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.

本文提出了一个基于关系嵌入的表示重建网络（R3Net）来明确区分真实变化和大量混乱和无关变化，并引入了外部语法骨架预测器（SSP）来增强变化定位和字幕生成之间的语义交互，实验结果表明，所提出的方法在两个公共数据集上达到了最先进的水平。