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A Framework for Jointly Training GAN with Person Re-Identification Model

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Book cover Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12664))

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Abstract

To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploited generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them which probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: 1) The adversarial training that encourages it to fool the discriminator; 2) The generated samples that augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separated training and achieves state-of-the-art re-id performance.

Supported by the National Natural Science Foundation of China under Grant U1913204, Shandong Major Scientific and Technological Innovation Project 2018CXGC1503, and Qilu Young Scholars Program of Shandong University No. 31400082063101.

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Author information

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, China

    Zhongwei Zhao, Ran Song & Qian Zhang

  2. School of Computer Science, Liaocheng University, Liaocheng, China

    Peng Duan

  3. School of Mathematics and Statistics, Qilu University of Technology, Jinan, China

    Youmei Zhang

Authors
  1. Zhongwei Zhao
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  2. Ran Song
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  3. Qian Zhang
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  4. Peng Duan
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  5. Youmei Zhang
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Corresponding author

Correspondence to Ran Song .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell'Informazione, University of Firenze, Florence, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Zhao, Z., Song, R., Zhang, Q., Duan, P., Zhang, Y. (2021). A Framework for Jointly Training GAN with Person Re-Identification Model. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-68799-1_3

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