结合视觉文本匹配和图嵌入的可见光-红外行人重识别

张红颖; 樊世钰; 罗谦; 张涛

doi:10.11999/JEIT240318

结合视觉文本匹配和图嵌入的可见光-红外行人重识别

doi: 10.11999/JEIT240318

张红颖^1, ,,
樊世钰²,
罗谦³,
张涛³

1.
中国民航大学电子信息与自动化学院天津 300300
2.
中国民航大学计算机科学与技术学院天津 300300
3.
民航成都电子技术有限责任公司成都 610041

基金项目: 国家自然科学基金民航联合研究基金重点支持项目(U2133211)，中国民航大学研究生科研创新资助项目(2023YJSKC05005)

详细信息

作者简介:
张红颖：女，博士，教授，硕士生导师，研究方向为图像工程与计算机视觉

樊世钰：男，硕士生，研究方向为计算机视觉、行人重识别

罗谦：男，研究员，研究方向为民航大数据挖掘算法研究、民航大数据平台仿真建模

张涛：男，高级工程师，研究方向为智慧机场运行技术

通讯作者:
张红颖　carole_zhang0716@163.com

中图分类号: TN911.73; TP391.41
计量
- 文章访问数: 26
- HTML全文浏览量: 11
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-22
- 修回日期: 2024-06-24
- 网络出版日期: 2024-06-27

Combining Visual-Textual Matching and Graph Embedding for Visible-Infrared Person Re-identification

1.
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
2.
College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
3.
Civil Aviation Electronic Technology Co., Ltd., Chengdu 610041, China

Funds: Key Supported Project of the Civil Aviation Joint Research Fund of the National Natural Science Foundation of China (U2133211), Graduate Research Innovation Grant Program of Civil Aviation University of China (2023YJSKC05005)

摘要

摘要: 对于可见光-红外跨模态行人重识别(Re-ID)，大多数方法采用基于模态转换的策略，通过对抗网络生成图像，以此建立不同模态间的相互联系。然而这些方法往往不能有效降低模态间的差距，导致重识别性能不佳。针对此问题，该文提出一种基于视觉文本匹配和图嵌入的双阶段跨模态行人重识别方法。该方法通过上下文优化方案构建可学习文本模板，生成行人描述作为模态间的关联信息。具体而言，在第1阶段基于图片-文本对的预训练(CLIP)模型实现同一行人不同模态间的统一文本描述作为先验信息辅助降低模态差异。同时在第2阶段引入基于图嵌入的跨模态约束框架，设计模态间自适应损失函数，提升行人识别准确率。为了验证所提方法的有效性，在SYSU-MM01和RegDB数据集上进行了大量实验，其中SYSU-MM01数据集上的首次命中(Rank-1)和平均精度均值(mAP)分别达到64.2%, 60.2%。实验结果表明，该文所提方法能够提升可见光-红外跨模态行人重识别的准确率。
- 行人重识别 /
- 跨模态 /
- 图片-文本对的预训练模型 /
- 上下文优化 /
- 图嵌入
Abstract: For cross-modal person re-identification in visible-infrared images, methods using modality conversion and adversarial networks yield associative information between modalities. However, this approach falls short in effective feature recognition. Thus, a two-stage approach using visual-text matching and graph embedding for enhanced re-identification effectiveness is proposed in this paper. A context-optimized scheme is utilized by the method to construct learnable text templates that generate person descriptions as associative information between modalities. Specifically, in the first stage, unified text descriptions of the same person across different modalities are utilized as prior information, assisting in the reduction of modality differences, based on the Contrastive Language–Image Pre-training (CLIP) model. Meanwhile, in the second stage, a cross-modal constraint framework based on graph embedding is applied, and a modality-adaptive loss function is designed, aiming to improve person recognition accuracy. The method's efficacy has been confirmed through extensive experiments on the SYSU-MM01 and RegDB datasets, with a Rank-1 accuracy of 64.2% and mean Average Precision (mAP) of 60.2% on SYSU-MM01 being achieved, thereby demonstrating significant improvements in cross-modal person re-identification.
- Person re-IDentification(Re-ID) /
- Cross-modal /
- Contrastive Language–Image Pre-training(CLIP) model /
- Context optimization /
- Graph embedding

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图 1 第1阶段处理流程图

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图 2 第2阶段处理流程图

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图 3 基于CoOp的文本提示优化示意图

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图 4 模态间特征奖励与惩罚示意图

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图 5 识别的可视化结果

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图 6 不同编码器的可视化结果

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表 1 在SYSU-MM01的All-search模式下和其他方法对比实验结果(%)

方法	单镜头				多镜头
方法	Rank-1	Rank-10	Rank-20	mAP	Rank-1	Rank-10	Rank-20	mAP
Zero-padding^[13]	14.8	54.1	71.3	16.0	–	61.4	78.4	10.9
HCML^[15]	14.3	53.2	69.2	16.2	–	–	–	–
BDTR^[16]	27.3	67.0	81.7	27.3	–	–	–	–
eBDTR^[17]	27.8	67.3	81.3	28.4	–	–	–	–
Hi-CMD^[4]	34.9	77.6	–	35.9	–	–	–	–
DPMBN^[18]	37.0	79.5	89.9	40.3	–	–	–	–
LZM^[19]	45.0	89.0	–	45.9	–	–	–	–
AlignGAN^[8]	42.4	85.0	93.7	40.7	51.5	89.4	95.7	33.9
Xmodal^[20]	49.9	89.8	96.0	50.7	47.6	88.1	96.0	36.1
DDAG^[21]	54.8	90.4	95.8	53.0	–	–	–	–
SFANET^[22]	60.5	91.8	95.2	53.9	–	–	–	–
MID^[23]	60.3	92.9	96.7	59.4	–	–	–	–
CM-NAS^[24]	60.8	92.1	96.8	58.9	68.0	94.8	97.9	52.4
Baseline(AGW)^[12]	47.5	84.4	92.1	47.7	–	–	–	–
本文方法	64.2	92.5	96.1	60.2	71.0	90.0	94.0	52.4

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表 2 在RegDB数据集和其他方法对比实验结果(%)

方法	可见光图像查询红外图像				红外图像查询可见光图像
方法	Rank-1	Rank-10	Rank-20	mAP	Rank-1	Rank-10	Rank-20	mAP
Zero-padding^[13]	17.8	34.2	44.4	18.9	16.6	34.7	44.3	17.8
HCML^[15]	24.4	47.5	56.8	20.0	21.7	45.0	55.6	22.2
BDTR^[16]	33.6	58.6	67.4	32.8	32.9	58.5	68.4	32.0
eBDTR^[17]	34.6	59.0	68.7	33.46	34.2	58.7	68.6	32.5
AlignGAN^[8]	57.9	–	–	53.6	56.3	–	–	53.4
Xmodal^[20]	62.2	83.1	91.7	60.2	–	–	–	–
DDAG^[21]	69.3	86.2	91.5	63.5	68.1	85.2	90.3	61.8
SFANET^[22]	76.3	91.0	94.3	68.0	70.2	85.2	89.2	63.8
Baseline(AGW)^[12]	70.1	86.2	–	66.4	70.5	87.1	–	65.9
本文方法	73.0	88.1	94.4	67.7	72.8	87.1	90.1	66.2

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表 3 在SYSU-MM01的All-search模式单镜头设置下实验结果(%)

方法	Rank-1	Rank-5	Rank-10	mAP
Baseline	47.5	–	86.2	47.7
Baseline+CLIP	60.2	80.2	87.8	56.1
Baseline+CLIP+MAGE	62.9	82.7	90.1	58.6

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表 4 损失函数的选择对实验指标的影响(%)

$ {L_{{\text{i2t}}}} $	$ {L_{{\text{id}}}} $	$ {L_{{\text{tri}}}} $	$ {L_{{\text{MAGE}}}} $	Rank-1	Rank-5	Rank-10	mAP
√	√		√	51.6	75.8	83	42.9
√		√	√	60.5	83.4	90.6	56.7
√			√	4.4	12.3	22.2	5.3
	√	√		62.8	85.3	91.7	59.2
√	√	√	√	64.2	86.5	92.5	60.2

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表 5 不同图像编码器下采用单阶段和双阶段对实验指标的影响(%)

		Rank-1	Rank-5	Rank-10	mAP
AGW^[12]	单阶段双阶段	61.3 64.2	84.5 86.5	91.2 92.5	57.6 60.2
ViT-B/16^[25]	单阶段双阶段	62.5 63.0	79.7 81.4	88.7 91.3	58.3 60.6

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表 6 参数$ \alpha $在不同取值下的实验结果(%)

$ \alpha $	SYSU-MM01
$ \alpha $	Rank-1	Rank-5	Rank-10	mAP
0.5	56.0	80.4	88.6	54.8
0.05	62.3	83.1	89.6	58.5
0.005	62.9	87.2	90.3	58.6
0.0005	59.6	83.1	90.1	56.9

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