Diffusion Augmentation and Pose Generation Based Pre-Training Method for Robust Visible-Infrared Person Re-Identification

Abstract

Cross-Modal Visible-Infrared Person Re-identification (VI-REID) constitutes a vital application for constructing all-time surveillance systems. However, the current VI-REID model exhibits significant performance deterioration in noisy environments. Existing algorithms endeavor to mitigate this challenge through fine-tuning stages. We contend that, in contrast to fine-tuning stages, the pre-training phase can effectively exploit the attributes of extensive unlabeled data, thereby facilitating the development of a robust VI-REID model. Therefore, in this paper, we propose a pre-training method for VI-REID based on Diffusion Augmentation and Pose Generation (DAPG), aiming to enhance the robustness and recognition rate of VI-REID models in the presence of damaged scenes. Multiple transfer experiments on the SYSU-MM01 and RegDB datasets demonstrate that our method outperforms existing self-supervised methods, as evidenced by the results.