ViP: Video Platform for PyTorch
This provides a software platform for researchers working on video-based problems, enabling easier experimentation and benchmarking, though it is incremental as it builds on existing deep learning tools.
The authors introduced ViP, a PyTorch-based framework for video processing that offers a unified interface, quick prototyping, reduced memory usage for large batches, and reproducible setups, aiming to enhance cross-domain research in video understanding.
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.