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Found 4 papers, 2 papers with code
ShapeGlot: Learning Language for Shape Differentiation
We also find that these models are amenable to zero-shot transfer learning to novel object classes (e. g. transfer from training on chairs to testing on lamps), as well as to real-world images drawn from furniture catalogs.
Learning to Refer to 3D Objects with Natural Language
We further show that a neural speaker that is `listener-aware' --- that plans its utterances according to how an imagined listener would interpret its words in context --- produces more discriminative referring expressions than an `listener-unaware' speaker, as measured by human performance in identifying the correct object.
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework.
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
When models are expressed as probabilistic programs, the models themselves are highly structured objects that can be used to derive annealing sequences that are more sensitive to domain structure.
