Abstract
Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while learning new information incrementally. Task-level domain gaps and limited old task datasets are key factors leading to catastrophic forgetting in ReLD, which are overlooked in existing methods. To alleviate this problem, we propose a novel Diverse Representation Embedding (DRE) framework for LReID. The proposed DRE preserves old knowledge while adapting to new information based on instance-level and task-level layout. Concretely, an Adaptive Constraint Module (ACM) is proposed to implement integration and push away operations between multiple representations, obtaining dense embedding subspace for each instance to improve matching ability on limited old task datasets. Based on the processed diverse representation, we interact knowledge between the adjustment model and the learner model through Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the task-level layout, which reduce the task-wise domain gap on both old and new tasks, and exploit diverse representation of each instance in limited datasets from old tasks, improving model performance for extended periods. Extensive experiments were conducted on eleven Re-ID datasets, including five seen datasets for training in order-1 and order-2 orders and six unseen datasets for inference. Compared to state-of-the-art methods, our method achieves significantly improved performance in holistic, large-scale, and occluded datasets.
Abstract (translated)
终身人物识别（LReID）旨在从连续的数据流中持续学习，并将个体跨越多台摄像机进行匹配。LReID的关键挑战是如何在逐渐学习新信息的同时有效保留旧知识。任务级别领域空白和有限的旧任务数据集是导致ReLD灾难性遗忘的原因，而现有的方法忽略了这个问题。为了减轻这个问题，我们提出了一个名为Diverse Representation Embedding（DRE）的新LReID框架。DRE在保留旧知识的同时，根据实例级别和任务级别布局自适应地适应新信息。具体来说，我们提出了一个自适应约束模块（ACM）来实现多个表示之间的集成和推开操作，为每个实例获得稠密嵌入子空间，从而提高在有限的老任务数据集中的匹配能力。根据处理后的多样性表示，我们在任务级别布局中通过知识更新（KU）和知识保留（KP）策略与调整模型和学习器模型交互，从而在老任务和新任务上减少任务级别领域差异，并从老任务有限数据集中每个实例的多样性表示中挖掘知识，提高模型在长时间内的性能。我们在包括order-1和order-2订单的五个可见数据集以及六个未见数据集上进行了广泛的实验。与最先进的 methods相比，我们的方法在整体、大型和遮挡的数据集上取得了显著的改进。
URL
https://arxiv.org/abs/2403.16003
