Combined Depth Space based Architecture Search For Person Re-identification

@article{Li2021CombinedDS,
  title={Combined Depth Space based Architecture Search For Person Re-identification},
  author={Hanjun Li and Gaojie Wu and Weishi Zheng},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={6725-6734}
}
Most works on person re-identification (ReID) take advantage of large backbone networks such as ResNet, which are designed for image classification instead of ReID, for feature extraction. However, these backbones may not be computationally efficient or the most suitable architectures for ReID. In this work, we aim to design a lightweight and suitable network for ReID. We propose a novel search space called Combined Depth Space (CDS), based on which we search for an efficient network… 

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