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End-to-end correspondence and relationship learning of mid-level deep features for person re-identification
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Lin, Shan and Li, Chang-Tsun (2017) End-to-end correspondence and relationship learning of mid-level deep features for person re-identification. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 39 Nov - 1 Dec 2017 ISBN 9781538628393. doi:10.1109/DICTA.2017.8227426
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WRAP-end-to-end-correspondence-relationship-learning-mid-level-deep-features-person-re-identification-Li-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1882Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/DICTA.2017.8227426
Abstract
In this paper, a unified deep convolutional architecture is proposed to address the problems in the person re-identification task. The proposed method adaptively learns the discriminative deep mid-level features of a person and constructs the correspondence features between an image pair in a data-driven manner. The previous Siamese structure deep learning approaches focus only on pair-wise matching between features. In our method, we consider the latent relationship between mid-level features and propose a network structure to automatically construct the correspondence features from all input features without a pre-defined matching function. The experimental results on three benchmarks VIPeR, CUHK01 and CUHK03 show that our unified approach improves over the previous state-of-the-art methods.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Data encryption (Computer science), Image processing -- Digital techniques, Biometric identification, Computer vision, Human face recognition (Computer science), Human-computer interaction, Pattern recognition systems | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781538628393 | ||||||
Official Date: | 21 December 2017 | ||||||
Dates: |
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DOI: | 10.1109/DICTA.2017.8227426 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2017 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. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 18 May 2020 | ||||||
Date of first compliant Open Access: | 19 May 2020 | ||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||
Title of Event: | International Conference on Digital Image Computing: Techniques and Applications (DICTA) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Sydney, Australia | ||||||
Date(s) of Event: | 39 Nov - 1 Dec 2017 |
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