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Robust joint learning network: improved deep representation learning for person re-identification

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Abstract

Existing person re-identification methods, which based on deep representation learning, mostly only focus on either global feature or local feature. This obviously ignores the joint advantages and the correlation between global and local features. In this paper, we test and verify the benefits of jointly learning local and global features in a network based on the Convolutional Neural Network (CNN). Specifically, we give distinct weights to global loss and local loss when considering their different influence on our research, then we innovatively combine two losses into one loss. Besides, we propose a novel and strong network to learn part-level features with unified partition. Experimental results on three person ReID data sets, show that our method outperforms existing deep learning methods.

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Acknowledgements

This paper was supported in part by the National Natural Science Foundation of China under Grant 61702394, Grant 61572385 and Grant 61711530248, in part by the Postdoctoral Science Foundation of China under Grant 2018T111021 and Grant 2017M613082, in part by the Science and Technology Project of Shaanxi Province under Grant 2016GY-033, in part by the Shaanxi Key Research and Development Program under Grant 2017ZDXM-GY-002, in part by the Aeronautical Science Foundation of China under Grant 20171981008, and in part by the Fundamental Research Funds for the Central Universities under Grant JBX170313, Grant XJS17063 and Grant JBF180301.

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  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Yumin Tian, Qiang Li, Di Wang & Bo Wan

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  1. Yumin Tian
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Correspondence to Di Wang.

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Tian, Y., Li, Q., Wang, D. et al. Robust joint learning network: improved deep representation learning for person re-identification. Multimed Tools Appl 78, 24187–24203 (2019). https://doi.org/10.1007/s11042-018-6998-x

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