Distribution-aware Knowledge Unification and Association for Non-exemplar Lifelong Person Re-identification

• URL: http://arxiv.org/abs/2508.03516v1
• Date: Tue, 05 Aug 2025 14:44:29 GMT
• Title: Distribution-aware Knowledge Unification and Association for Non-exemplar Lifelong Person Re-identification
• Authors: Shiben Liu, Mingyue Xu, Huijie Fan, Qiang Wang, Yandong Tang, Zhi Han,
• Abstract summary: Lifelong person re-identification (LReID) encounters a key challenge: balancing the preservation of old knowledge with adaptation to new information.<n>We propose a novel distribution-aware knowledge unification and association framework to overcome these limitations.<n> Experimental results show our DKUA outperforms the existing methods by 7.6%/5.3% average mAP/R@1 improvement on anti-forgetting and generalization capacity.
• Score: 10.062289730759575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong person re-identification (LReID) encounters a key challenge: balancing the preservation of old knowledge with adaptation to new information. Existing LReID methods typically employ knowledge distillation to enforce representation alignment. However, these approaches ignore two crucial aspects: specific distribution awareness and cross-domain unified knowledge learning, both of which are essential for addressing this challenge. To overcome these limitations, we propose a novel distribution-aware knowledge unification and association (DKUA) framework where domain-style modeling is performed for each instance to propagate domain-specific representations, enhancing anti-forgetting and generalization capacity. Specifically, we design a distribution-aware model to transfer instance-level representations of the current domain into the domain-specific representations with the different domain styles, preserving learned knowledge without storing old samples. Next, we propose adaptive knowledge consolidation (AKC) to dynamically generate the unified representation as a cross-domain representation center. To further mitigate forgetting, we develop a unified knowledge association (UKA) mechanism, which explores the unified representation as a bridge to explicitly model inter-domain associations, reducing inter-domain gaps. Finally, distribution-based knowledge transfer (DKT) is proposed to prevent the current domain distribution from deviating from the cross-domain distribution center, improving adaptation capacity. Experimental results show our DKUA outperforms the existing methods by 7.6%/5.3% average mAP/R@1 improvement on anti-forgetting and generalization capacity, respectively. Our code will be publicly released.

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