  Lifelong object re-identification incrementally learns from a stream of
re-identification tasks. The objective is to learn a representation that can be
applied to all tasks and that generalizes to previously unseen
re-identification tasks. The main challenge is that at inference time the
representation must generalize to previously unseen identities. To address this
problem, we apply continual meta metric learning to lifelong object
re-identification. To prevent forgetting of previous tasks, we use knowledge
distillation and explore the roles of positive and negative pairs. Based on our
observation that the distillation and metric losses are antagonistic, we
propose to remove positive pairs from distillation to robustify model updates.
Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on
extensive intra-domain experiments on person and vehicle re-identification
datasets, as well as inter-domain experiments on the LReID benchmark. Our
experiments demonstrate that DwoPP significantly outperforms the
state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_code
