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arXiv · 2210.04889

Turbo Training with Token Dropout

Tengda Han·Weidi Xie·Andrew Zisserman
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Abstract

The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity classification, showing that Turbo training can largely maintain competitive performance while achieving almost 4X speed-up and significantly less memory consumption. (3) Turbo training enables long-schedule video-language training and end-to-end long-video training, delivering competitive or superior performance than previous works, which were infeasible to train under limited resources.

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Tengda Han, Weidi Xie, Andrew Zisserman. 2022-10-10. Turbo Training with Token Dropout. https://arxiv.org/abs/2210.04889

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