  Person ReID methods always learn through a stationary domain that is fixed by
the choice of a given dataset. In many contexts (e.g., lifelong learning),
those methods are ineffective because the domain is continually changing in
which case incremental learning over multiple domains is required potentially.
In this work we explore a new and challenging ReID task, namely lifelong person
re-identification (LReID), which enables to learn continuously across multiple
domains and even generalise on new and unseen domains. Following the cognitive
processes in the human brain, we design an Adaptive Knowledge Accumulation
(AKA) framework that is endowed with two crucial abilities: knowledge
representation and knowledge operation. Our method alleviates catastrophic
forgetting on seen domains and demonstrates the ability to generalize to unseen
domains. Correspondingly, we also provide a new and large-scale benchmark for
LReID. Extensive experiments demonstrate our method outperforms other
competitors by a margin of 5.8% mAP in generalising evaluation.
