Prototype Topology Consistency for Visible-Infrared Lifelong Person Re-Identification

Visible-Infrared Lifelong Person Re-Identification (VI-LReID) requires a model to sequentially acquire cross-modal person retrieval capabilities across sequentially arriving tasks while retaining performance on previously learned ones.Existing methods face two fundamental problems.First, new-task gradient updates disrupt each instance's assignment ranking over historical class prototypes, which is the direct mechanism behind past-task retrieval performance degradation. At the same time, the feature manifold inevitably undergoes global drift as a necessary byproduct of new-task adaptation. Effective anti-forgetting should target the detrimental ranking disruption while permitting this necessary drift.Second, visible RGB and thermal infrared (TIR) imaging capture fundamentally different physical cues: texture-and-color reflectance versus heat radiation contours. This causes the two modalities to form inherently inconsistent inter-class similarity structures that conventional cross-modal supervision cannot resolve, manifesting as highly asymmetric forgetting across modalities under shared-backbone continual training.We propose S-PTC (Stage-wise Prototype Topology Consistency), which preserves each instance's relative distribution over the frozen historical prototype set, protecting assignment rankings while leaving the overall feature manifold free to evolve for new-task adaptation.We further propose CM-PTC (Cross-Modal Prototype Topology Consistency), which aligns the inter-class topology matrices of RGB and IR prototypes, mitigating the structural inconsistency rooted in their imaging physics.Both modules require no stored exemplars.Extensive experiments on the VI-LReID benchmark verify the effectiveness and superiority of our approach against state-of-the-art methods.