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Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Haiming Xu · Lingqiao Liu · Qiuchen Bian · Zhen Yang


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.

Author Information

Haiming Xu (The University of Adelaide)
Lingqiao Liu (The University of Adelaide)
Qiuchen Bian (Northeastern University)
Zhen Yang (Huawei Technologies Ltd.)

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