Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization

Understand

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.