Unsupervised language learning for discovered visual concepts

Abstract

Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for concept formation in infants, we argue that the availability of pre-lexical concepts (learned from image sequences) leads to considerable computational efficiency in word acquisition. Key to the process is a model of bottom-up visual attention in dynamic scenes. We have used existing work in background-foreground segmentation, multiple object tracking, object discovery and trajectory clustering to form object category and action concepts. The set of acquired concepts under visual attentive focus are then correlated with contemporaneous commentary to learn the grounded semantics of words and multi-word phrasal concatenations from the narrative. We demonstrate that even based on mere 5 minutes of video segments, a number of rudimentary visual concepts can be discovered. When these concepts are associated with unedited English commentary, we observe that several words emerge - more than 60% of the concepts discovered from the video are associated with correct language labels. Thus, the computational model imitates the beginning of language comprehension, based on attentional parsing of the visual data. Finally, the emergence of multi-word phrasal concatenations, a precursor to syntax, is observed where there are more salient referents than single words. © 2013 Springer-Verlag.