Temporal Consistency as Pretext Task in Unsupervised Domain Adaptation for Semantic Segmentation

Intelligent and autonomous robots (and vehicles) largely adopt computer vision systems to help in localization, navigation and obstacle avoidance tasks. By integrating different deep learning techniques, such as Object Detection and Image Semantic Segmentation, these systems achieve high accuracy in the domain they were trained on. Nonetheless, robustly operating in different domains still poses a major challenge to vision-based perception. In this sense, Unsupervised Domain Adaptation (UDA) has recently gained momentum due to its importance to real-world applications. Specifically, it leverages the prompt availability of unlabeled data to design auxiliary sources of supervision to guide the knowledge transfer between domains. The advantages of such an approach are two-fold: avoiding going through exhaustive labeling processes, and enhancing adaptation performance. In this scenario, exploring temporal correlations in unlabeled video data stands as an interesting alternative, which has not yet been explored to its full potential. In this work, we propose a Self-supervised learning framework that employs Temporal Consistency from unlabeled video sequences as a pretext task for improving UDA for Semantic Segmentation (UDASS). A simple yet effective strategy, it has shown promising results in a real-to-real adaptation setting. Our results and discussions are expected to benefit both new and experienced researchers on the subject.