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Grounding referring expressions aims to locate in an image an object referredto by a natural language expression. The linguistic structure of a referringexpression provides a layout of reasoning over the visual contents, and it isoften crucial to align and jointly understand the image and the referringexpression. In this paper, we propose a scene graph guided modular network(SGMN), which performs reasoning over a semantic graph and a scene graph withneural modules under the guidance of the linguistic structure of theexpression. In particular, we model the image as a structured semantic graph,and parse the expression into a language scene graph. The language scene graphnot only decodes the linguistic structure of the expression, but also has aconsistent representation with the image semantic graph. In addition toexploring structured solutions to grounding referring expressions, we alsopropose Ref-Reasoning, a large-scale real-world dataset for structuredreferring expression reasoning. We automatically generate referring expressionsover the scene graphs of images using diverse expression templates andfunctional programs. This dataset is equipped with real-world visual contentsas well as semantically rich expressions with different reasoning layouts.Experimental results show that our SGMN not only significantly outperformsexisting state-of-the-art algorithms on the new Ref-Reasoning dataset, but alsosurpasses state-of-the-art structured methods on commonly used benchmarkdatasets. It can also provide interpretable visual evidences of reasoning. Dataand code are available at https://github.com/sibeiyang/sgmn

Graph-Structured Referring Expression Reasoning in The Wild