Augmented and Softened Matching for Unsupervised Visible-Infrared Person Re-Identification

Color variations, a key challenge in the unsupervised visible-infrared person re-identification (UVI-ReID) task, have garnered significant attention. While existing UVIReID methods have made substantial efforts during the optimization phase to enhance the model's robustness to color variations, they often overlook the impact of color variations on the acquisition of pseudo-labels. To address this, in this paper, we focus on improving the robustness of pseudo-labels to color variations through data augmentation and propose an augmented and softened matching (ASM) method. Specifically, we first develop the crossmodality augmented matching (CAM) module, which performs channel augmentation on visible images to generate augmented images. Then, based on the fusion of the visibleinfrared and augmented-infrared centroid similarity matrices, CAM establishes cross-modality correspondences that are robust to color variations. To increase training stability, we design a soft-labels momentum update (SMU) strategy, which converts traditional one-hot labels into soft-labels through momentum updates, thus adapting to CAM. During the optimization phase, we introduce the cross-modality soft contrastive loss and cross-modality hard contrastive loss to promote modality-invariant learning from the perspectives of shared and diversified features, respectively. Extensive experimental results validate the effectiveness of the proposed method, showing that ASM not only outperforms state-of-the-art unsupervised methods but also competes with some supervised methods.