Deep joint discriminative learning for vehicle re-identification and retrieval

@article{Li2017DeepJD,
  title={Deep joint discriminative learning for vehicle re-identification and retrieval},
  author={Yuqi Li and Yanghao Li and Hongfei Yan and Jiaying Liu},
  journal={2017 IEEE International Conference on Image Processing (ICIP)},
  year={2017},
  pages={395-399}
}
In this paper, we propose a novel vehicle re-identification method based on a Deep Joint Discriminative Learning (DJDL) model, which utilizes a deep convolutional network to effectively extract discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, we design a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks. The whole network is… CONTINUE READING

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Hardaware deeply cascaded embedding

  • Yuhui Yuan, Kuiyuan Yang, Chao Zhang
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