ViP: Video Platform for PyTorch

TL;DR

ViP is a flexible, modular deep learning platform built on PyTorch that simplifies video model development, supports large-batch processing, and promotes cross-domain research in video understanding.

Contribution

Introduces ViP, a unified, extensible platform for video deep learning that enhances prototyping, efficiency, and reproducibility across various video problem domains.

Findings

Supports all video problem domains with a single interface

Enables quick prototyping of video models

Reduces memory usage for large-batch operations

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.