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 ap-proaches fail to capture continuous and concurrent shifts in classes anddomains, a critical capability for real-world deployment. This work in-troduces Online VIL (Online Versatile Incremental Learning),a novel scenario where class concepts and visual domains evolve simul-taneously online without explicit boundaries. To better adapt to thechallenges of such dynamic environments that more closely resemblereal-world conditions, we propose a novel framework TopFlow, Topologypreservation with Flow matching representation that contains two com-plementary mechanisms: Domain-agnostic Flow Matching (DFM)and Global Topology Preservation (GTP). DFM guides the modelto have domain-agnostic representations by integrating the geodesic flowkernel into contrastive learning. In contrast, GTP maintains the globalstructure of the feature space without explicitly storing past examples. Ourextensive experiments demonstrate that TopFlow e!ectively addresses thelimitations of existing methods within the Online VIL scenario, achievingstate-of-the-art performance in challenging Online VIL. The proposedmethods suggest potential directions for building continual learning sys-tems in realistic dynamic environments. Our implementation code isavailable at https://github.com/KU-VGI/Online-VIL.