Unsupervised RGB-To-Thermal Domain Adaptation Via Multi-Domain Attention Network

This work presents a new method for unsuper- vised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network. Our method does not re- quire any thermal annotations or co-registered RGB-thermal pairs, enabling robots to perform visual tasks at night and in adverse weather conditions without incurring additional costs of data labeling and registration. Current unsupervised domain adaptation methods look to align global images or features across domains. However, when the domain shift is significantly larger for cross-modal data, not all features can be transferred. We solve this problem by using a shared backbone network that promotes generalization, and domain- specific attention that reduces negative transfer by attending to domain-invariant and easily-transferable features. Our ap- proach outperforms the state-of-the-art RGB-to-thermal adap- tation method in classification benchmarks, and is successfully applied to thermal river scene segmentation using only syn- thetic RGB images. Our code is made publicly available at https://github.com/ganlumomo/thermal-uda-attention.