dc.contributor.author | Wu, G | |
dc.contributor.author | Zhu, X | |
dc.contributor.author | Gong, S | |
dc.contributor.author | IEEE | |
dc.date.accessioned | 2020-06-01T13:12:20Z | |
dc.date.available | 2019-09-01 | |
dc.date.available | 2020-06-01T13:12:20Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Wu, Guile et al. "Person Re-Identification By Ranking Ensemble Representations". 2019 IEEE International Conference On Image Processing (ICIP), 2019. IEEE, doi:10.1109/icip.2019.8803280. Accessed 1 June 2020. | en_US |
dc.identifier.issn | 1522-4880 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/64522 | |
dc.description.abstract | Existing deep learning algorithms for person re-identification (re-id) typically rely on single-sample classification or pairwise matching constraints. This indicates a breach of deployment due to ignoring the probe-specific matching information against the gallery set encoded in ranking lists. In this work, we address this problem by exploring the idea of RANkinG Ensembles (RANGE) that learns such information from the ranking lists. Specifically, given an off-the-self deep re-id feature representation model, we construct per-probe ranking lists and exploit them to learn inter ranking ensemble representation. To mitigate the harm of inevitable false gallery positives, we further introduce a complementary intra ranking ensemble representation. Extensive experiments show that both supervised and unsupervised re-id benefit from the proposed RANGE method on four challenging benchmarks: MSMT17, Market-1501, DukeMTMC-ReID, and CUHK03. | en_US |
dc.format.extent | 2259 - 2263 | |
dc.publisher | IEEE | en_US |
dc.subject | Person re-identification | en_US |
dc.subject | ranking list | en_US |
dc.title | PERSON RE-IDENTIFICATION BY RANKING ENSEMBLE REPRESENTATIONS | en_US |
dc.type | Conference Proceeding | en_US |
dc.rights.holder | © 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. | |
pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000521828602077&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |