  Change captioning is to use a natural language sentence to describe the
fine-grained disagreement between two similar images. Viewpoint change is the
most typical distractor in this task, because it changes the scale and location
of 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 and
irrelevant changes. Specifically, a relation-embedded module is first devised
to explore potential changed objects in the large amount of clutter. Then,
based on the semantic similarities of corresponding locations in the two
images, a representation reconstruction module (RRM) is designed to learn the
reconstruction representation and further model the difference representation.
Besides, we introduce a syntactic skeleton predictor (SSP) to enhance the
semantic interaction between change localization and caption generation.
Extensive experiments show that the proposed method achieves the
state-of-the-art results on two public datasets.
