Cross-modality person re-identification is the study of images of people matching under different modalities (RGB modality, IR modality). Given one RGB image of a pedestrian collected under visible light in the daytime, cross-modality person re-identification aims to determine whether the same pedestrian appears in infrared images (IR images) collected by infrared cameras at night, and vice versa. Cross-modality person re-identification can solve the task of pedestrian recognition in low light or at night. This paper aims to improve the degree of similarity for the same pedestrian in two modalities by improving the feature expression ability of the network and designing appropriate loss functions. To implement our approach, we introduce a deep neural network structure combining heterogeneous center loss (HC loss) and a non-local mechanism. On the one hand, this can heighten the performance of feature representation of the feature learning module, and, on the other hand, it can improve the similarity of cross-modality within the class. Experimental data show that the network achieves excellent performance on SYSU-MM01 datasets.

中文翻译：

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基于异构中心损失和非局部特征的跨模态行人重识别

跨模态行人重识别是研究在不同模态（RGB 模态、IR 模态）下匹配的人的图像。给定一张白天在可见光下采集的行人RGB图像，跨模态行人重识别旨在确定同一行人是否出现在夜间红外摄像机采集的红外图像（IR图像）中，反之亦然。跨模态行人重识别可以解决弱光或夜间行人识别任务。本文旨在通过提高网络的特征表达能力和设计合适的损失函数来提高两种模态下同一行人的相似度。为了实现我们的方法，我们引入了一种结合异构中心损失（HC 损失）和非局部机制的深度神经网络结构。一方面，这可以提高特征学习模块的特征表示性能，另一方面，可以提高类内跨模态的相似性。实验数据表明，该网络在 SYSU-MM01 数据集上取得了优异的性能。