  Along with predictive performance and runtime speed, reliability is a key
requirement for real-world semantic segmentation. Reliability encompasses
robustness, predictive uncertainty and reduced bias. To improve reliability, we
introduce Superpixel-mix, a new superpixel-based data augmentation method with
teacher-student consistency training. Unlike other mixing-based augmentation
techniques, mixing superpixels between images is aware of object boundaries,
while yielding consistent gains in segmentation accuracy. Our proposed
technique achieves state-of-the-art results in semi-supervised semantic
segmentation on the Cityscapes dataset. Moreover, Superpixel-mix improves the
reliability of semantic segmentation by reducing network uncertainty and bias,
as confirmed by competitive results under strong distributions shift (adverse
weather, image corruptions) and when facing out-of-distribution data.
