Multi-Level Factorisation Net for Person Re-Identification

Xiaobin Chang, Timothy Hospedales, Tao Xiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes. We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
DOIs
Publication statusPublished - 17 Dec 2018
EventComputer Vision and Pattern Recognition 2018 - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018
http://cvpr2018.thecvf.com/
http://cvpr2018.thecvf.com/
http://cvpr2018.thecvf.com/

Publication series

Name
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceComputer Vision and Pattern Recognition 2018
Abbreviated titleCVPR 2018
CountryUnited States
CitySalt Lake City
Period18/06/1822/06/18
Internet address

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