Deep Feature Flow for Video Recognition

CVPR 2017  ·  Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei ·

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. read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Semantic Segmentation Cityscapes val DFF [22] mIoU 69.2 # 8


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