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基于多标签协同学习的跨域行人重识别

李慧 张晓伟 赵新鹏 路昕雨

李慧, 张晓伟, 赵新鹏, 等 . 基于多标签协同学习的跨域行人重识别[J]. 北京航空航天大学学报, 2022, 48(8): 1534-1542. doi: 10.13700/j.bh.1001-5965.2021.0600
引用本文: 李慧, 张晓伟, 赵新鹏, 等 . 基于多标签协同学习的跨域行人重识别[J]. 北京航空航天大学学报, 2022, 48(8): 1534-1542. doi: 10.13700/j.bh.1001-5965.2021.0600
LI Hui, ZHANG Xiaowei, ZHAO Xinpeng, et al. Multi-label cooperative learning for cross domain person re-identification[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1534-1542. doi: 10.13700/j.bh.1001-5965.2021.0600(in Chinese)
Citation: LI Hui, ZHANG Xiaowei, ZHAO Xinpeng, et al. Multi-label cooperative learning for cross domain person re-identification[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1534-1542. doi: 10.13700/j.bh.1001-5965.2021.0600(in Chinese)

基于多标签协同学习的跨域行人重识别

doi: 10.13700/j.bh.1001-5965.2021.0600
基金项目: 

国家自然科学基金 61902204

山东省自然科学基金 ZR2019BF028

详细信息
    通讯作者:

    张晓伟, E-mail: by1306114@buaa.edu.cn

  • 中图分类号: TP37; TP277

Multi-label cooperative learning for cross domain person re-identification

Funds: 

National Natural Science Foundation of China 61902204

Shandong Provincial Natural Science Foundation ZR2019BF028

More Information
  • 摘要:

    跨域是行人重识别的重要应用场景,但是源域与目标域行人图像在光照条件、拍摄视角、成像背景与风格等方面的表观特征差异性是导致行人重识别模型泛化能力下降的关键因素。针对该问题,提出了基于多标签协同学习的跨域行人重识别方法。利用语义解析模型构造了基于语义对齐的多标签数据表示,以引导构建更关注行人前景区域的局部特征,达到语义对齐的目的,减少背景对跨域重识别的影响。基于行人图像全局特征和语义对齐后的行人局部特征,利用协同学习平均模型生成行人重识别模型的多标签表示,减少跨域场景下噪声硬标签的干扰。利用协同学习网络框架联合多标签的语义对齐模型,提高行人重识别模型的识别能力。实验结果表明:在Market-1501→ DukeMTMC-reID、DukeMTMC-reID→Market-1501、Market-1501→MSMT17、DukeMTMC-reID→MSMT17跨域行人重识别数据集上,与NRMT方法相比,平均精度均值分别提高了8.3%、8.9%、7.6%、7.9%,多标签协同学习方法具有显著的优越性。

     

  • 图 1  多标签协同学习跨域行人重识别框架

    Figure 1.  Framework of cross-domain person re-identification on multi-label collaborative learning

    图 2  硬划分与语义对齐对比效果

    Figure 2.  Comparison effect of hard division and semantic alignment

    图 3  全局硬标签与前景硬标签示意图

    Figure 3.  Schematic diagram of global hard label and foreground hard label

    图 4  全局软标签与前景软标签示意图

    Figure 4.  Schematic diagram of global soft label and foreground soft label

    表  1  在DukeMTMC-reID和Market-1501数据集上的语义对齐模块有效性消融实验

    Table  1.   Ablation study for semantic alignment module validity on DukeMTMC-reID dataset and Market-1501 dataset

    方法 Market-1501→DukeMTMC-reID DukeMTMC-reID→Market-1501
    R-1/% R-5/% R-10/% mAP/% R-1/% R-5/% R-10/% mAP/%
    GFM 68.4 80.1 83.5 49.0 75.8 89.5 93.2 53.7
    HPM 76.0 85.8 89.3 60.3 86.2 94.6 96.5 68.7
    SPM 76.7 85.9 89.0 60.9 87.9 95.2 96.8 70.9
    注:黑体数据为每列最优值。
    下载: 导出CSV

    表  2  在DukeMTMC-reID和Market-1501数据集上的多标签有效性消融实验

    Table  2.   Ablation study of multi labels validity on DukeMTMC-reID dataset and Market-1501 dataset

    方法 Market-1501→DukeMTMC-reID DukeMTMC-reID→Market-1501
    R-1/% R-5/% R-10/% mAP/% R-1/% R-5/% R-10/% mAP/%
    GSL 78.0 88.8 92.5 65.1 87.7 94.9 96.9 71.2
    FSL 82.4 91.1 93.4 69.0 91.7 96.5 97.7 77.8
    MCL(GSL+FSL) 82.5 91.1 93.2 70.5 93.2 97.1 98.1 80.6
    注:黑体数据为每列最优值。
    下载: 导出CSV

    表  3  在DukeMTMC-reID数据集上不同方法的比较

    Table  3.   Comparison with different methods on DukeMTMC-reID dataset

    方法 Market-1501→DukeMTMC-reID
    R-1/% R-5/% R-10/% mAP/%
    ECN[28] 63.3 75.8 80.4 40.4
    D-MMD[29] 63.5 78.8 83.9 46.0
    AD-Cluster[30] 72.6 82.5 85.5 54.1
    SSG[21] 76.0 85.8 89.3 60.3
    DG-Net++[31] 78.9 87.8 90.4 63.8
    JVTC+[32] 80.4 89.9 92.2 66.5
    MPLP+MMCL[11] 72.4 82.9 85.0 51.4
    NRMT[33] 77.8 86.9 89.5 62.2
    MEB[34] 79.6 88.3 92.2 66.1
    MCL(本文) 82.5 91.1 93.2 70.5
    注:黑体数据为每列最优值。
    下载: 导出CSV

    表  4  在Market-1501数据集上不同方法的实验比较

    Table  4.   Comparison with different methods on Market-1501 dataset

    方法 DukeMTMC-reID→Market-1501
    R-1/% R-5/% R-10/% mAP/%
    ECN[28] 75.1 87.6 91.6 43.0
    D-MMD[29] 70.6 87.0 91.5 48.8
    AD-Cluster[30] 86.7 94.4 96.5 68.3
    SSG[21] 86.2 94.6 96.5 68.7
    DG-Net++[31] 82.1 90.2 92.7 61.7
    JVTC+[32] 86.8 95.2 97.1 67.2
    MPLP+MMCL[11] 84.4 92.8 95.0 60.4
    NRMT[33] 87.8 94.6 96.5 71.7
    MEB[34] 89.9 96.0 97.5 76.0
    MCL(本文) 93.2 97.1 98.1 80.6
    注:黑体数据为每列最优值。
    下载: 导出CSV

    表  5  在MSMT17数据集上不同方法的实验比较

    Table  5.   Comparison with different methods on MSMT17 dataset

    方法 Market-1501→MSMT17 DukeMTMC-reID→MSMT17
    R-1/% R-5/% R-10/% mAP/% R-1/% R-5/% R-10/% mAP/%
    ECN[28] 25.3 36.3 42.1 8.5 30.2 41.5 46.8 10.2
    D-MMD[29] 29.1 46.3 54.1 13.5 34.4 51.1 58.5 15.3
    MPLP+MMCL[11] 40.8 51.8 56.7 15.1 43.6 54.3 58.9 16.2
    NRMT[33] 43.7 56.5 62.2 19.8 45.2 57.8 63.3 20.6
    DG-Net++[31] 48.4 60.9 66.1 22.1 48.8 60.9 65.9 22.1
    MCL(本文) 57.3 68.5 73.3 27.4 58.5 70.0 74.5 28.5
    注:黑体数据为每列最优值。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-10
  • 录用日期:  2021-10-29
  • 刊出日期:  2021-11-12

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