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Learning About Multiple Objects in Images: Factorial Learning without Factorial Search
2002
Neural Information Processing Systems
We consider data which are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations
dblp:conf/nips/WilliamsT02
fatcat:2t6wv4c5dbhcjiixsd6zxy6kem