Joint Robust Representation And Generalization Enhancement For Cross-Modality Person Re-Identification

Abstract

Cross-modality person re-identification (cm-ReID) aims to match pedestrian images from visible and infrared cameras. Most existing methods ignore data bias due to different cameras and views and overlook the strong dependence between feature maps that hinders modal alignment. In this paper, we propose a unified method named Joint Robust Representation and Generalization Enhancement (RRGE) to alleviate the above issues. First, we propose a robust representation module (RRM), which can improve the model’s robustness for the global context, camera, and view change perturbations. Second, we propose a generalization enhancement module (GEM), which uses channel-level dropout to alleviate the dependencies between feature maps to improve the model’s generalization. Moreover, we balance the number of different modalities in each batch. Our method outperforms other state-of-the-art methods in terms of cross-modality person re-identification tasks.