title: Vision: are models of object recognition catching up with the brain?
abstract: Object recognition has been a central yet elusive goal of computational vision. For many years, computer performance seemed highly deficient and unable to emulate the basic capabilities of the human recognition system. Over the past decade or so, computer scientists and neuroscientists have developed algorithms and systems-and models of visual cortex-that have come much closer to human performance in visual identification and categorization. In this personal perspective, we discuss the ongoing struggle of visual models to catch up with the visual cortex, identify key reasons for the relatively rapid improvement of artificial systems and models, and identify open problems for computational vision in this domain.
authors.affiliation: Department of Brain and Cognitive Sciences, McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts.
authors.forename: Tomaso
authors.initials: T
authors.lastname: Poggio
authors.forename: Shimon
authors.initials: S
authors.lastname: Ullman
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D014785:Vision, Ocular / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*
keywords: backprojection; feedforward; object recognition; supervised learning; visual cortex; visual models