ViP: Video Platform for PyTorch
Published in arXiv as Technical Report, 2019
Summary: Developed a pytorch-based video platform that can handle any image- or video-based problem domainwith minimal changes. It includes strong bookkeeping, mimics large mini-batch computations on lowmemory systems while including a large suite of video-specific preprocessing functions.
Abstract: 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.
Recommended citation: Ganesh, M.R., Hofesmann, E., Louis, N. and Corso, J., ViP: Video Platform for PyTorch.