Lifelong person re-identification (LReID) seeks to continuously train models across multiple target domains, enabling effective generalization in both known and unseen domains. Achieving a balance between “plasticity” (the ability to adapt to new knowledge) and “stability” (the capacity to prevent forgetting) is crucial in lifelong learning. However, most existing LReID methods primarily focus on enhancing model stability or plasticity, often neglecting the critical balance between them. Moreover, current LReID approaches largely rely on supervised learning, which necessitates large-scale pre-labeled datasets — a process that is both time-consuming and labor-intensive in practical applications. To address these challenges, this paper proposes an Unsupervised LReID approach called the Affinity Harmonization Network (AHN). AHN includes an Old Domain Affinity Constraint (ODAC) module, which builds an expert model for the old domain to provide affinity relationships as references. This helps limit changes among old representations, enabling the model to integrate new knowledge while preserving compatibility with previous representations. To harmonize stability and plasticity while guiding the model in acquiring new knowledge, AHN incorporates a Current Domain Affinity Guidance (CDAG) module. This module builds an expert model for the new domain and uses the generated affinity relationships to assist in training the model. Furthermore, this paper proposes the Old Domain Intra-class Variance Constraint (OIVC) module, which mitigates potential deviations in the intra-class variance of legacy samples by limiting the distance between replay samples and old domain camera prototypes. Extensive experiments demonstrate that our method achieves significant performance improvements over existing unsupervised lifelong ReID methods, with an average gain of 5.3% in mAP and 5.2% in Rank-1 accuracy.
Tan et al. (Mon,) studied this question.