Dynamic Network Quantization for Efficient Video Inference

By Ximeng Sun, Rameswar Panda, Chun-Fu Chen, Aude Oliva, Rogério Schmidt Feris, Kate Saenko. 2021

57 citations recorded by the source corpus.

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. Project page: https://cs-people.bu.edu/sunxm/VideoIQ/project.html.

Subfields: Computer Vision and Pattern Recognition, Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Yizeng Han
  5. Le Yang
  6. Honghui Wang
  7. Luc Van Gool
  8. Alan L. Yuille

In Efficiency & Compression

  1. Shiji Song
  2. Gao Huang
  3. Yulin Wang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang

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