Abstract: To solve the overfitting problem caused by leveraging pedestrian detail features to reduce the modality gap between visible and infrared images in the visible-infrared person re-identification task, an end-to-end improved CNN ( Convolutional Neural Networks) based on data augmentation techniques and detail feature extraction methods is proposed. With the goal of reducing the modality gap by generating diverse embedding features while simultaneously matching pedestrian details, an efficient dual-branch attention module is designed to learn more informative feature representations, and a triplet loss function with data augmentation is proposed to alleviate overfitting. Extensive experiments on the public datasets SYSU-MM01 and RegDB demonstrate that the proposed method outperforms other methods, effectively mitigating the overfitting problem caused by attention mechanisms and improving the accuracy of person re-identification.

Key words: convolutional neural network(CNN), visible-infrared person re-identification(VI-ReID), attention mechanisms, data augmentation, overfitting