SOFTCUTMIX: Data Augmentation and Algorithmic Enhancements for Cross-Modality Person Re-Identification

Yuxiang Wan, Banghai Wang, Lunke Fei

Abstract: One of the primary challenges in achieving Infrared-Visible Person Re-Identification (IV Re-ID) is the significant differences in modalities between visible (VIS) and infrared (IR) images.In addressing this challenge, we propose a new data augmentation method-SOFTCUTMIX and introduce a new algorithm called SOFTCUTMIX Auxiliary Modality(SCAM). SOFTCUTMIX augmentation strategy aims to randomly crop and blend portions of two images with random weights, and meanwhile blend their non-cropped portions with other random weights. SCAM algorithm generates mixed modality images by blending visible light and infrared images and serves as an auxiliary modality to reduce the inherent modality differences. We also design a Channel Random Selection (CRS) to adjust the channels of the three-channel visible light image to reduce differences with the single-channel infrared image. Furthermore, we propose a Weighted Regularization Center Triplet Loss (WRCT) and combine it with the Weighted Regularization Triplet Loss (WRT). This approach reduces intra-class variations and increases inter-class separability, thereby enhancing the discriminative power of the learned features. Experimental results on the SYSU-MM01 and RegDB datasets demonstrate that our algorithm significantly outperforms the state-of-the-art method.