  Motivated by the growing amount of publicly available video data on online
streaming services and an increased interest in applications that analyze
continuous video streams such as autonomous driving, this technical report
provides a theoretical insight into deep neural networks for video learning,
under label constraints. I build upon previous work in video learning for
computer vision, make observations on model performance and propose further
mechanisms to help improve our observations.
