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Online Learning of Objects and Faces in an Integrated Biologically Motivated Architecture

We present a biologically motivated integrated vision sys- tem that is capable of online learning of several objects and faces in a unified representation. The training is unconstrained in the sense that arbitrary objects can be freely presented in front of a stereo camera system and labeled by speech input. We combine biological principles such as appearance-based representation in topographical feature detec- tion hierarchies and context-driven transfer between different levels of object memory. The learning is driven by interactively sharing atten- tion between user and system. It is fully online and avoids an artificial separation of the interaction into training and test phases.