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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 20–36Cite as

An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12373))

Abstract

The progressive clustering method and its variants, which iteratively generate pseudo labels for unlabeled data and per form feature learning, have shown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, that is, pedestrian features extracted from the same camera tend to be clustered into the same class. This often results in a non-convergent model in the real world application of clustering based re-id models, leading to degenerated performance. In the present study, we propose an attention-driven two-stage clustering (ADTC) method to solve this problem. Specifically, our method consists of two strategies. Firstly, we use an unsupervised attention kernel to shift the learned features from the image background to the pedestrian foreground, which results in more informative clusters. Secondly, to aid the learning of the attention driven clustering model, we separate the clustering process into two stages. We first use kmeans to generate the centroids of clusters (stage 1) and then apply the k-reciprocal Jaccard distance (KRJD) metric to re-assign data points to each cluster (stage 2). By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative. Using two benchmark datasets Market1501 and DukeMTMC, we demonstrate that our model outperforms other state-of-the-art unsupervised approaches for person re-id.

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Notes

  1. 1.

    The term of voxel attention comes from that it is a 3D attention mask combining the spatial and channel attentions.

  2. 2.

    Note that NMI is independent of the absolute values of labels, in term of that a permutation of cluster labels does not change its value.

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Acknowledgments

ZLJ designed the study and carried out the experiments. XLZ, XHL and XL helped with integrating algorithms and conducting experiments. TJH and SW contributed to the conception and design of the study and revision. ZLJ and SW wrote the manuscript. This work was supported by Huawei Technology Co., Ltd. (YBN2019105137) and Guangdong Province with grant (No. 2018B030338001, SW). This work was also supported by BMSTC(Beijing municipal science and technology commission) with grant (No. Z161100000216143, SW) and the National Natural Science Foundation of China (No. 61425025, T.J. Huang).

Author information

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Zilong Ji

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Xiaolong Zou, Xiaohan Lin, Tiejun Huang & Si Wu

  3. IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Xiao Liu & Si Wu

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  1. Zilong Ji
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  2. Xiaolong Zou
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  3. Xiaohan Lin
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  4. Xiao Liu
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  5. Tiejun Huang
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  6. Si Wu
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Corresponding author

Correspondence to Si Wu .

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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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Ji, Z., Zou, X., Lin, X., Liu, X., Huang, T., Wu, S. (2020). An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12373. Springer, Cham. https://doi.org/10.1007/978-3-030-58604-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-58604-1_2

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