Learn Robust Pedestrian Representation Within Minimal Modality Discrepancy for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification has attracted extensive attention from the community due to its potential great application prospects in video surveillance. There are huge modality discrepancies between visible and infrared images caused by different imaging mechanisms. Existing studies alleviate modality discrepancies by aligning modality distribution or extracting modality-shared features on the original image. However, they ignore a key solution, i.e., converting visible images to gray images directly, which is efficient and effective to reduce modality discrepancies. In this paper, we transform the cross-modality person re-identification task from visible-infrared images to gray-infrared images, which is named as the minimal modality discrepancy. In addition, we propose a pyramid feature integration network (PFINet) which mines the discriminative refined features of pedestrian images and fuses high-level and semantically strong features to build a robust pedestrian representation. Specifically, PFINet first performs the feature extraction from concrete to abstract and the top-down semantic transfer to obtain multi-scale feature maps. Second, the multi-scale feature maps are inputted to the discriminative-region response module to emphasize the identity-discriminative regions by the spatial attention mechanism. Finally, the pedestrian representation is obtained by the feature integration. Extensive experiments demonstrate the effectiveness of PFINet which achieves the rank-1 accuracy of 81.95% and mAP of 74.49% on the multi-all evaluation mode of the SYSU-MM01 dataset.