Prototype Guided Pseudo Labeling and Perturbation-based Active Learning for domain adaptive semantic segmentation

This work aims at active domain adaptation to transfer knowledge from a fully-labeled source domain to an entirely unlabeled target domain. During the active learning period, some pixels in the target domain are selected and annotated as active labels through several selection rounds. Such active labels can improve the target domain model performance greatly. However, existing approaches solely rely on pseudo labels, highly-confident classifier predictions on target images, to train the initial target domain model, resulting in a sub-optimal solution for model training. This initial model will be used for active label selection. Meanwhile, previous methods use entropy-based measurement to select pixels for annotation, which fails to detect high-confidence errors in earlier selection rounds due to the absence of target information. To address these issues, we propose a prototype-guided pseudo-label generating approach that leverages the relationships between source prototypes and target features. It generates target pseudo labels based on diverse source prototypes, thereby alleviating the issue of classifier predictions. Furthermore, perturbation-based uncertainty measurement, calculating the discrepancy between the target image and the augmented one, is introduced to find the areas with unstable predictions. Extensive experiments demonstrate that our approach outperforms state-of-the-art active domain adaptation methods on two benchmarks, GTAV → Cityscapes, and SYNTHIA → Cityscapes. Comparable performance is also achieved when compared to fully-supervised methods.