Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165561
Title: Person re-identification via pose-aware multi-semantic learning
Authors: Luo, Xiangzhong
Duong, Luan H. K.
Liu, Weichen
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Luo, X., Duong, L. H. K. & Liu, W. (2020). Person re-identification via pose-aware multi-semantic learning. 2020 IEEE International Conference on Multimedia and Expo (ICME). https://dx.doi.org/10.1109/ICME46284.2020.9102719
Project: MOE2019-T2-1-071 
MOE2019-T1- 001-072 
NAP (M4082282) 
SUG (M4082087) 
Conference: 2020 IEEE International Conference on Multimedia and Expo (ICME)
Abstract: Person re-identification (ReID) remains an open-ended research topic, with its variety of substantial applications such as tracking, searching, etc. Existing methods mostly explore the highest-semantic feature embedding, ignoring the insights hidden among the earlier layers. Moreover, owing to the misalignment and pose variations, pose-related information is of great significance and needs to be comprehensively utilized. In this paper, we present a novel person ReID framework called Pose-aware Multi-semantic Fusion Network (PMFN). First, taking into account multiple semantics, we propose Multi-semantic Fusion Network (MFN) as the backbone, employing several shortcuts to reserve bypass feature maps for subsequent fusion. Second, to learn a pose-sensitive embedding, pose-aware clues are considered, forming the complete PMFN and investigating the well-aligned global and local body regions. Finally, the center loss is introduced for enhancing the feature discriminability. Exhaustive experiments on two large-scale person ReID benchmarks demonstrate the strengths of our approach over recent state-of-the-art works.
URI: https://hdl.handle.net/10356/165561
ISBN: 978-1-7281-1331-9
ISSN: 1945-788X
DOI: 10.1109/ICME46284.2020.9102719
DOI (Related Dataset): 10.21979/N9/DKN6CN
Schools: School of Computer Science and Engineering 
Research Centres: Parallel and Distributed Computing Centre 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/ 10.1109/ICME46284.2020.9102719.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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