled semantics. Additionally, we propose two data
augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate
Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few
prompt images. We conduct extensive experiments on four night-time datasets:
NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that
utilizing PIG can enhance the parsing accuracy of UDA.
