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Learning word meanings from images of natural scenes

. Children in their early life are faced with the challenge of learning the meanings of words from noisy and highly ambiguous contexts. The utterances that guide their learning are emitted in complex scenes where they need to identify which aspects of these scenes are related to which parts of the perceived utterances. One of the key challenges in computational modeling of the acquisition of word meanings is to provide rich enough representations of scenes that contain the similar sources of information and have similar statistical properties as naturally occurring data. In this paper we propose a novel computational model of cross-situational word learning that takes images of natural scenes paired with their descriptions as input and incrementally learns probabilistic associations between words and image features. We examine our model’s ability to learn word meanings from ambiguous and noisy data through a set of experiments. Our results show that the model is able to learn meaning representations that correlate with human similarity judgments of word pairs. Furthermore, we show that given an image of a natural scene our model is able to name words conceptually related to the image. RÉSUMÉ.