Abstract
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 this https URL
Abstract (translated)
终身人物再识别（LReID）是一项重要但极具挑战性的任务，由于训练步骤之间的显著领域差异，该任务会遭受灾难性遗忘的影响。现有的LReID方法通常依赖于数据重放和知识蒸馏来缓解这一问题。然而，数据重放方法通过存储历史样本牺牲了数据隐私，而知识蒸馏方法因未被提炼的知识累积遗忘而导致性能受限。为了克服这些挑战，我们提出了一种新的范式，该范式通过建模并复习旧领域的分布，在新数据学习过程中增强知识巩固，无需存储任何样例就具有强大的抗遗忘能力。具体来说，我们引入了一种无样本的LReID方法，称为自适应风格核学习（DASK）下的分布复习。DASK包括一个分布复习器学习机制，该机制在每次学习步骤中学会将任意分布的数据转换为当前数据样式。为了增强DRL的风格迁移能力，探索了一个自适应核预测网络以实现特定实例的分布调整。此外，我们设计了一个由分布复习驱动的LReID训练模块，通过旧AKPNet模型基于新数据复习旧分布，从而在联合知识巩固方案下实现有效的新型和旧型知识积累。实验结果表明，我们的DASK方法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%。我们的代码可在该链接获取：[此https URL]
URL
https://arxiv.org/abs/2412.09224
