  Referring expression comprehension aims to localize objects identified by
natural language descriptions. This is a challenging task as it requires
understanding of both visual and language domains. One nature is that each
object can be described by synonymous sentences with paraphrases, and such
varieties in languages have critical impact on learning a comprehension model.
While prior work usually treats each sentence and attends it to an object
separately, we focus on learning a referring expression comprehension model
that considers the property in synonymous sentences. To this end, we develop an
end-to-end trainable framework to learn contrastive features on the image and
object instance levels, where features extracted from synonymous sentences to
describe the same object should be closer to each other after mapping to the
visual domain. We conduct extensive experiments to evaluate the proposed
algorithm on several benchmark datasets, and demonstrate that our method
performs favorably against the state-of-the-art approaches. Furthermore, since
the varieties in expressions become larger across datasets when they describe
objects in different ways, we present the cross-dataset and transfer learning
settings to validate the ability of our learned transferable features.
