Lifelong Person Re-Identification via Adaptive Knowledge Accumulation
This addresses the challenge of incremental learning across multiple domains in person re-identification, which is important for real-world applications like surveillance, though it is incremental as it builds on existing lifelong learning concepts.
The paper tackles the problem of person re-identification in lifelong learning scenarios where domains change continuously, proposing an Adaptive Knowledge Accumulation framework that reduces catastrophic forgetting and generalizes to unseen domains, achieving a 5.8% mAP improvement over competitors.
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.