Modeling cross-scale consistency via dual-view learning for semi-supervised semantic segmentation

Mean Teacher (MT)-based semi-supervised semantic segmentation faces two key limitations: (i) teacher-generated pseudo-labels are sensitive to scale variations and boundary details, leading to the accumulation of scale-induced uncertainty during iterative self-training; (ii) existing consistency regularization is largely restricted to single-scale or static alignment, lacking explicit modeling of class-level semantic structures across different views. To address these issues, we propose Cross-scale Dual-View Consistency Learning (CDV-CL), which explicitly models the complementarity between predictions from zoomed-in and zoomed-out views and jointly enforces pixel-level and class-level constraints to improve pseudo-label quality and stabilize semantic representation learning. Within this framework, we introduce a dual-view semantic boundary complementary fusion module (SEC-Fuse). It leverages cross-view confidence distributions and prediction discrepancies to estimate pixel-wise adaptive weights across dual-view teacher predictions, and then performs region-aware adaptive fusions to generate pseudo-labels that are more robust to scale variations. To complement the pixel-level pseudo-label refinement of SEC-Fuse, we further introduce cross-scale prototype alignment (CSP-Align), which aligns class prototypes across views in high-confidence non-boundary regions, thereby strengthening the class-level structure of the feature space and improving discriminability. Experiments show that CDV-CL achieves 84.72 % and 81.02 % mIoU on Pascal VOC and Cityscapes under the 1/16-label setting, respectively, outperforming state-of-the-art methods. It also demonstrates strong generalization to medical skin lesion segmentation on ISIC and remote sensing change detection.