Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Existing LReID approaches typically rely on data replay and knowledge distillation to mitigate this issue. However, data replay methods compromise data privacy by storing historical exemplars, while knowledge distillation methods suffer from limited performance due to the cumulative forgetting of undistilled knowledge. To overcome these challenges, we propose a novel paradigm that models and rehearses the distribution of the old domains to enhance knowledge consolidation during the new data learning, possessing a strong anti-forgetting capacity without storing any exemplars. Specifically, we introduce an exemplar-free LReID method called Distribution Rehearsing via Adaptive Style Kernel Learning (DASK). DASK includes a Distribution Rehearser Learning mechanism that learns to transform arbitrary distribution data into the current data style at each learning step. To enhance the style transfer capacity of DRL, an Adaptive Kernel Prediction network is explored to achieve an instance-specific distribution adjustment. Additionally, we design a Distribution Rehearsing-driven LReID Training module, which rehearses old distribution based on the new data via the old AKPNet model, achieving effective new-old knowledge accumulation under a joint knowledge consolidation scheme. Experimental results show our DASK outperforms the existing methods by 3.6%-6.8% and 4.5%-6.5% on anti-forgetting and generalization capacity, respectively. Our code is available at https://github.com/zhoujiahuan1991/AAAI2025-DASK

翻译：终身行人重识别（LReID）是一项重要但具有挑战性的任务，由于训练步骤之间存在显著的领域差异，该任务易受灾难性遗忘的影响。现有的LReID方法通常依赖数据回放和知识蒸馏来缓解此问题。然而，数据回放方法通过存储历史样本来实现，这损害了数据隐私；而知识蒸馏方法则由于未蒸馏知识的累积遗忘而导致性能受限。为克服这些挑战，我们提出了一种新颖的范式，该范式通过对旧领域分布进行建模和复现，以增强新数据学习过程中的知识巩固，从而在不存储任何样本的情况下具备强大的抗遗忘能力。具体而言，我们提出了一种名为“基于自适应风格核学习的分布复现”（DASK）的免示例LReID方法。DASK包含一个分布复现学习机制，该机制学习在每个学习步骤中将任意分布的数据转换为当前数据的风格。为增强分布复现学习的风格迁移能力，我们探索了一种自适应核预测网络，以实现实例特定的分布调整。此外，我们设计了一个分布复现驱动的LReID训练模块，该模块通过旧的AKPNet模型基于新数据复现旧分布，在联合知识巩固方案下实现有效的新旧知识积累。实验结果表明，我们的DASK在抗遗忘能力和泛化能力上分别比现有方法高出3.6%-6.8%和4.5%-6.5%。我们的代码可在https://github.com/zhoujiahuan1991/AAAI2025-DASK 获取。