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Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lifting

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.

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Notes

  1. 1.

    https://cysu.github.io/open-reid/.

  2. 2.

    We have tried different hyper parameters and reported the best results. The best margin values were found to be 0.5 on Market-1501 and 0.2 on DukeMTMC-reID.

  3. 3.

    Note that TFusion parameters were optimized on each dataset to get the best results, but for TLift we used fixed parameters for all datasets (see Appendix for analysis).

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Acknowledgements

This work was partly supported by the NSFC Project #61672521. The authors would like to thank Yanan Wang who helped producing several illustration figures in this paper, Jinchuan Xiao who optimized the TLift code, and Anna Hennig who helped proofreading the paper.

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Authors and Affiliations

  1. Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE

    Shengcai Liao & Ling Shao

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Ling Shao

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  1. Shengcai Liao
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  2. Ling Shao
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Correspondence to Shengcai Liao .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Liao, S., Shao, L. (2020). Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lifting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_27

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