Abstract
Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However, it faces significant challenges due to modality discrepancies, varying illumination, and frequent occlusions. To overcome these obstacles, we propose \textbf{AMINet}, an Adaptive Modality Interaction Network. AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images, improving robustness against occlusions and background clutter. The model integrates an interactive feature fusion strategy for deep intra-modal and cross-modal alignment, enhancing generalization and effectively bridging the RGB-IR modality gap. Furthermore, AMINet utilizes phase congruency for robust, illumination-invariant feature extraction and incorporates an adaptive multi-scale kernel MMD to align feature distributions across varying scales. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, achieving a Rank-1 accuracy of $74.75\%$ on SYSU-MM01, surpassing the baseline by $7.93\%$ and outperforming the current state-of-the-art by $3.95\%$.
Abstract (translated)
可见光-红外重识别（VI-ReID）在搜索与救援、基础设施保护和夜间监控等应用中扮演着关键角色。然而，由于模态差异、光照变化以及频繁遮挡等问题，VI-ReID面临着重大挑战。为了克服这些障碍，我们提出了一种自适应模态交互网络（AMINet）。AMINet采用了多层次特征提取方法，从全身图像和上半身图像中捕捉全面的身份属性信息，提高了对遮挡和背景杂乱的鲁棒性。 该模型整合了一个互动式特征融合策略，用于深层次的模内及跨模态对齐，从而增强泛化能力，并有效地弥合了RGB-IR模态之间的差距。此外，AMINet利用相位一致性进行稳健且不受光照影响的特征提取，并采用自适应多尺度核MMD（最大均值差异）来在不同尺度上对齐特征分布。 在基准数据集上的大量实验验证了我们方法的有效性，在SYSU-MM01数据集中实现了74.75%的Rank-1准确率，比基线提高了7.93%，并且超过了当前最先进的技术3.95%。
URL
https://arxiv.org/abs/2502.21163
