Deep convolutional networks have recently achieved great success in video
recognition, yet their practical realization remains a challenge due to the
large amount of computational resources required to achieve robust recognition.
Motivated by the effectiveness of quantization for boosting efficiency, in this
paper, we propose a dynamic network quantization framework, that selects
optimal precision for each frame conditioned on the input for efficient video
recognition. Specifically, given a video clip, we train a very lightweight
network in parallel with the recognition network, to produce a dynamic policy
indicating which numerical precision to be used per frame in recognizing
videos. We train both networks effectively using standard backpropagation with
a loss to achieve both competitive performance and resource efficiency required
for video recognition. Extensive experiments on four challenging diverse
benchmark datasets demonstrate that our proposed approach provides significant
savings in computation and memory usage while outperforming the existing
state-of-the-art methods.

本文提出动态网络量化框架，依据每个输入帧选择最佳精度以实现高效的视频识别，实验结果显示，在四种具有挑战性的基准数据集上，与现有先进方法相比，该方法提供了显著的计算和内存使用节约，同时保持竞争性能。