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Visual referring expression recognition is a challenging task that requiresnatural language understanding in the context of an image. We criticallyexamine RefCOCOg, a standard benchmark for this task, using a human study andshow that 83.7% of test instances do not require reasoning on linguisticstructure, i.e., words are enough to identify the target object, the word orderdoesn't matter. To measure the true progress of existing models, we split thetest set into two sets, one which requires reasoning on linguistic structureand the other which doesn't. Additionally, we create an out-of-distributiondataset Ref-Adv by asking crowdworkers to perturb in-domain examples such thatthe target object changes. Using these datasets, we empirically show thatexisting methods fail to exploit linguistic structure and are 12% to 23% lowerin performance than the established progress for this task. We also propose twomethods, one based on contrastive learning and the other based on multi-tasklearning, to increase the robustness of ViLBERT, the current state-of-the-artmodel for this task. Our datasets are publicly available athttps://github.com/aws/aws-refcocog-adv

Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions