2016's WAC model, building the training set for each word w as follows: all visual objects in a corpus that have been referred to as w are used as positive instances, the remaining objects as negative instances.
the classifiers learn to predict referential appropriateness for individual words based on the visual features of the objects they refer to, in isolation of other words.