  In this paper, we tackle a new problem: how to transfer knowledge from the
pre-trained cumbersome yet well-performed CNN-based model to learn a compact
Vision Transformer (ViT)-based model while maintaining its learning capacity?
Due to the completely different characteristics of ViT and CNN and the
long-existing capacity gap between teacher and student models in Knowledge
Distillation (KD), directly transferring the cross-model knowledge is
non-trivial. To this end, we subtly leverage the visual and
linguistic-compatible feature character of ViT (i.e., student), and its
capacity gap with the CNN (i.e., teacher) and propose a novel CNN-to-ViT KD
framework, dubbed C2VKD. Importantly, as the teacher's features are
heterogeneous to those of the student, we first propose a novel
visual-linguistic feature distillation (VLFD) module that explores efficient KD
among the aligned visual and linguistic-compatible representations. Moreover,
due to the large capacity gap between the teacher and student and the
inevitable prediction errors of the teacher, we then propose a pixel-wise
decoupled distillation (PDD) module to supervise the student under the
combination of labels and teacher's predictions from the decoupled target and
non-target classes. Experiments on three semantic segmentation benchmark
datasets consistently show that the increment of mIoU of our method is over
200% of the SoTA KD methods
