Visible-Xray Cross-Modality Package Re-Identification

During the package inspection, once prohibited articles are checked under the X-ray, the inspector needs to find the corresponding package in time for further confirmation. When the number of passengers increases, this process is time-consuming. Recently, prohibited articles detection as a detection task has attracted much attention, but few studies have focused on Visible-Xray package re-identification (VX-ReID) task. In this paper, we mainly explore the VX-ReID task. Firstly, we establish the first VX-ReID dataset RX01, which includes 55883 Visible and 29174 X-ray package images. Furthermore, we introduce a baseline model that includes a cross-modality channel attention module (CMCA) and momentum contrast mAP (MoCoAP). CMCA is used to enhance channels that contain modality-invariant information. MoCoAP is a differentiable mAP approximation strategy that directly optimizes the retrieve performance of the model. By combining these two strategies, we achieve competitive performance on the RX01 and SYSU-MM01 datasets. Code will be released at https://github.com/cjjjao/VX-ReID.