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.

在本文中，我们提出了一种适应分布匹配语义调整（Distribution Aligned Semantics Adaption，简称 DASA）框架，通过有效地调整 BN 以减轻数据分布差异的干扰并冻结预训练的卷积层来保留共享知识，其显著降低存储消耗，优于先进的生命周期行人重新识别方法，是一种创新且经济高效的适应预训练模型进行生命周期行人重新识别的方法。