Abstract— In autonomous driving, thermal image semantic
segmentation has emerged as a critical research area, owing
to its ability to provide robust scene understanding under
adverse visual conditions. In particular, unsupervised domain
adaptation (UDA) for thermal image segmentation can be an
efficient solution to address the lack of labeled thermal datasets.
Nevertheless, since these methods do not effectively utilize
the complementary information between RGB and thermal
images, they significantly decrease performance during domain
adaptation. In this paper, we present a comprehensive study on
cross-spectral UDA for thermal image semantic segmentation.
We first propose a novel masked mutual learning strategy that
promotes complementary information exchange by selectively
transferring results between each spectral model while masking
out uncertain regions. Additionally, we introduce a novel prototypical self-supervised loss designed to enhance the performance
of the thermal segmentation model in nighttime scenarios.
This approach addresses the limitations of RGB pre-trained
networks, which cannot effectively transfer knowledge under
low illumination due to the inherent constraints of RGB sensors.
In experiments, our method achieves higher performance over
previous UDA methods and comparable performance to stateof-the-art supervised methods.
