A NEW PARADIGM FOR CROSS-MODALITY PERSON RE-IDENTIFICATION

Visible and infrared Person Re-identification(ReID) is still very challenging on account of few cross-modality dataset and large inter-modality variation. Most existing cross-modality ReID methods have trouble eliminating cross-modality discrepancy resulting from the heterogeneous images. In this paper, we present an effective framework and build a large benchmark, named NPU-ReID. To this end, we propose a dual-path fusion network and taking transformer as the smallest feature extraction unit. To expand cross-modality sample diversity, we propose a modality augmentation strategy to generate semi-modality pedestrian images by exchanging certain patch and the main innovation is that the cross-modality gap can be indirectly minimized by reducing the variance of semi-modality and infrared or visible modality. Moreover, in order to make the traditional triplet loss more suitable for cross-modal matching tasks, multi-masking triplet loss is a targeted design for optimizing the relative distance between anchor and positive/negative samples pairs from cross-modality, especially constraining the distance between simple and hard positive samples. Experimental results demonstrate that our proposed method achieves superior performance than other methods on SYSU-MM01, RegDB and our proposed NPU-ReID dataset, especially on the RegDB dataset with significant improvement of 6.81$\%$ in rank1 and 9.65$\%$ in mAP.