  Describing images with text is a fundamental problem in vision-language
research. Current studies in this domain mostly focus on single image
captioning. However, in various real applications (e.g., image editing,
difference interpretation, and retrieval), generating relational captions for
two images, can also be very useful. This important problem has not been
explored mostly due to lack of datasets and effective models. To push forward
the research in this direction, we first introduce a new language-guided image
editing dataset that contains a large number of real image pairs with
corresponding editing instructions. We then propose a new relational speaker
model based on an encoder-decoder architecture with static relational attention
and sequential multi-head attention. We also extend the model with dynamic
relational attention, which calculates visual alignment while decoding. Our
models are evaluated on our newly collected and two public datasets consisting
of image pairs annotated with relationship sentences. Experimental results,
based on both automatic and human evaluation, demonstrate that our model
outperforms all baselines and existing methods on all the datasets.
