  Prototype-based classification is a classical method in machine learning, and
recently it has achieved remarkable success in semi-supervised semantic
segmentation. However, the current approach isolates the prototype
initialization process from the main training framework, which appears to be
unnecessary. Furthermore, while the direct use of K-Means algorithm for
prototype generation has considered rich intra-class variance, it may not be
the optimal solution for the classification task. To tackle these problems, we
propose a novel boundary-refined prototype generation (BRPG) method, which is
incorporated into the whole training framework. Specifically, our approach
samples and clusters high- and low-confidence features separately based on a
confidence threshold, aiming to generate prototypes closer to the class
boundaries. Moreover, an adaptive prototype optimization strategy is introduced
to make prototype augmentation for categories with scattered feature
distributions. Extensive experiments on the PASCAL VOC 2012 and Cityscapes
datasets demonstrate the superiority and scalability of the proposed method,
outperforming the current state-of-the-art approaches. The code is available at
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