Class-Domain Incremental Learning with Extensible Multi-Center Modeling

Incremental learning aims to continuously learn from dynamic data streams without suffering from catastrophic forgetting. However, existing methods may fail in challenging scenarios where class and domain spaces expand simultaneously, struggling to learn continually and stably from fragmented data streams. To address this challenge, we propose EMCift (Extensible Multi-Center Modeling with Dual-Path Drift Harmonization), a novel model that treats heterogeneous data across domains and semantic spaces as co-existing multi-view observations, enabling the construction of global knowledge from local observations. Our approach organizes extensible multi-center prototypes as a tree hierarchy, allowing the global class representation across different domains to grow organically from fragmented local data. To sustain this dynamic topology, we introduce a dual-path drift harmonization strategy that balances structural stability with adaptive plasticity. Specifically, one path captures essential updates to enable prototype evolution, while the other path leverages a conditional adversarial mechanism to robustly initialize domains, effectively preventing catastrophic forgetting caused by class confusion. Extensive experiments on three datasets demonstrate the state-of-the-art performance of our proposed model in the class-domain incremental learning scenario. Code will be released upon acceptance.