Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences

Hyunjong Park, Sanghoon Lee, Junghyup Lee, Bumsub Ham

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

20 Citations (Scopus)


We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting. Two main challenges in VI-reID are intra-class variations across person images, and cross-modal discrepancies between visible and infrared images. Assuming that the person images are roughly aligned, previous approaches attempt to learn coarse image- or rigid part-level person representations that are discriminative and generalizable across different modalities. However, the person images, typically cropped by off-the-shelf object detectors, are not necessarily well-aligned, which distract discriminative person representation learning. In this paper, we introduce a novel feature learning framework that addresses these problems in a unified way. To this end, we propose to exploit dense correspondences between cross-modal person images. This allows to address the cross-modal discrepancies in a pixel-level, suppressing modality-related features from person representations more effectively. This also encourages pixel-wise associations between cross-modal local features, further facilitating discriminative feature learning for VI-reID. Extensive experiments and analyses on standard VI-reID benchmarks demonstrate the effectiveness of our approach, which significantly outperforms the state of the art.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665428125
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 2021 Oct 112021 Oct 17

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgements. This research was partly supported by R&D program for Advanced Integrated-intelligence for Identification (AIID) through the National Research Foundation of KOREA (NRF) funded by Ministry of Science and ICT (NRF2018M3E3A1057289), Institute for Information and Communications Technology Promotion (IITP) funded by the Korean Government (MSIP) under Grant 2016-0-00197, and Yonsei University Research Fund of 2021 (2021-22-0001).

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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