  Cross-modal knowledge distillation deals with transferring knowledge from a
model trained with superior modalities (Teacher) to another model trained with
weak modalities (Student). Existing approaches require paired training examples
exist in both modalities. However, accessing the data from superior modalities
may not always be feasible. For example, in the case of 3D hand pose
estimation, depth maps, point clouds, or stereo images usually capture better
hand structures than RGB images, but most of them are expensive to be
collected. In this paper, we propose a novel scheme to train the Student in a
Target dataset where the Teacher is unavailable. Our key idea is to generalize
the distilled cross-modal knowledge learned from a Source dataset, which
contains paired examples from both modalities, to the Target dataset by
modeling knowledge as priors on parameters of the Student. We name our method
"Cross-Modal Knowledge Generalization" and demonstrate that our scheme results
in competitive performance for 3D hand pose estimation on standard benchmark
datasets.
