Unity style transfer for person re-identification

Chong Liu, Xiaojun Chang, Yi-Dong Shen

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Abstract

Style variation has been a major challenge for person re-identification, which aims to match the same pedestrians across different cameras. Existing works attempted to address this problem with camera-invariant descriptor subspace learning. However, there will be more image artifacts when the difference between the images taken by different cameras is larger. To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras. Specifically, we firstly create UnityGAN to learn the style changes between cameras, producing shape-stable style-unity images for each camera, which is called UnityStyle images. Meanwhile, we use UnityStyle images to eliminate style differences between different images, which makes a better match between query and gallery. Then, we apply the proposed method to Re-ID models, expecting to obtain more style-robust depth features for querying. We conduct extensive experiments on widely used benchmark datasets to evaluate the performance of the proposed framework, the results of which confirm the superiority of the proposed model.

Original languageEnglish
Title of host publicationProceedings - 33th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2020
EditorsCe Liu, Greg Mori, Kate Saenko, Silvio Savarese
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages6886-6895
Number of pages10
ISBN (Electronic)9781728171685
ISBN (Print)9781728171692
DOIs
Publication statusPublished - 2020
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com (Website )
https://openaccess.thecvf.com/CVPR2020 (Proceedings)
https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
Abbreviated titleCVPR 2020
CountryChina
CityVirtual
Period14/06/2019/06/20
Internet address

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