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.
Abstract (translated)
终身人员重识别（LReID）面临的主要挑战是如何在保持旧知识的同时适应新信息。现有的LReID方法通常使用知识蒸馏来强制执行表示对齐，但这些方法忽略了两个关键方面：特定分布感知和跨域统一知识学习，这两点对于解决这一挑战至关重要。为了克服这些限制，我们提出了一种新颖的、基于分布感知的知识统合与关联（DKUA）框架，在该框架中为每个实例进行领域风格建模以传播领域特有表示，从而增强抗遗忘能力和泛化能力。 具体而言，我们设计了一个基于分布感知的模型，将当前领域的实例级别表示转化为具有不同领域风格的特定领域表示，而无需存储旧样本即可保持已学知识。接下来，我们提出了自适应知识巩固（AKC）机制，用于动态生成跨域表示中心作为统一表示。为了进一步减轻遗忘问题，我们开发了一种统一的知识关联（UKA）机制，该机制利用统一表示作为桥梁来显式地建模跨领域关联，减少跨领域的差距。 最后，我们提出了基于分布的知识转移（DKT），以防止当前领域的分布偏离跨域的中心分布，从而提高适应能力。实验结果表明，在抗遗忘能力和泛化能力方面，我们的DKUA方法分别比现有方法提高了7.6%和5.3%的平均mAP/R@1。 我们的代码将公开发布。
URL
https://arxiv.org/abs/2508.03516
