  Semi-supervised semantic segmentation needs rich and robust supervision on
unlabeled data. Consistency learning enforces the same pixel to have similar
features in different augmented views, which is a robust signal but neglects
relationships with other pixels. In comparison, contrastive learning considers
rich pairwise relationships, but it can be a conundrum to assign binary
positive-negative supervision signals for pixel pairs. In this paper, we take
the best of both worlds and propose multi-view correlation consistency (MVCC)
learning: it considers rich pairwise relationships in self-correlation matrices
and matches them across views to provide robust supervision. Together with this
correlation consistency loss, we propose a view-coherent data augmentation
strategy that guarantees pixel-pixel correspondence between different views. In
a series of semi-supervised settings on two datasets, we report competitive
accuracy compared with the state-of-the-art methods. Notably, on Cityscapes, we
achieve 76.8% mIoU with 1/8 labeled data, just 0.6% shy from the fully
supervised oracle.

Computer Vision and Pattern Recognition