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.

通过 Diverse Representation Embedding （DRE）框架，提出了一种新颖的 Lifelong Person Re-Identification（LReID）方法，该方法在实例级别和任务级别布局上保留老知识并适应新信息，减少旧任务和新任务之间的领域差距，从而提高模型的性能。