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Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time
Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing approaches fail to capture continuous and concurrent shifts in classes and domains, a critical capability for real-world deployment. This work introduces Online VIL (Online
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