  For human children as well as machine learning systems, a key challenge in
learning a word is linking the word to the visual phenomena it describes. We
explore this aspect of word learning by using the performance of computer
vision systems as a proxy for the difficulty of learning a word from visual
cues. We show that the age at which children acquire different categories of
words is correlated with the performance of visual classification and
captioning systems, over and above the expected effects of word frequency. The
performance of the computer vision systems is correlated with human judgments
of the concreteness of words, which are in turn a predictor of children's word
learning, suggesting that these models are capturing the relationship between
words and visual phenomena.
