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Domain adaptation with structural knowledge transfer learning for person re-identification

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Abstract

Recently, supervised person re-identification (Re-ID) algorithms have achieved great performance on benchmarks. However, it highly depends on labeled training samples and may not generalize well to new domains, limiting the applicability of person Re-ID in practical. To this end, we propose a novel unsupervised domain adaptive approach to transfer the learned knowledge across diverse domains. To address the issue of lacking target domain annotations, we perform a supervised classification task using only labeled source data and share weights of two feature extraction networks. Considering unbalanced data distribution between the source domain and target domain, we then adopt a generative adversarial approach with GAN-based losses to reduce domain discrepancy and further improve Re-ID performance. The entire framework can be trained in an unsupervised manner with standard deep neural networks. Extensive experiments demonstrate that our proposed approach performs favourably against state-of-the-art methods.

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Acknowledgements

This work was supported by the Nature Science Foundation of China (No.61762023), the Sprouts Come Special Project of GuiZhou Department of Science and Technology (No. QKHPTRC [2017]5726) and the Startup Project of Doctoral Research of Guizhou Normal University (2017).

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Authors and Affiliations

  1. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550025, China

    Haojie Liu, Fang Guo & Daoxun Xia

  2. Engineering Laboratory for Applied Technology of Big Data in Education, Guizhou Normal University, Guiyang, 550025, China

    Haojie Liu & Daoxun Xia

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  1. Haojie Liu
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  2. Fang Guo
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  3. Daoxun Xia
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Correspondence to Daoxun Xia.

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Liu, H., Guo, F. & Xia, D. Domain adaptation with structural knowledge transfer learning for person re-identification. Multimed Tools Appl 80, 29321–29337 (2021). https://doi.org/10.1007/s11042-021-11139-w

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  • DOI: https://doi.org/10.1007/s11042-021-11139-w

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