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Change captioning is to use a natural language sentence to describe thefine-grained disagreement between two similar images. Viewpoint change is themost typical distractor in this task, because it changes the scale and locationof the objects and overwhelms the representation of real change. In this paper,we propose a Relation-embedded Representation Reconstruction Network (R$^3$Net)to explicitly distinguish the real change from the large amount of clutter andirrelevant changes. Specifically, a relation-embedded module is first devisedto explore potential changed objects in the large amount of clutter. Then,based on the semantic similarities of corresponding locations in the twoimages, a representation reconstruction module (RRM) is designed to learn thereconstruction representation and further model the difference representation.Besides, we introduce a syntactic skeleton predictor (SSP) to enhance thesemantic interaction between change localization and caption generation.Extensive experiments show that the proposed method achieves thestate-of-the-art results on two public datasets.

R$^3$Net:Relation-embedded Representation Reconstruction Network for Change Captioning