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Our survival depends on recognizing everything around us: how we can act on objects, and how they can act on us. Likewise, intelligent machines must interpret each object within a task context. For example, an automated vehicle needs to correctly respond if suddenly faced with a large boulder, a wandering moose, or a child on a tricycle. Such robust ability requires a broad view of recognition, with many new challenges. Computer vision researchers are accustomed to building algorithms that search through image collections for a target object or category. But how do we make computers that can deal with the world as it comes? How can we build systems that can recognize any animal or vehicle, rather than just a few select basic categories? What can be said about novel objects? How do we approach the problem of learning about many related categories? We have recently begun grappling with these questions, exploring shared representations that facilitate visual learning and prediction for new object categories. In this talk, I will discuss our recent efforts and future challenges to enable broader and more flexible recognition systems.
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