Boundary-Based Active Domain Adaptation for Semantic Segmentation Under Adverse Conditions

Existing domain adaptation semantic segmentation (DASS) methods under adverse conditions often depend on pseudo-labels for network training. However, these pseudo-labels are frequently plagued by noise and bias toward high-confidence predictions, thereby impeding the enhancement of segmentation performance. This article tackles the above challenge by proposing a novel boundary-based active domain adaptation (ADA) framework, which efficiently selects both informative low-confidence samples and high-confident but misclassified samples to be labeled while maximizing the segmentation performance under a limited annotation budget. For the evaluation of sample confidence and informativeness, we first propose ranking weighted feature space impurity (RWFSI) metric to quantify category distribution among a sample's nearest neighbors within the feature space and consider the samples with higher RWFSI values as low-confidence samples around the decision boundary, which can also alleviate the category imbalance of active labels. Subsequently, we apply Gaussian mixture models (GMMs) to model the distribution across source and target domains. Using the spatial arrangement of each GMM component, we define the intraclass domain shift score (ICDSS), which identifies samples with high ICDSS values as those more likely to be high-confidence but misclassified, aiding in refining sample selection. Extensive experiments demonstrate that our method is superior to the existing state-of-the-art domain adaptation and active learning (AL) methods and comparable with those of full supervision. The code will be released at https://github.com/1061018609/BADA.