DSKC: Domain Style Modeling with Adaptive Knowledge Consolidation for Exemplar-free Lifelong Person Re-Identification

TL;DR

DSKC introduces a novel framework for lifelong person re-identification that dynamically models domain styles and consolidates knowledge without rehearsal or distillation, improving anti-forgetting and generalization.

Contribution

It proposes a domain-style encoder and a unified knowledge consolidation mechanism for exemplar-free lifelong person re-identification.

Findings

Outperforms state-of-the-art methods in multiple training orders.

Enhances model's generalization and anti-forgetting capabilities.

Achieves superior performance without rehearsal or distillation.

Abstract

Lifelong Person Re-identification (LReID) aims to continuously match individuals across camera views from sequential data streams. Existing LReID methods often ignore domain-specific style awareness and unified knowledge consolidation, which are crucial for mitigating forgetting when adapting to new information. We propose DSKC, a novel rehearsal-free and distillation-free framework for LReID. DSKC designs a domain-style encoder (DSE) to dynamically model domain-specific styles, and a unified knowledge consolidation (UKC) mechanism to adaptively integrate instance-level representations with domain-specific style into a cross-domain unified representation. By leveraging unified representation as a bridge, DSKC explicitly models inter-domain associations at both instance and domain levels to enhance anti-forgetting and generalization. Experimental results demonstrate that our DSKC outperforms state-of-the-art methods in two training orders and enhances the model's strong performance. Our code is available at https://github.com/LiuShiBen/DKUA.