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.

通过引入区域数据增强和轻量级混合补偿模块的转移感知行人关注学习 (TMPA) 模型，以有效补偿缺失的特定模态特征，并完全探索具有最小计算开销的判别模块完整特征，我们提出了一种新颖的可见 - 红外行人再识别方法。在 SYSU-MM01 和 RegDB 数据集上进行的广泛实验证明了我们提出的 TMPA 模型的有效性。