NDLI: Mask-guided class activation mapping network for person re-identification

Content Provider	IET Digital Library
Author	Lian, Sicheng Hu, Haifeng
Abstract	In this Letter, the authors propose a novel mask-guided class activation mapping (MCAM) network for person re-identification, which learns background-invariant and view-invariant features. Specifically, a novel loss function named mask-guided mapping loss is meticulously formulated to utilise the human binary masks, which contain helpful body shape information as the reference standard, thereby guiding the model to place more emphasis on human body regions. Moreover, they propose a new weighted channel attention (WCA) module, which replaces the global average pooling with a global depthwise convolution layer. By virtue of this particular WCA module, the feature information distributed across the spatial space can be individually weighted and dynamically compressed into a more precise channel attention map. Extensive experiments have been carried out on three widely-used re-identification data sets. Compared with the baseline model, MCAM has gained rank-1 accuracy improvement of 2.0% on Market-1501, 6.0% on DukeMTMC-reID, and 7.5% on CUHK03-NP, confirming its effectiveness.
Starting Page	1416
Ending Page	1418
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1643
Journal	Electronics Letters
Publisher Date	2020-11-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background-invariant Features Body Shape Information Channel Attention Map Computer Vision And Image Processing Technique CUHK03-NP DukeMTMC-reID Feature Extraction Feature Information Global Average Pooling Global Depthwise Convolution Layer Human Binary Masks Human Body Region Image Recognition Learning in AI Loss Function Mask-guided Mapping Loss MCAM Network Neural Computing Technique Novel Mask-guided Class Activation Mapping Network Person Re-identification Re-identification Data Sets View-invariant Feature WCA Module Weighted Channel Attention Module
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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