Abstract
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 language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 12026-12035 |
Number of pages | 10 |
ISBN (Electronic) | 9781665428125 |
DOIs | |
Publication status | Published - 2021 |
Event | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada Duration: 2021 Oct 11 → 2021 Oct 17 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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ISSN (Print) | 1550-5499 |
Conference
Conference | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 21/10/11 → 21/10/17 |
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