  Semi-supervised semantic segmentation focuses on the exploration of a small
amount of labeled data and a large amount of unlabeled data, which is more in
line with the demands of real-world image understanding applications. However,
it is still hindered by the inability to fully and effectively leverage
unlabeled images. In this paper, we reveal that cross-window consistency (CWC)
is helpful in comprehensively extracting auxiliary supervision from unlabeled
data. Additionally, we propose a novel CWC-driven progressive learning
framework to optimize the deep network by mining weak-to-strong constraints
from massive unlabeled data. More specifically, this paper presents a biased
cross-window consistency (BCC) loss with an importance factor, which helps the
deep network explicitly constrain confidence maps from overlapping regions in
different windows to maintain semantic consistency with larger contexts. In
addition, we propose a dynamic pseudo-label memory bank (DPM) to provide
high-consistency and high-reliability pseudo-labels to further optimize the
network. Extensive experiments on three representative datasets of urban views,
medical scenarios, and satellite scenes demonstrate our framework consistently
outperforms the state-of-the-art methods with a large margin. Code will be
available publicly.
