• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0810010 (2022)
Fengsui Wang1、2、3、*, Furong Liu1、2、3, Jingang Chen1、2、3, and Qisheng Wang1、2、3
Author Affiliations
  • 1School of Electrical Engineering, Anhui Polytechnic University, Wuhu , Anhui 241000, China
  • 2Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Wuhu , Anhui 241000, China
  • 3Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Wuhu , Anhui 241000, China
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    DOI: 10.3788/LOP202259.0810010 Cite this Article Set citation alerts
    Fengsui Wang, Furong Liu, Jingang Chen, Qisheng Wang. Multi-Loss Joint Cross-Modality Person Re-Identification Method Integrating Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810010 Copy Citation Text show less
    Diagram of multi-loss joint cross-modality network structure of fused attention model
    Fig. 1. Diagram of multi-loss joint cross-modality network structure of fused attention model
    Feature extraction module
    Fig. 2. Feature extraction module
    Attention structure
    Fig. 3. Attention structure
    Feature constraint module
    Fig. 4. Feature constraint module
    Trends of loss and accuracy during training stage. (a) Loss; (b) accuracy
    Fig. 5. Trends of loss and accuracy during training stage. (a) Loss; (b) accuracy
    Trend of mAP in training stage
    Fig. 6. Trend of mAP in training stage
    Comparison of proposed method and other advanced methods for SYSU-MM01 all-search single mode
    Fig. 7. Comparison of proposed method and other advanced methods for SYSU-MM01 all-search single mode
    Comparison of proposed method and other advanced methods for SYSU-MM01 all-search multi mode
    Fig. 8. Comparison of proposed method and other advanced methods for SYSU-MM01 all-search multi mode
    BNECARank-1Rank-10Rank-20mAP
    ××79.1794.0897.5269.89
    ×79.8194.7197.7270.50
    ×84.4796.8098.1672.10
    85.6396.8498.5075.44
    Table 1. Experimental results of each module in RegDB dataset
    MethodSingle-hotMulti-hot
    Rank-1Rank-10Rank-20mAPRank-1Rank-10Rank-20mAP
    GSM189.4648.9872.0615.5711.3651.3473.419.03
    LOMO+GMA191.7917.9036.015.631.7118.1136.172.88
    LOMO+CDFE205.7534.3554.9010.197.3640.3860.335.64
    HoG-Euclidean213.2224.6844.527.524.7529.0649.383.51
    HoG-KISSME223.1125.4746.577.434.1029.3250.593.61
    HoG-LFDA232.4424.1345.506.873.4225.2745.113.19
    DPMBN(ResNet50)2444.4787.1295.2454.51----
    LOMO+CCA254.1130.6052.548.834.8634.4057.304.47
    Asymmetric FC2614.5957.9478.6820.3320.0969.3785.8013.04
    CmGAN(ResNet50)2731.3677.2389.1842.1937.0080.9492.1132.76
    One-stream516.9463.5582.1022.9522.6271.7487.8215.04
    Zero-padding520.5868.3885.7926.224.4375.8691.3218.64
    Two-stream615.6061.1881.0221.4922.4972.2288.6113.92
    BDTR(ResNet50)731.9277.1889.2841.86----
    eBDTR(ResNet50)832.4677.4289.6242.46----
    TSLFN959.7492.0796.2264.9169.7695.8598.9057.81
    HPILN(ResNet50)1445.7791.8298.4656.5253.0593.7198.9347.48
    AGW1554.17--62.97----
    JSIA1743.886.294.252.952.791.196.442.7
    Ours60.1996.7499.4168.1171.5697.2399.4160.29
    Table 2. Comparative experiment results between our method and others for SYSU-MM01 indoor-search mode
    Fengsui Wang, Furong Liu, Jingang Chen, Qisheng Wang. Multi-Loss Joint Cross-Modality Person Re-Identification Method Integrating Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810010
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