Multi-branch perturbation learning with constraint simulation for semi-supervised semantic segmentation

Current semi-supervised semantic segmentation (SSS) methods improve generalization via weak-to-strong pseudo-supervision with image perturbations. However, many methods are limited by employing a single perturbation mode and a specific weak-to-strong learning strategy, restricting exploration of the perturbation space and hindering performance in fine-grained segmentation. While diverse perturbations are intuitively beneficial, simply combining them can lead to inefficient optimization and instability. In this paper, we propose a multi-branch strong perturbation constraint learning framework for SSS. Our framework introduces a novel multi-branch perturbation learning (MSPL) strategy, employing multiple parallel branches with diverse strong augmentations to expand the perturbation space and capture complex semantic variations. We further design a novel constraint simulation loss (CSSL), based on a hierarchical consistency learning structure (weak-to-strong and strong-to-strong), which enforces strong-to-strong consistency between different perturbation branches. CSSL mitigates instability and enhances robustness to perturbation-induced noise, enabling the network to better generalize and achieve more accurate segmentation, especially for fine object boundaries. Extensive evaluations on benchmark datasets (PASCAL VOC 2012, Cityscapes, COCO) demonstrate that our method achieves state-of-the-art performance. Ablation studies further validate the effectiveness of our proposed MSPL and CSSL components.