Unified Stability and Plasticity for Lifelong Person Re-Identification in Cloth-Changing and Cloth-Consistent Scenarios

Lifelong person re-identification (LReID) is developed for dynamic domains where domain distribution is constantly changing due to climate changes, scene changes, etc., and the data can only be collected for a specific scenario over a period of time. With the development of ReID, the issue of clothing changes has also attracted attention. Clothing change itself should be solved more from the perspective of lifelong learning because pedestrians may wear new clothes and the time span of their appearance can be long which can also cause domain changes. Meanwhile, it is difficult to know in advance whether a pedestrian is cloth-changing or cloth-consistent. However, current LReID tasks overlook these issues. To overcome these limitations, we introduce a more practical LReID task, denoted as L4C-ReID (Lifelong Person Re-Identification in Cloth-Changing and Cloth-Consistent Scenarios). This novel task empowers ReID models capable of adapting to incrementally encountered cloth-changing and cloth-consistent domains without prior knowledge of the scenario type and generalizing to unseen domains. A key challenge supposed to be fixed for LReID is the stability-plasticity dilemma. Unlike current LReID methods, which implement plasticity and stability by two contradictory loss items to achieve a sub-optimal balance, we propose an effective scheme termed Unified Stability and Plasticity (USP) that unifies these seemingly disparate concepts to achieve both harmoniously. Taking inspiration from the cognitive processes in the human brain, we decompose the cognitive processes into two independent processes: knowledge representation and knowledge operation. We then design a Knowledge Representation and Operation (KRO) framework to represent and operate the knowledge like the human brain which can better learn new knowledge and consolidate old knowledge to coordinate plasticity and stability. Additionally, we introduce Plasticizing with Stability (PWS) to generalize and optimize the learned knowledge, which integrates the implementation of plasticity and stability into one common objective item to achieve both simultaneously. To simulate the L4C-ReID setup, we gather existing cloth-changing and cloth-consistent datasets to provide a new benchmark. Extensive experiments conducted both on this new benchmark and previous benchmarks established for previous LReID setup, demonstrate the superiority of our method.