Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1QE, UK.
Sensors (Basel). 2023 Apr 15;23(8):4011. doi: 10.3390/s23084011.
Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a graph to learn the relevance of pedestrian images of distinct modalities to narrow the gap between IR modality and RGB modality, but they omit the correlation between IR image and RGB image pairs. In this paper, we propose a novel graph model called Local Paired Graph Attention Network (LPGAT). It uses the paired local features of pedestrian images from different modalities to build the nodes of the graph. For accurate propagation of information among the nodes of the graph, we propose a contextual attention coefficient that leverages distance information to regulate the process of updating the nodes of the graph. Furthermore, we put forward Cross-Center Contrastive Learning (C3L) to constrain how far local features are from their heterogeneous centers, which is beneficial for learning the completed distance metric. We conduct experiments on the RegDB and SYSU-MM01 datasets to validate the feasibility of the proposed approach.
跨模态行人重识别（ReID）旨在从红外（IR）行人图像中搜索 RGB 模态的行人图像，反之亦然。最近，一些方法构建了一个图来学习不同模态的行人图像之间的相关性，以缩小 IR 模态和 RGB 模态之间的差距，但它们忽略了 IR 图像和 RGB 图像对之间的相关性。在本文中，我们提出了一种名为局部成对图注意网络（LPGAT）的新图模型。它使用来自不同模态的行人图像的成对局部特征来构建图的节点。为了在图的节点之间准确地传播信息，我们提出了上下文注意系数，利用距离信息来调节图的节点更新过程。此外，我们提出了交叉中心对比学习（C3L）来约束局部特征与其异质中心的距离，这有利于学习完整的距离度量。我们在 RegDB 和 SYSU-MM01 数据集上进行了实验，验证了所提出方法的可行性。
