## 2 Related Work  
**Domain adaptation for semantic segmentation:** Domain adaptation seeks to transfer knowledge learned in the source domain to the target domain, where the object classes are similar but the distribution of data statistics differs. Currently, a portion of domain adaptive schemes adopt adversarial learning frameworks, introducing an adversarial loss function between the source and target domains to guide the model in aligning feature representations across different data domains. For example, in [20], [PERSON] et al. proposed a new approach to semantic segmentation using category-constrained [21] full convolutional domain adversarial learning. AdapSegNet [6] utilizes adversarial training to achieve feature alignment in the source and target domains. Additionally, several approaches employ joint training and multiple task learning strategies to improve model generalization by sharing parameters among source and target domains. In BDL [22], images from both source and target domains are input into a shared convolutional neural network, with the last layer divided into two branches for semantic segmentation tasks in the respective source and target domains. Through sharing the feature extraction layer of the network, the source and target domains can leverage the underlying image feature representation, thereby improving adaptation to redundant representations of the target domain. Unlike adversarial learning, style transfer [18] and image translation from source image to target image are also widely used for domain adaptation. They typically incorporate a domain invariance loss function into the generator network to enforce domain-invariant image generation in the target domain [23; 24]. This type of loss function commonly comprises both an adversarial loss and a domain invariance loss. Specifically, the generator network is trained using the adversarial loss, while the domain invariance loss is used to teach the generator network to learn the shared features between source and target domains, resulting in domain-invariant representations. Some other studies have explored the combination of self-training strategy and fine-tuning strategy through multiple rounds of network training. However, the self-training strategy may introduce noise when using pseudo-labeling, thus impacting model performance [25]. To mitigate the influence of noise, researchers have proposed several improved self-training techniques [26; 27], such as using model ensembles to reduce noise or refining pseudo-labeling to reduce mislabeling. Alternatively, some studies have employed course-based learning [28; 29] to acquire simple attributes in the target domain before using them to normalize semantic segmentation models. However, the significant visual disparities between daytime and nighttime images pose a formidable challenge for these methods, making them ill suited to effectively handling domain adaptation in scenarios with markedly different illumination intensities. Consequently, they often fall short of delivering satisfactory performance in nighttime semantic segmentation. This paper explores more efficient techniques to minimize the domain gap so that transfer models can achieve accurate segmentation predictions. **Nighttime (low-light) semantic segmentation:** Some studies have demonstrated the effectiveness of employing intermediate domains for the progressive adaptation of semantic models trained on daytime scenes to nighttime scenes. For example, [PERSON] et al. [10] proposed a step-by-step adaptive approach based on intermediate domains. This approach leverages an intermediate twilight domain as a bridge between daytime and nighttime scenes and trains an intermediate model on the twilight domain, which is then applied to the semantic segmentation of nighttime scenes. Later, [PERSON] et al. [5; 6] extended the approach to a class of guided curriculum adaptation frameworks, incorporating synthetic and unlabeled real images to establish correspondences in scene images at various times.