  When humans describe images they tend to use combinations of nouns and
adjectives, corresponding to objects and their associated attributes
respectively. To generate such a description automatically, one needs to model
objects, attributes and their associations. Conventional methods require strong
annotation of object and attribute locations, making them less scalable. In
this paper, we model object-attribute associations from weakly labelled images,
such as those widely available on media sharing sites (e.g. Flickr), where only
image-level labels (either object or attributes) are given, without their
locations and associations. This is achieved by introducing a novel weakly
supervised non-parametric Bayesian model. Once learned, given a new image, our
model can describe the image, including objects, attributes and their
associations, as well as their locations and segmentation. Extensive
experiments on benchmark datasets demonstrate that our weakly supervised model
performs at par with strongly supervised models on tasks such as image
description and retrieval based on object-attribute associations.
