/sci/ - Science & Math

https://arxiv.org/abs/1611.07715
>This work presents a fast, accurate, general, and endto-end
>framework for video recognition. It is the first of
>its kind.
Abstract
>Deep convolutional neutral networks have achieved
>great success on image recognition tasks. Yet, it is nontrivial
>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.