Publication Type
Journal Article
Version
submittedVersion
Publication Date
2-2023
Abstract
Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier (NLC) and Global Logits Revise (GLR). The NLC is designed to obtain a score vector of each local region on feature maps in a non-parametric manner. The GLR module directly revises the logits such that the subsequent cross entropy loss up-weights the loss assigned to samples with hard-to-learn local regions. The convergence of the deep model indicates more local regions (the number of local regions is manually defined) on the feature maps of each sample are discriminative. We implement these two modules on two strong baseline methods including the BagTricks (BOT) [1] and AGW [2]. The network achieves 65.9% mAP, 85.1% rank1 on MSMT17, 86.4% mAP, 87.4% rank1 on CUHK03 labeled, 84.2% mAP, 85.9% rank1 on CUHK03 detected, and 92.2% mAP, 96.3% rank1 on Market-1501. The results show that the proposed baseline achieves a new state-of-the-art when using only global embedding during inference without any re-ranking technique.
Keywords
person re-identification, baseline, comprehensive
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Pattern Recognition
Volume
134
First Page
1
Last Page
35
ISSN
0031-3203
Identifier
10.1016/j.patcog.2022.109068
Publisher
Elsevier
Citation
XIA, Jiali; HUANG, Jianqiang; ZHENG, Shibao; ZHOU, Qin; SCHIELE, Bernt; HUA, Xian-Sheng; and SUN, Qianru.
Learning comprehensive global features in person re-identification: Ensuring discriminativeness of more local regions. (2023). Pattern Recognition. 134, 1-35.
Available at: https://ink.library.smu.edu.sg/sis_research/7555
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.patcog.2022.109068
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons