FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification

For Visible-Infrared person ReIDentification (VI-ReID), existing modality-specific information compensation based models try to generate the images of missing modality from existing ones for reducing cross-modality discrepancy. However, because of the large modality discrepancy between visible and infrared images, the generated images usually have low qualities and introduce much more interfering information (e.g., color inconsistency). This greatly degrades the subsequent VI-ReID performance. Alternatively, we present a novel Feature-level Modality Compensation Network (FMCNet) for VI-ReID in this paper, which aims to compensate the missing modality-specific information in the feature level rather than in the…