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Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification

文献类型：期刊论文

作者Zhang, La4; Guo, Haiyun3; Zhu, Kuan3; Qiao, Honglin2; Huang, Gaopan5; Zhang, Sen1; Zhang, Huichen1; Sun, Jian4; Wang, Jinqiao3
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2022-02-01
卷号18期号:1页码:15
关键词Visible-infrared person re-identification cross-modality metric learning
ISSN号1551-6857
DOI10.1145/3473341
通讯作者Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn)
英文摘要Visible-infrared person re-identification (Re-ID) has received increasing research attention for its great practical value in night-time surveillance scenarios. Due to the large variations in person pose, viewpoint, and occlusion in the same modality, as well as the domain gap brought by heterogeneous modality, this hybrid modality person matching task is quite challenging. Different from the metric learning methods for visible person re-ID, which only pose similarity constraints on class level, an efficient metric learning approach for visible-infrared person Re-ID should take both the class-level and modality-level similarity constraints into full consideration to learn sufficiently discriminative and robust features. In this article, the hybrid modality is divided into two types, within modality and cross modality. We first fully explore the variations that hinder the ranking results of visible-infrared person re-ID and roughly summarize them into three types: within-modality variation, cross-modality modality-related variation, and cross-modality modality-unrelated variation. Then, we propose a comprehensive metric learning framework based on four kinds of paired-based similarity constraints to address all the variations within and cross modality. This framework focuses on both class-level and modality-level similarity relationships between person images. Furthermore, we demonstrate the compatibility of our framework with any paired-based loss functions by giving detailed implementation of combing it with triplet loss and contrastive loss separately. Finally, extensive experiments of our approach on SYSIJ-MM01 and RegDB demonstrate the effectiveness and superiority of our proposed metric learning framework for visible-infrared person Re-ID.
资助项目Key-Area Research and Development Program of Guangdong Province[2020B010165001] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[62002356] ; National Natural Science Foundation of China[61925303] ; Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety[2020ZDSYSKFKT04]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000772639300002
出版者ASSOC COMPUTING MACHINERY
资助机构Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety
源URL[http://ir.ia.ac.cn/handle/173211/48180]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者Guo, Haiyun
作者单位1.Minist Publ Secur, Traff Management Res Inst, 88 Qianrong Rd, Wuxi, Jiangsu, Peoples R China
2.Alibaba Cloud, Radiance JinHui Tower,Bldg 6,4th Dist, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing, Peoples R China
4.Beijing Inst Technol, 5 South St, Beijing 100081, Peoples R China
5.Alibaba Cloud, Ali Ctr, Nanjing, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, La,Guo, Haiyun,Zhu, Kuan,et al. Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2022,18(1):15.
APA Zhang, La.,Guo, Haiyun.,Zhu, Kuan.,Qiao, Honglin.,Huang, Gaopan.,...&Wang, Jinqiao.(2022).Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,18(1),15.
MLA Zhang, La,et al."Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18.1(2022):15.

入库方式： OAI收割

来源：自动化研究所

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