The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengenalan Semula Orang telah menerima kajian yang meluas dalam beberapa tahun kebelakangan ini dan mencapai kemajuan yang mengagumkan. Kaedah cemerlang terkini mengekstrak ciri diskriminasi dengan menghiris peta ciri rangkaian saraf dalam kepada beberapa jalur. Masih terdapat peningkatan pada gabungan ciri dan strategi pembelajaran metrik yang boleh membantu mempromosikan kaedah berasaskan kepingan. Dalam kertas kerja ini, kami mencadangkan rangka kerja baru yang boleh dilatih hujung ke hujung, dipanggil Rangkaian Berasaskan Slice Berbilang Peringkat (MSN), untuk menangkap ciri dalam kedua-dua peringkat dan bahagian badan yang berbeza. Model kami terdiri daripada seni bina rangkaian dwi-cawangan, satu cawangan untuk pengekstrakan ciri global dan cawangan lain untuk yang tempatan. Kedua-dua cawangan memproses ciri berbilang peringkat menggunakan modul yang sama ciri piramid. Dengan menggabungkan ciri global dan tempatan, ciri tersendiri dieksploitasi dan dibandingkan dengan betul. Selain itu, kaedah cadangan kami secara kreatif memperkenalkan kehilangan pusat tiga kali ganda untuk menghuraikan fungsi kehilangan gabungan, yang membantu melatih rangkaian pembelajaran bersama. Dengan menunjukkan eksperimen komprehensif pada set data penilaian arus perdana termasuk Market-1501, DukeMTMC, CUHK03, ini menunjukkan bahawa model cadangan kami berjaya mencapai prestasi cemerlang dan mengatasi banyak pendekatan sedia ada. Sebagai contoh, pada set data DukeMTMC dalam mod pertanyaan tunggal, kami memperoleh hasil yang hebat dari Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).
Yusheng ZHANG
South China University of Technology
Zhiheng ZHOU
South China University of Technology
Bo LI
South China University of Technology
Yu HUANG
South China University of Technology
Junchu HUANG
South China University of Technology
Zengqun CHEN
South China University of Technology
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Salinan
Yusheng ZHANG, Zhiheng ZHOU, Bo LI, Yu HUANG, Junchu HUANG, Zengqun CHEN, "Improving Slice-Based Model for Person Re-ID with Multi-Level Representation and Triplet-Center Loss" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2230-2237, November 2019, doi: 10.1587/transinf.2019EDP7067.
Abstract: Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7067/_p
Salinan
@ARTICLE{e102-d_11_2230,
author={Yusheng ZHANG, Zhiheng ZHOU, Bo LI, Yu HUANG, Junchu HUANG, Zengqun CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Slice-Based Model for Person Re-ID with Multi-Level Representation and Triplet-Center Loss},
year={2019},
volume={E102-D},
number={11},
pages={2230-2237},
abstract={Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).},
keywords={},
doi={10.1587/transinf.2019EDP7067},
ISSN={1745-1361},
month={November},}
Salinan
TY - JOUR
TI - Improving Slice-Based Model for Person Re-ID with Multi-Level Representation and Triplet-Center Loss
T2 - IEICE TRANSACTIONS on Information
SP - 2230
EP - 2237
AU - Yusheng ZHANG
AU - Zhiheng ZHOU
AU - Bo LI
AU - Yu HUANG
AU - Junchu HUANG
AU - Zengqun CHEN
PY - 2019
DO - 10.1587/transinf.2019EDP7067
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).
ER -