| Deep Graph Metric Learning for Weakly Supervised Person Re-Identification | |
Meng, Jingke1,2; Zheng, Wei-Shi3,4; Lai, Jian-Huang1; Wang, Liang5
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| Source Publication | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| ISSN | 0162-8828 |
| 2022-10-01 | |
| Volume | 44Issue:10Pages:6074-6093 |
| Corresponding Author | Zheng, Wei-Shi(wszheng@ieee.org) |
| Abstract | 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. |
| Keyword | Training Cameras Labeling Probes Visualization Annotations Loss measurement Person re-identification weakly supervised person re-identification visual surveillance |
| DOI | 10.1109/TPAMI.2021.3084613 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000853875300019 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50120 |
| Collection | 智能感知与计算 |
| Corresponding Author | Zheng, Wei-Shi |
| Affiliation | 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 |
| Recommended Citation 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. |
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