In this letter, we propose a multi-spectral unsupervised domain adaptation for thermal image semantic segmentation. The proposed framework aims to address the data scarcity problem and boost segmentation performance in the thermal domain with the help of existing large-scale RGB datasets and segmentation knowledge from an RGB image segmentation network. We also enhance the generalization capability of our thermal segmentation network with pixel-level domain adaptation bridging day and night thermal image domains. With our framework, a thermal image segmentation network can achieve high performance without any ground-truth labels by exploiting successive multi-spectral knowledge transfers including RGB-to-RGB, RGB-to-Thermal, and Thermal-to-Thermal adaptations. Moreover, we provide a real-world RGB-Thermal semantic segmentation dataset with 950 manually annotated Cityscapes-style ground-truth labels in 19 classes. Experimental results on real-world datasets demonstrate the effectiveness and robustness of the proposed framework quantitatively and qualitatively.

中文翻译：

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MS-UDA：用于热图像语义分割的多光谱无监督域适应

在这封信中，我们提出了一种用于热图像语义分割的多光谱无监督域适应。所提出的框架旨在借助现有的大规模 RGB 数据集和来自 RGB 图像分割网络的分割知识，解决数据稀缺问题并提高热域中的分割性能。我们还通过连接日夜热图像域的像素级域自适应增强了热分割网络的泛化能力。使用我们的框架，热图像分割网络可以通过利用连续的多光谱知识转移（包括 RGB 到 RGB、RGB 到热和热到热适应）来实现高性能，而无需任何真实标签。而且，我们提供了一个真实世界的 RGB-Thermal 语义分割数据集，其中包含 19 个类别中的 950 个手动注释的 Cityscapes 风格的地面实况标签。真实世界数据集的实验结果从数量和质量上证明了所提出框架的有效性和稳健性。