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One-shot learning of object categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented bydoi:10.1109/tpami.2006.79 pmid:16566508 fatcat:wq66umghvrbrljqnbr7fwkpf7a