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Style Transfer with Adversarial Learning for Cross-Dataset Person Re-identification

  • Furong Xu
  • Bingpeng MaEmail author
  • Hong Chang
  • Shiguang Shan
  • Xilin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

Person re-identification (ReID) has witnessed great progress in recent years. Existing approaches are able to achieve significant performance on the single dataset but fail to generalize well on another datasets. The emerging problem mainly comes from style difference between two datasets. To address this problem, we propose a novel style transfer framework based on Generative Adversarial Networks (GAN) to generate target-style images. Specifically, we get the style representation by calculating the Garm matrix of the three-channel original image, and then minimize the Euclidean distance of the style representation on different images transferred by the same generator while image generation. Finally, the labeled source dataset combined with the style-transferred images are all used to enhance the generalization ability of the ReID model. Experimental results suggest that the proposed strategy is very effective on the Market-1501 and DukeMTMC-reID.

Keywords

Person re-identification Style transfer Adversarial learning 

Notes

Acknowledgment

This work is supported in part by National Basic Research Program of China (973 Program): 2015CB351802, and Natural Science Foundation of China (NSFC): 61390501, 61876171 and 61572465.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Furong Xu
    • 1
  • Bingpeng Ma
    • 1
    Email author
  • Hong Chang
    • 2
  • Shiguang Shan
    • 2
  • Xilin Chen
    • 2
  1. 1.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina

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