  The popularity of Deep Learning for real-world applications is ever-growing.
With the introduction of high performance hardware, applications are no longer
limited to image recognition. With the introduction of more complex problems
comes more and more complex solutions, and the increasing need for explainable
AI. Deep Neural Networks for Video tasks are amongst the most complex models,
with at least twice the parameters of their Image counterparts. However,
explanations for these models are often ill-adapted to the video domain. The
current work in explainability for video models is still overshadowed by Image
techniques, while Video Deep Learning itself is quickly gaining on methods for
still images. This paper seeks to highlight the need for explainability methods
designed with video deep learning models, and by association spatio-temporal
input in mind, by first illustrating the cutting edge for video deep learning,
and then noting the scarcity of research into explanations for these methods.
