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Horizontal Flipping Assisted Disentangled Feature Learning for Semi-supervised Person Re-identification

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Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12624))

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Abstract

In this paper, we propose to learn a powerful Re-ID model by using less labeled data together with lots of unlabeled data, i.e. semi-supervised Re-ID. Such kind of learning enables Re-ID model to be more generalizable and scalable to real-world scenes. Specifically, we design a two-stream encoder-decoder-based structure with shared modules and parameters. For the encoder module, we take the original person image with its horizontal mirror image as a pair of inputs and encode deep features with identity and structural information properly disentangled. Then different combinations of disentangling features are used to reconstruct images in the decoder module. In addition to the commonly used constraints from identity consistency and image reconstruction consistency for loss function definition, we design a novel loss function of enforcing consistent transformation constraints on disentangled features. It is free of labels, and can be applied to both supervised and unsupervised learning branches in our model. Extensive results on four Re-ID datasets demonstrate that by reducing 5/6 labeled data, Our method achieves the best performance on Market-1501 and CUHK03, and comparable accuracy on DukeMTMC-reID and MSMT17.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (No. 61972071), the National Key Research & Development Program (No. 2020YFC2003901), the 2019 Fundamental Research Funds for the Central Universities, the Research Program of Zhejiang lab (No. 2019KD0AB02), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR No. 201900014) and Sichuan Science and Technology Program (No. 2020YJ0036).

Author information

Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China

    Gehan Hao & Xue Zhou

  2. National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yang Yang, Guanan Wang & Zhen Lei

  3. Shenzhen Institute of Advanced Study, UESTC, Shenzhen, China

    Xue Zhou

Authors
  1. Gehan Hao
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  2. Yang Yang
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  3. Xue Zhou
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  4. Guanan Wang
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  5. Zhen Lei
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Corresponding author

Correspondence to Xue Zhou .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Hao, G., Yang, Y., Zhou, X., Wang, G., Lei, Z. (2021). Horizontal Flipping Assisted Disentangled Feature Learning for Semi-supervised Person Re-identification. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12624. Springer, Cham. https://doi.org/10.1007/978-3-030-69535-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-69535-4_2

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