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

• URL: http://arxiv.org/abs/2108.07422v1
• Date: Tue, 17 Aug 2021 03:38:51 GMT
• Title: Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences
• Authors: Hyunjong Park, Sanghoon Lee, Junghyup Lee, Bumsub Ham
• Abstract summary: Two main challenges in VI-reID are intra-class variations across person images, and cross-modal discrepancies between visible and infrared images. We introduce a novel feature learning framework that addresses these problems in a unified way.
• Score: 42.16002082436691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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