Semi-supervised Semantic Segmentation via Prototypical Contrastive Learning

The key idea of semi-supervised semantic segmentation is to leverage both labeled and unlabeled data. To achieve the goal, most existing methods resort to pseudo-labels for training. However, the dispersed feature distribution and biased category centroids could inevitably lead to the calculation deviation of feature distances and noisy pseudo labels. In this paper, we propose to denoise pseudo labels with representative prototypes. Specifically, to mitigate the effects of outliers, we first employ automatic clustering to model multiple prototypes with which the distribution of outliers can be better characterized. Then, a compact structure and clear decision boundary can be obtained by using contrastive learning. It is worth noting that our prototype-wise pseudo segmentation strategy can also be applied in most existing semantic segmentation networks. Experimental results show that our method outperforms other state-of-the-art approaches on both Cityscapes and Pascal VOC semantic segmentation datasets under various data partition protocols.