Semi-supervised semantic segmentation involves assigning pixel-wise labels to
unlabeled images at training time. This is useful in a wide range of real-world
applications where collecting pixel-wise labels is not feasible in time or
cost. Current approaches to semi-supervised semantic segmentation work by
predicting pseudo-labels for each pixel from a class-wise probability
distribution output by a model. If the predicted probability distribution is
incorrect, however, this leads to poor segmentation results, which can have
knock-on consequences in safety critical systems, like medical images or
self-driving cars. It is, therefore, important to understand what a model does
not know, which is mainly achieved by uncertainty quantification. Recently,
neural processes (NPs) have been explored in semi-supervised image
classification, and they have been a computationally efficient and effective
method for uncertainty quantification. In this work, we move one step forward
by adapting NPs to semi-supervised semantic segmentation, resulting in a new
model called NP-SemiSeg. We experimentally evaluated NP-SemiSeg on the public
benchmarks PASCAL VOC 2012 and Cityscapes, with different training settings,
and the results verify its effectiveness.

通过将神经过程（NPs）应用于半监督语义分割，我们提出了一种新的模型 NP-SemiSeg，并在公共基准数据集 PASCAL VOC 2012 和 Cityscapes 上进行了实验验证其有效性。