  The popular methods for semi-supervised semantic segmentation mostly adopt a
unitary network model using convolutional neural networks (CNNs) and enforce
consistency of the model's predictions over perturbations applied to the inputs
or model. However, such a learning paradigm suffers from two critical
limitations: a) learning the discriminative features for the unlabeled data; b)
learning both global and local information from the whole image. In this paper,
we propose a novel Semi-supervised Learning (SSL) approach, called
Transformer-CNN Cohort (TCC), that consists of two students with one based on
the vision transformer (ViT) and the other based on the CNN. Our method subtly
incorporates the multi-level consistency regularization on the predictions and
the heterogeneous feature spaces via pseudo-labeling for the unlabeled data.
First, as the inputs of the ViT student are image patches, the feature maps
extracted encode crucial class-wise statistics. To this end, we propose
class-aware feature consistency distillation (CFCD) that first leverages the
outputs of each student as the pseudo labels and generates class-aware feature
(CF) maps for knowledge transfer between the two students. Second, as the ViT
student has more uniform representations for all layers, we propose
consistency-aware cross distillation (CCD) to transfer knowledge between the
pixel-wise predictions from the cohort. We validate the TCC framework on
Cityscapes and Pascal VOC 2012 datasets, which outperforms existing SSL methods
by a large margin.
