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We address the problem of visible-infrared person re-identification(VI-reID), that is, retrieving a set of person images, captured by visible orinfrared cameras, in a cross-modal setting. Two main challenges in VI-reID areintra-class variations across person images, and cross-modal discrepanciesbetween visible and infrared images. Assuming that the person images areroughly aligned, previous approaches attempt to learn coarse image- or rigidpart-level person representations that are discriminative and generalizableacross different modalities. However, the person images, typically cropped byoff-the-shelf object detectors, are not necessarily well-aligned, whichdistract discriminative person representation learning. In this paper, weintroduce a novel feature learning framework that addresses these problems in aunified way. To this end, we propose to exploit dense correspondences betweencross-modal person images. This allows to address the cross-modal discrepanciesin a pixel-level, suppressing modality-related features from personrepresentations more effectively. This also encourages pixel-wise associationsbetween cross-modal local features, further facilitating discriminative featurelearning for VI-reID. Extensive experiments and analyses on standard VI-reIDbenchmarks demonstrate the effectiveness of our approach, which significantlyoutperforms the state of the art.

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