Abstract. A sizable body of work on relative attributes provides evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields improvements in a variety of vision
tasks. In this paper, we show how emerging ideas in graph neural networks can yield a solution to various problems that broadly fall under
relative attribute learning. Our main idea is the observation that relative attribute learning naturally benefits from exploiting the graph of
dependencies among the different relative attributes of images, especially
when only partial ordering is provided at training time. We use message
passing to perform end to end learning of the image representations,
their relationships as well as the interplay between different attributes.
Our experiments show that this simple framework is effective in achieving
competitive accuracy with specialized methods for both relative attribute
learning and binary attribute prediction, while relaxing the requirements
on the training data and/or the number of parameters, or both