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Motivated by the abundance of images labeled only by their captions, we construct tree-structured multiscale conditional random fields capable of performing semisupervised learning. We show that such caption-only data can in fact increase pixel-level accuracy at test time. In addition, we compare two kinds of tree: the standard one with pairwise potentials, and one based on noisy-or potentials, which better matches the semantics of the recursive partitioning used to create the tree.doi:10.1109/crv.2011.56 dblp:conf/crv/DuvenaudMM11 fatcat:r6qlr7ee4fg7zoq6fqhxyrtszy