Co-training with Progressive Distribution Alignment and Uncertainty-Interactive Relabeling for Semi-Supervised Domain Adaptive Semantic Segmentation

Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.