  Semi-supervised semantic segmentation requires the model to effectively
propagate the label information from limited annotated images to unlabeled
ones. A challenge for such a per-pixel prediction task is the large intra-class
variation, i.e., regions belonging to the same class may exhibit a very
different appearance even in the same picture. This diversity will make the
label propagation hard from pixels to pixels. To address this problem, we
propose a novel approach to regularize the distribution of within-class
features to ease label propagation difficulty. Specifically, our approach
encourages the consistency between the prediction from a linear predictor and
the output from a prototype-based predictor, which implicitly encourages
features from the same pseudo-class to be close to at least one within-class
prototype while staying far from the other between-class prototypes. By further
incorporating CutMix operations and a carefully-designed prototype maintenance
strategy, we create a semi-supervised semantic segmentation algorithm that
demonstrates superior performance over the state-of-the-art methods from
extensive experimental evaluation on both Pascal VOC and Cityscapes benchmarks.
