  This work presents the Video Platform for PyTorch (ViP), a deep
learning-based framework designed to handle and extend to any problem domain
based on videos. ViP supports (1) a single unified interface applicable to all
video problem domains, (2) quick prototyping of video models, (3) executing
large-batch operations with reduced memory consumption, and (4) easy and
reproducible experimental setups. ViP's core functionality is built with
flexibility and modularity in mind to allow for smooth data flow between
different parts of the platform and benchmarking against existing methods. In
providing a software platform that supports multiple video-based problem
domains, we allow for more cross-pollination of models, ideas and stronger
generalization in the video understanding research community.
