Learning by Aligning: Visible Infrared Person Re identification using Cross Modal Correspondences
We address the problem of visible infrared person reidentification (VI reID) that is, retrieving a set of person images, captured by visible or infrared cameras, in a crossmodal setting. Two main challenges in VI reID are intraclass 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 imageor 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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