  Visible-infrared person re-identification (VI-ReID) aims to search the same
pedestrian of interest across visible and infrared modalities. Existing models
mainly focus on compensating for modality-specific information to reduce
modality variation. However, these methods often lead to a higher computational
overhead and may introduce interfering information when generating the
corresponding images or features. To address this issue, it is critical to
leverage pedestrian-attentive features and learn modality-complete and
-consistent representation. In this paper, a novel Transferring Modality-Aware
Pedestrian Attentive Learning (TMPA) model is proposed, focusing on the
pedestrian regions to efficiently compensate for missing modality-specific
features. Specifically, we propose a region-based data augmentation module
PedMix to enhance pedestrian region coherence by mixing the corresponding
regions from different modalities. A lightweight hybrid compensation module,
i.e., the Modality Feature Transfer (MFT), is devised to integrate cross
attention and convolution networks to fully explore the discriminative
modality-complete features with minimal computational overhead. Extensive
experiments conducted on the benchmark SYSU-MM01 and RegDB datasets
demonstrated the effectiveness of our proposed TMPA model.
