  It is challenging to annotate large-scale datasets for supervised video
shadow detection methods. Using a model trained on labeled images to the video
frames directly may lead to high generalization error and temporal inconsistent
results. In this paper, we address these challenges by proposing a
Spatio-Temporal Interpolation Consistency Training (STICT) framework to
rationally feed the unlabeled video frames together with the labeled images
into an image shadow detection network training. Specifically, we propose the
Spatial and Temporal ICT, in which we define two new interpolation schemes,
\textit{i.e.}, the spatial interpolation and the temporal interpolation. We
then derive the spatial and temporal interpolation consistency constraints
accordingly for enhancing generalization in the pixel-wise classification task
and for encouraging temporal consistent predictions, respectively. In addition,
we design a Scale-Aware Network for multi-scale shadow knowledge learning in
images, and propose a scale-consistency constraint to minimize the discrepancy
among the predictions at different scales. Our proposed approach is extensively
validated on the ViSha dataset and a self-annotated dataset. Experimental
results show that, even without video labels, our approach is better than most
state of the art supervised, semi-supervised or unsupervised image/video shadow
detection methods and other methods in related tasks. Code and dataset are
available at \url{https://github.com/yihong-97/STICT}.
