Before revision: RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest between heterogeneous modalities, suffering from large  modality discrepancy caused by different sensory devices. Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent , leading to performance degradation.
After revision: RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest between heterogeneous modalities, suffering from large modality discrepancy caused by different sensing wavelength ranges. Existing work usually minimizes such discrepancy by aligning domain distribution of global features, while neglecting the intra-modality structural relations between semantic parts. This could result in the network overly focusing on local cues, without considering long-range body part dependencies , leading to performance degradation.