With the continuous expansion of intelligent surveillance networks, lifelong
person re-identification (LReID) has received widespread attention, pursuing
the need of self-evolution across different domains. However, existing LReID
studies accumulate knowledge with the assumption that people would not change
their clothes. In this paper, we propose a more practical task, namely lifelong
person re-identification with hybrid clothing states (LReID-Hybrid), which
takes a series of cloth-changing and cloth-consistent domains into account
during lifelong learning. To tackle the challenges of knowledge granularity
mismatch and knowledge presentation mismatch that occurred in LReID-Hybrid, we
take advantage of the consistency and generalization of the text space, and
propose a novel framework, dubbed $Teata$, to effectively align, transfer and
accumulate knowledge in an "image-text-image" closed loop. Concretely, to
achieve effective knowledge transfer, we design a Structured Semantic Prompt
(SSP) learning to decompose the text prompt into several structured pairs to
distill knowledge from the image space with a unified granularity of text
description. Then, we introduce a Knowledge Adaptation and Projection strategy
(KAP), which tunes text knowledge via a slow-paced learner to adapt to
different tasks without catastrophic forgetting. Extensive experiments
demonstrate the superiority of our proposed $Teata$ for LReID-Hybrid as well as
on conventional LReID benchmarks over advanced methods.

基于 $Teata$ 框架，提出一种考虑混合服饰情况的终身个体重识别任务（LReID-Hybrid），通过结构语义提示学习和知识适应与投影策略来解决知识粒度和表示的不匹配问题。实验证明，所提出的方法在 LReID-Hybrid 和传统 LReID 基准上具有优势。