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12. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale
image recognition. In: International conference on learning representations, pp 135–
153
13. Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation (Online). arXiv:1708.04896
14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition.
In: IEEE conference on computer vision and pattern recognition, pp 770–778
15. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person reidentification: a benchmark. In: IEEE international conference on computer vision,
pp 1116–1124
16. Felzenszwalb PF, Girshick RB, Mcallester DA, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal
Mach Intell 32:1627–1645
17. Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera
person re-identification. In: European conference on computer vision, pp 475–491
18. Varior RR, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision, pp 791–808
19. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present
and future (Online). arXiv:1610.02984
20. Zhao L, Li X, Wang J, Zhuang Y (2017) Deeply-learned part-aligned representations for person re-identification. In: IEEE conference on computer vision and
pattern recognition, pp 3219–3228
21. Sun Y, Zheng L, Deng W, Wang S (2017) SVDNet for pedestrian retrieval. In:
IEEE international conference on computer vision, pp 3820–3828
22. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve
the person re-identification baseline in vitro. In: IEEE international conference on
computer vision, pp 3754–3762
23. Lin Y, Zheng L, Zheng Z, Wu Y, Yang Y (2017) Improving person re-identification
by attribute and identity learning (Online). arXiv:1703.07220
24. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person reidentification (Online). arXiv:1703.07737