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Generalizable Person Re-identification by Domain-Invariant Mapping Network

Song, Jifei, Yang, Yongxin, Song, Yi-Zhe, Xiang, Tao and Hospedales, Timothy M. (2019) Generalizable Person Re-identification by Domain-Invariant Mapping Network In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 16-20 Jun 2019, Long Beach, California, USA.

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Abstract

We aim to learn a domain generalizable person reidentification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value in real-world deployments, generalizable ReID has seldom been studied. In this work, a novel deep ReID model termed Domain-Invariant Mapping Network(DIMN) is proposed. DIMN is designed to learn a mapping between a person image and its identity classifier, i.e., it produces a classifier using a single shot. To make the model domain-invariant, we follow a meta-learning pipeline and sample a subset of source domain training tasks during each training episode. However, the model is significantly different from conventional meta-learning methods in that: (1) no model updating is required for the target domain, (2) different training tasks share a memory bank for maintaining both scalability and discrimination ability, and (3) it can be used to match an arbitrary number of identities in a target domain. Extensive experiments on a newly proposed large-scale ReID domain generalization benchmark show that our DIMN significantly outperforms alternative domain generalization or meta-learning methods.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Song, Jifei
Yang, Yongxinyongxin.yang@surrey.ac.uk
Song, Yi-Zhey.song@surrey.ac.uk
Xiang, Taot.xiang@surrey.ac.uk
Hospedales, Timothy M.
Date : 2019
Copyright Disclaimer : © 2019 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.
Related URLs :
Depositing User : Diane Maxfield
Date Deposited : 31 May 2019 15:52
Last Modified : 28 Oct 2019 13:54
URI: http://epubs.surrey.ac.uk/id/eprint/851918

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