Collaborative Spatial-Temporal Distillation for Efficient Video Deraining

In this paper, we propose a novel knowledge distillation framework to improve the efficiency of deep networks for video deraining. The knowledge is transferred from a large-scale powerful teacher network to a compact efficient student network via the proposed collaborative spatial-temporal distillation framework. The framework is equipped with three collaboration schemes of different granularities that make use of spatial-temporal redundancy in a complementary way for better distillation performance. First, the spatial alignment module applies distillation constraints at different spatial scales to achieve better scale invariance in transferred knowledge. Second, the temporal alignment module traces both temporal status between teacher and student separately and collaboratively, to comprehensively utilize inter-frame information. Third, these two alignment modules interact through a spatial-temporal adaptor, where spatial-temporal knowledge is transferred in a unified framework. Extensive experiments demonstrate the superiority of our distillation framework as well as the effectiveness of each module. Our code is available at: https://github.com/HuYuzhang/Knowledge-Distillation.