Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition.
@article{arxiv.1611.07715,
title = {Deep Feature Flow for Video Recognition},
author = {Xizhou Zhu and Yuwen Xiong and Jifeng Dai and Lu Yuan and Yichen Wei},
journal= {arXiv preprint arXiv:1611.07715},
year = {2017}
}