Auto-selected Knowledge Adapters for Lifelong Person Re-identification

TL;DR

This paper introduces AdalReID, a lifelong person re-identification framework that uses knowledge adapters and auto-selection to effectively learn and preserve knowledge across multiple datasets, outperforming existing methods.

Contribution

The paper proposes a novel lifelong learning framework with knowledge adapters and an auto-selection mechanism to improve domain adaptation in person re-identification.

Findings

Outperforms state-of-the-art methods by 10-20% mAP on multiple datasets.

Effectively preserves knowledge across different domains.

Enhances generalization ability through adaptive adapter selection.

Abstract

Lifelong Person Re-Identification (LReID) extends traditional ReID by requiring systems to continually learn from non-overlapping datasets across different times and locations, adapting to new identities while preserving knowledge of previous ones. Existing approaches, either rehearsal-free or rehearsal-based, still suffer from the problem of catastrophic forgetting since they try to cram diverse knowledge into one fixed model. To overcome this limitation, we introduce a novel framework AdalReID, that adopts knowledge adapters and a parameter-free auto-selection mechanism for lifelong learning. Concretely, we incrementally build distinct adapters to learn domain-specific knowledge at each step, which can effectively learn and preserve knowledge across different datasets. Meanwhile, the proposed auto-selection strategy adaptively calculates the knowledge similarity between the input set and the adapters. On the one hand, the appropriate adapters are selected for the inputs to process ReID, and on the other hand, the knowledge interaction and fusion between adapters are enhanced to improve the generalization ability of the model. Extensive experiments are conducted to demonstrate the superiority of our AdalReID, which significantly outperforms SOTAs by about 10$\sim$20\% mAP on both seen and unseen domains.