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

Understanding Synonymous Referring Expressions via Contrastive Features