基于可解释注意力部件模型的行人重识别方法

周勇; 王瀚正; 赵佳琦; 陈莹; 姚睿; 陈思霖

doi:10.16383/j.aas.c200493

基于可解释注意力部件模型的行人重识别方法

doi: 10.16383/j.aas.c200493

周勇^{1, 2},
王瀚正^{1, 2,},
赵佳琦^{1, 2,},
陈莹^{1, 2,},
姚睿^{1, 2,},
陈思霖^{1, 2,}

1.
中国矿业大学计算机科学与技术学院徐州 221116
2.
矿山数字化教育部工程研究中心徐州 221116

基金项目: 国家自然科学基金(61806206, U1610124, 61772530, 61773383), 江苏省自然科学基金(BK20180639, BK20171192), 江苏省六大人才高峰计划(2015-DZXX-010)资助

详细信息

作者简介:
周勇：中国矿业大学计算机科学与技术学院教授. 主要研究方向为数据挖掘, 机器学习和人工智能. E-mail: yzhou@cumt.edu.cn

王瀚正：中国矿业大学计算机科学与技术学院硕士研究生. 主要研究方向为计算机视觉, 图像处理, 行人重识别. E-mail: hzwang@cumt.edu.cn

赵佳琦：中国矿业大学计算机科学与技术学院副教授. 主要研究方向为多目标优化, 深度学习, 图像处理. 本文通信作者. E-mail: jiaqizhao88@126.com

陈莹：中国矿业大学计算机科学与技术学院博士研究生. 主要研究方向为计算机视觉, 图像处理, 行人重识别. E-mail: cheny@cumt.edu.cn

姚睿：中国矿业大学计算机科学与技术学院副教授. 主要研究方向为计算机视觉, 机器学习. E-mail: ruiyao@cumt.edu.cn

陈思霖：中国矿业大学计算机科学与技术学院硕士研究生. 主要研究方向为计算机视觉, 图像处理, 目标检测. E-mail: silin.chen@cumt.edu.cn

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出版历程
- 收稿日期: 2020-07-06
- 修回日期: 2020-08-23
- 网络出版日期: 2023-08-29
- 刊出日期: 2023-10-24

Interpretable Attention Part Model for Person Re-identification

ZHOU Yong^{1, 2},
WANG Han-Zheng^{1, 2
,},
ZHAO Jia-Qi^{1, 2
,},
CHEN Ying^{1, 2
,},
YAO Rui^{1, 2
,},
CHEN Si-Lin^{1, 2
,}

1.
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116
2.
Engineering Research Center of Mine Digitization of Ministry of Education, Xuzhou 221116

Funds: Supported by National Natural Science Foundation of China (61806206, U1610124, 61772530, 61773383), Natural Science Foundation of Jiangsu Province (BK20180639, BK20171192), and the Six Talent Peaks Project in Jiangsu Province (2015-DZXX-010)

More Information

Author Bio:
ZHOU Yong　Professor at the School of Computer Science and Technology, China University of Mining and Technology. His research interest covers data mining, machine learning, and artificial intelligence

WANG Han-Zheng　Master student at the School of Computer Science and Technology, China University of Mining and Technology. His research interest covers computer vision, image processing, and person re-identification

ZHAO Jia-Qi　Associate professor at the School of Computer Science and Technology, China University of Mining and Technology. His research interest covers multiobjective optimization, deep learning, and image processing. Corresponding author of this paper

CHEN Ying　Ph.D. candidate at the School of Computer Science and Technology, China University of Mining and Technology. Her research interest covers computer vision, image processing, and person re-identification

YAO Rui　Associate professor at the School of Computer Science and Technology, China University of Mining and Technology. His research interest covers computer vision and machine learning

CHEN Si-Lin　Master student at the School of Computer Science and Technology, China University of Mining and Technology. His research interest covers computer vision, image processing, and objective detection

摘要

摘要: 大多数行人重识别(Person re-identification, ReID)方法仅将注意力机制作为提取显著特征的辅助手段, 缺少网络对行人图像关注程度的量化研究. 基于此, 提出一种可解释注意力部件模型(Interpretable attention part model, IAPM). 该模型有3 个优点: 1)利用注意力掩码提取部件特征, 解决部件不对齐问题; 2)为了根据部件的显著性程度生成可解释权重, 设计可解释权重生成模块(Interpretable weight generation module, IWM); 3)提出显著部件三元损失(Salient part triplet loss, SPTL)用于IWM的训练, 提高识别精度和可解释性. 在3 个主流数据集上进行实验, 验证所提出的方法优于现有行人重识别方法. 最后通过一项人群主观测评比较IWM生成可解释权重的相对大小与人类直观判断得分, 证明本方法具有良好的可解释性.
- 行人重识别 /
- 注意力机制 /
- 可解释深度学习 /
- 部件模型
Abstract: Most person re-identification (ReID) methods only use the attention mechanism as an auxiliary method to extract salient features, and lack of quantitative research on the attention degree of person images on the network. Based on this, this paper proposes an interpretable attention part model (IAPM). The model has three advantages: 1) Using the attention mask to extract component features for solving the problem of component misalignment; 2) To generate interpretable weights based on the significance of the components, we devise the interpretable weight generation module (IWM); 3) Salient part triple loss (SPTL) for IWM is proposed to further improve recognition accuracy and interpretability. A series of experiments are carried out on three mainstream datasets, and demonstrate that our method is superior to the state-of-the-art methods. Finally, a crowd subjective test is used to compare the relative size of the interpretable weights generated by IWM and human intuitive judgment scores, which proves that the method has good interpretability.
- Person re-identification (ReID) /
- attention mechanism /
- interpretable deep learning /
- part model

HTML全文

图 1 IAPM整体结构

Fig. 1 Structure of IAPM

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图 2 横向分割示意图

Fig. 2 Schematic diagram of horizontal split

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图 3 PS模块使用的伪标签^[16]

Fig. 3 Pseudo-labels used by PS^[16]

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图 4 注意力权重生成模块结构

Fig. 4 Structure of IWM

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图 5 负样本对距离变化图

Fig. 5 Negative sample pair distance graph

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图 6 正样本对距离变化图

Fig. 6 Positive sample pair distance graph

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图 7 SPTL损失曲线图

Fig. 7 SPTL loss curve graph

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图 8 可解释权重展示

Fig. 8 The display of interpretable weights

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图 9 主观测评结果

Fig. 9 The display of subjective evaluation results

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图 10 可解释权重与主观测评结果对比

Fig. 10 Comparison of interpretable weights and subjective evaluation results

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表 1 实验环境

Table 1 Experimental environment

软硬件环境	配置
实验平台	Pytorch
显卡	NVIDIA Tesla P100
内存	40 GB
显存	16 GB

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表 2 实验参数

Table 2 Experimental parameters

实验参数	参数数值
输入图像尺寸(像素)	$384\times 128 $
迭代次数	100
优化器	SGD
动量因子	0.9
权重衰减系数	$5\times10^{-4} $
Batchsize	128
显著部件三元损失$\alpha $	1.2

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表 3 与EANet的性能对比(%)

Table 3 Performance comparison with EANet (%)

方法	数据集
方法	Market-1501	DukeMTMC-reID	CUHK03
PAP-6P	94.3 (84.3)	85.6 (72.4)	68.1 (62.4)
PAP	94.5 (84.9)	86.1 (73.3)	72.0 (66.2)
PAP-S-PS	94.6 (85.6)	87.5 (74.6)	72.5 (66.8)
IAPM-6P (本文)	95.0 (85.3)	86.9 (74.3)	72.5 (65.2)
IAPM-9P (本文)	95.1 (86.0)	87.9 (75.6)	72.6 (67.4)
IAPM (本文)	95.2 (86.3)	88.0 (75.7)	72.6 (67.2)

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表 4 与其他方法的性能对比 (%)

Table 4 Performance comparison with other methods (%)

方法	数据集
方法	Market-1501	DukeMTMC-reID	CUHK03
Verif-Identify^[38]	79.5 (59.9)	68.9 (49.3)	—
MSCAN^[29]	80.8 (57.5)	—	—
MGCAM^[12]	83.8 (74.3)	—	50.1 (50.2)
Part-Aligned^[39]	91.7 (79.6)	84.4 (69.3)	—
SPReID^[40]	92.5 (81.3)	84.4 (71.0)	—
AlignedReID^[41]	91.8 (79.3)	—	—
Deep-Person^[42]	92.3 (79.6)	80.9 (64.8)	—
PCB^[7]	85.3 (68.5)	73.2 (52.8)	43.8 (38.9)
PCB + RPP^[7]	93.8 (81.6)	83.3 (69.2)	63.7 (57.5)
HA-CNN^[43]	91.2 (75.7)	80.5 (63.8)	44.4 (41.0)
Mancs^[44]	93.1 (82.3)	84.9 (71.8)	69.0 (63.9)
P²-Net ^[45]	95.1 (85.6)	86.5 (73.1)	74.9 (68.9)
M³ + ResNet50^[46]	95.4 (82.6)	84.7 (68.5)	66.9 (60.7)
IAPM (本文)	95.2 (86.3)	88.0 (75.7)	72.6 (67.2)
注: “—” 表示文献中没有提供相应数据.

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表 5 消融实验1

Table 5 Ablation experiment 1

模型	Rank-1 (%)	mAP (%)
原始模型	92.4	80.5
原始模型 + IWM + SPTL	95.0	86.1
原始模型 + IWM + SPTL + 中心损失	95.2	86.3
注: 加粗字体表示各列最优结果.

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表 6 消融实验2

Table 6 Ablation experiment 2

人体部件个数	Rank-1 (%)	mAP (%)
6	95.0	85.3
7	95.2	86.3
9	95.1	86.0
注: 加粗字体表示各列最优结果.

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表 7 消融实验3

Table 7 Ablation experiment 3

$\alpha $	Rank-1 (%)	mAP (%)
0.1	94.4	85.2
0.5	94.5	85.3
0.8	94.8	85.7
1.0	94.7	85.6
1.2	95.2	86.3
1.5	94.6	85.6
2.0	94.7	85.3
5.0	93.5	83.5
10.0	93.3	81.0
注: 加粗字体表示各列最优结果.

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表 8 消融实验4

Table 8 Ablation experiment 4

$\lambda $	Rank-1 (%)	mAP (%)
0.2	94.4	85.4
0.4	94.8	85.4
0.6	94.4	85.1
0.8	94.8	85.7
1.0	95.2	86.3
注: 加粗字体表示各列最优结果.

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