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SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-identification

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Book cover Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11362))

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Abstract

Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.

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Author information

Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xing Fan, Hao Luo & Wei Jiang

  2. Megvii Inc. (Face++), Beijing, China

    Xing Fan, Hao Luo, Xuan Zhang & Chi Zhang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Lingxiao He

Authors
  1. Xing Fan
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  2. Hao Luo
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  3. Xuan Zhang
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  4. Lingxiao He
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  5. Chi Zhang
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  6. Wei Jiang
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Corresponding author

Correspondence to Wei Jiang .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Cite this paper

Fan, X., Luo, H., Zhang, X., He, L., Zhang, C., Jiang, W. (2019). SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-identification. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-20890-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20889-9

  • Online ISBN: 978-3-030-20890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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