CoMix: Collaborative Mixed Learning via Style Fuzzy Normalization for Visible–Infrared Person Re-Identification

Visible–infrared person re-identification (VI-ReID) focuses on accurately matching individuals across different imaging modalities. Existing studies focus on generating modality-consistent images at the pixel level through the use of generative adversarial networks (GANs) to mitigate the impact of modality discrepancies. However, these methods face significant challenges in overcoming the limitation that synthesized samples from different modalities may suffer from semantic distortion. In this work, we propose an online one-stage style fuzzy normalization (SFN) method to generate modality-fuzzy features in the latent space while regularizing the model’s predictions. Specifically, SFN adaptively mixes the feature statistics of two random modality instances of the same identity in a single forward pass during training. In this process, to enhance the richness of modality interaction information, we design a novel causality balance loss, which enforces the generated fuzzy features to be independent of their initial modality while simultaneously encouraging them to align more closely with the other modality. Furthermore, we introduce an identity-aware consistency loss to regularize the predictions between the original and SFN-generated features to ensure semantic consistency. In contrast to prior work, SFN is a plug-and-play module that does not rely on any generative-based models, making it highly adaptable to various network architectures. Extensive experiments were performed on three public cross-modality datasets to ensure fair and reliable comparisons. The empirical results demonstrate the clear superiority of our method over previous state-of-the-art methods.