Amplitude-aware Domain Style Replay for Lifelong Person Re-identification

Lifelong Person Re-identification (LReID) focuses on continuously adapting to new domains over time while preserving knowledge from previously seen domains, particularly under the domain incremental learning setting. The major challenge of LReID is catastrophic forgetting, typically caused by large domain shifts during training. To address this, we propose a novel Amplitude-aware Domain Style Replay (ADSR) framework, which introduces a Fourier-based Style Transfer (FST) mechanism to generate synthetic data that reflects the style of previously encountered domains. These proxy images help retain prior knowledge without the need to store actual past data. Our method transfers stylistic information-mainly encoded in the amplitude spectrum-from old domains to new ones, creating old-stylized images that preserve the content of new domain data while adopting the visual style of earlier domains. To further boost generalization, we design a Self-Stylization Normalization (SSN) module that adapts the current domain's style distribution, making the model more robust to stylistic variations. Additionally, we introduce a Multi-Granularity Transfer (MGT) module that uses K-Means clustering to extract multiple representative style features from each domain, enabling compact yet comprehensive storage and replay of domain-specific information. Extensive experiments on multiple LReID benchmarks show that ADSR achieves superior performance over existing approaches, effectively reducing forgetting and improving cross-domain generalization. Our code is available at https://github.com/cclong8/MM2025-ADSR.