CASIA OpenIR  > 智能感知与计算
Deep Graph Metric Learning for Weakly Supervised Person Re-Identification
Meng, Jingke1,2; Zheng, Wei-Shi3,4; Lai, Jian-Huang1; Wang, Liang5
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2022-10-01
卷号44期号:10页码:6074-6093
通讯作者Zheng, Wei-Shi(wszheng@ieee.org)
摘要In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples that are manually labeled from raw surveillance video (likely with the assistance of detection) in a frame-by-frame manner. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time consuming. To overcome these issues, we consider a weakly supervised person re-id modeling that aims to find the raw video clips where a given target person appears. In our weakly supervised setting, during training, given a sample of a person captured in one camera view, our weakly supervised approach aims to train a re-id model without further instance-level labeling for this person in another camera view. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. The weakly supervised person re-id is challenging since it not only suffers from the difficulties occurring in conventional person re-id (e.g., visual ambiguity and appearance variations caused by occlusions, pose variations, background clutter, etc.), but more importantly, is also challenged by weakly supervised information because the instance-level labels and the ground-truth locations for person instances (i.e., the ground-truth bounding boxes of person instances) are absent. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames, where the spatial graph captures neighborhood relationship about the detected person instances in each frame. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR), which utilizes multiple weak video-level labels to learn discriminative features by means of a weak identity loss and a cross-video alignment loss. We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g., its bag-to-bag extension) and show that the proposed DGML is effective.
关键词Training Cameras Labeling Probes Visualization Annotations Loss measurement Person re-identification weakly supervised person re-identification visual surveillance
DOI10.1109/TPAMI.2021.3084613
收录类别SCI
语种英语
资助项目National Key Research andDevelopment Program of China[2016YFB1001002] ; NSFC[U1911401] ; NSFC[U1811461] ; NSFC[U1803120] ; Guangdong NSF Project[2020B1515120085] ; Guangdong NSF Project[2018B030312002] ; Research Projects of Zhejiang Lab[2019KD0AB03] ; Key-Area Research and Development Program of Guangzhou[202007030004] ; Guangzhou Research Project[201902010037]
项目资助者National Key Research andDevelopment Program of China ; NSFC ; Guangdong NSF Project ; Research Projects of Zhejiang Lab ; Key-Area Research and Development Program of Guangzhou ; Guangzhou Research Project
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000853875300019
出版者IEEE COMPUTER SOC
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50120
专题智能感知与计算
通讯作者Zheng, Wei-Shi
作者单位1.Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 519082, Peoples R China
2.Pazhou Lab, Guangzhou 519082, Peoples R China
3.Sun Yat Sen Univ, Sch Comp Sci & Engn, Key Lab Machine Intelligence & Adv Comp, Minist Educ, Guangzhou 519082, Peoples R China
4.Peng Cheng Lab, Shenzhen 518066, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Meng, Jingke,Zheng, Wei-Shi,Lai, Jian-Huang,et al. Deep Graph Metric Learning for Weakly Supervised Person Re-Identification[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(10):6074-6093.
APA Meng, Jingke,Zheng, Wei-Shi,Lai, Jian-Huang,&Wang, Liang.(2022).Deep Graph Metric Learning for Weakly Supervised Person Re-Identification.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(10),6074-6093.
MLA Meng, Jingke,et al."Deep Graph Metric Learning for Weakly Supervised Person Re-Identification".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.10(2022):6074-6093.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Meng, Jingke]的文章
[Zheng, Wei-Shi]的文章
[Lai, Jian-Huang]的文章
百度学术
百度学术中相似的文章
[Meng, Jingke]的文章
[Zheng, Wei-Shi]的文章
[Lai, Jian-Huang]的文章
必应学术
必应学术中相似的文章
[Meng, Jingke]的文章
[Zheng, Wei-Shi]的文章
[Lai, Jian-Huang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。