  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
incrementally learning new information, which is caused by task-level domain
gaps and limited old task datasets. Existing methods based on CNN backbone are
insufficient to explore the representation of each instance from different
perspectives, limiting model performance on limited old task datasets and new
task datasets. Unlike these methods, we propose a Diverse Representations
Embedding (DRE) framework that first explores a pure transformer 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 overlapping representations generated by transformer-based backbone,
obtaining rich and discriminative representations for each instance to improve
adaptive ability of LReID. Based on the processed diverse representations, we
propose Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the
task-level layout by introducing the adjustment model and the learner model. KU
strategy enhances the adaptive learning ability of learner models for new
information under the adjustment model prior, and KP strategy preserves old
knowledge operated by representation-level alignment and logit-level
supervision in limited old task datasets while guaranteeing the adaptive
learning information capacity of the LReID model. Compared to state-of-the-art
methods, our method achieves significantly improved performance in holistic,
large-scale, and occluded datasets.
