Nighttime semantic segmentation plays a crucial role in practical applications, such as autonomous driving, where it frequently encounters difficulties caused by inadequate illumination conditions and the absence of well-annotated datasets. Moreover, semantic segmentation models trained on daytime datasets often face difficulties in generalizing effectively to nighttime conditions. Unsupervised domain adaptation (UDA) has shown the potential to address the challenges and achieved remarkable results for nighttime semantic segmentation. However, existing methods still face limitations in 1) their reliance on style transfer or relighting models, which struggle to generalize to complex nighttime environments, and 2) their ignorance of dynamic and small objects like vehicles and poles, which are difficult to be directly learned from other domains. This paper proposes a novel UDA method that refines both label and feature levels for dynamic and small objects for nighttime semantic segmentation. First, we propose a dynamic and small object refinement module to complement the knowledge of dynamic and small objects from the source domain to target the nighttime domain. These dynamic and small objects are normally context-inconsistent in under-exposed conditions. Then, we design a feature prototype alignment module to reduce the domain gap by deploying contrastive learning between features and prototypes of the same class from different domains, while re-weighting the categories of dynamic and small objects. Extensive experiments on three benchmark datasets demonstrate that our method outperforms prior arts by a large margin for nighttime segmentation. Project page: https://rorisis.github.io/DSRNSS/.

翻译：夜间语义分割在实际应用（如自动驾驶）中扮演着关键角色，但常因光照条件不足及缺乏高质量标注数据集而面临挑战。此外，基于日间数据集训练的语义分割模型难以有效泛化至夜间场景。无监督域自适应方法已展现出应对上述挑战的潜力，并在夜间语义分割中取得了显著成果。然而，现有方法仍存在局限：1）依赖风格迁移或重光照模型，难以泛化至复杂夜间环境；2）忽略车辆、电线杆等动态与小目标物体，这些目标难以直接从其他域中学习。本文提出一种新颖的无监督域自适应方法，在标签与特征层面精炼动态与小目标，以优化夜间语义分割。首先，我们设计动态与小目标精炼模块，将源域中动态与小目标的知识补充至目标夜间域——此类目标在欠曝光条件下通常存在上下文不一致性。随后，构建特征原型对齐模块，通过跨域同类特征与原型间的对比学习减少域间隙，同时重新加权动态与小目标类别。在三个基准数据集上的大量实验表明，本方法在夜间分割任务上显著优于现有技术。项目主页：https://rorisis.github.io/DSRNSS/。