  In real-world scenarios, person Re-IDentification (Re-ID) systems need to be
adaptable to changes in space and time. Therefore, the adaptation of Re-ID
models to new domains while preserving previously acquired knowledge is
crucial, known as Lifelong person Re-IDentification (LReID). Advanced LReID
methods rely on replaying exemplars from old domains and applying knowledge
distillation in logits with old models. However, due to privacy concerns,
retaining previous data is inappropriate. Additionally, the fine-grained and
open-set characteristics of Re-ID limit the effectiveness of the distillation
paradigm for accumulating knowledge. We argue that a Re-ID model trained on
diverse and challenging pedestrian images at a large scale can acquire robust
and general human semantic knowledge. These semantics can be readily utilized
as shared knowledge for lifelong applications. In this paper, we identify the
challenges and discrepancies associated with adapting a pre-trained model to
each application domain, and introduce the Distribution Aligned Semantics
Adaption (DASA) framework. It efficiently adjusts Batch Normalization (BN) to
mitigate interference from data distribution discrepancy and freezes the
pre-trained convolutional layers to preserve shared knowledge. Additionally, we
propose the lightweight Semantics Adaption (SA) module, which effectively
adapts learned semantics to enhance pedestrian representations. Extensive
experiments demonstrate the remarkable superiority of our proposed framework
over advanced LReID methods, and it exhibits significantly reduced storage
consumption. DASA presents a novel and cost-effective perspective on
effectively adapting pre-trained models for LReID.
