Towards Unsupervised Online Domain Adaptation for Semantic Segmentation

Yevhen Kuznietsov, Marc Proesmans, Luc Van Gool; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2022, pp. 261-271

In recent years, there has been significant progress in overcoming the negative effects of domain shift in semantic segmentation. Yet, existing unsupervised domain adaptation methods operate in an offline fashion, which imposes multiple restrictions on their deployment in real world scenarios. In this paper, we introduce a problem of online domain adaptation for semantic segmentation, which involves producing predictions for and, at the same time, continuously adapting a model to new frames of target domain videos. To tackle this problem, we propose a novel method which utilizes unsupervised structure-from-motion cues as the primary source of domain adaptation. By optimizing online the representation shared between depth and semantics networks, our geometry-guided algorithm achieves semantic segmentation performance comparable to state-of-the-art offline methods, without using target domain training data whatsoever.