  A common use of language is to refer to visually present objects. Modelling
it in computers requires modelling the link between language and perception.
The "words as classifiers" model of grounded semantics views words as
classifiers of perceptual contexts, and composes the meaning of a phrase
through composition of the denotations of its component words. It was recently
shown to perform well in a game-playing scenario with a small number of object
types. We apply it to two large sets of real-world photographs that contain a
much larger variety of types and for which referring expressions are available.
Using a pre-trained convolutional neural network to extract image features, and
augmenting these with in-picture positional information, we show that the model
achieves performance competitive with the state of the art in a reference
resolution task (given expression, find bounding box of its referent), while,
as we argue, being conceptually simpler and more flexible.
