Structured binary neural networks for accurate image classification and semantic segmentation

Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

76 Citations (Scopus)


In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically for mobile devices with limited power capacity and computation resources. By assuming the same architecture to full-precision networks, previous works on quantizing CNNs seek to preserve the floating-point information using a set of discrete values, which we call value approximation. However, we take a novel "structure approximation" view for quantization--it is very likely that a different architecture may be better for best performance. In particular, we propose a "network decomposition" strategy, named Group-Net, in which we divide the network into groups. In this way, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. In addition, we learn effect connections among groups to improve the representational capability. Moreover, the proposed Group-Net shows strong generalization to other tasks. For instance, we extend Group-Net for highly accurate semantic segmentation by embedding rich context into the binary structure. Experiments on both classification and semantic segmentation tasks demonstrate the superior performance of the proposed methods over various popular architectures. In particular, we outperform the previous best binary neural networks in terms of accuracy and huge computation saving.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
EditorsAbhinav Gupta, Derek Hoiem, Gang Hua, Zhuowen Tu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781728132938
ISBN (Print)9781728132945
Publication statusPublished - 2019
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2019 - Long Beach, United States of America
Duration: 16 Jun 201920 Jun 2019
Conference number: 32nd (Proceedings)


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2019
Abbreviated titleCVPR 2019
Country/TerritoryUnited States of America
CityLong Beach
Internet address


  • Deep Learning
  • Grouping and Shape
  • Representation Learning
  • Segmentation

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