Online Continual Learning for Robust Indoor Object Recognition
Umberto Michieli,Mete Ozay,Umberto Michieli,Mete Ozay
Vision systems mounted on home robots need to interact with unseen classes in changing environments. Robots have limited computational resources, labelled data and storage capability. These requirements pose some unique challenges: models should adapt without forgetting past knowledge in a data- and parameter-efficient way. We characterize the problem as few-shot (FS) online continual learning (OC...
