  Recently, attention-based encoder-decoder models have been used extensively
in image captioning. Yet there is still great difficulty for the current
methods to achieve deep image understanding. In this work, we argue that such
understanding requires visual attention to correlated image regions and
semantic attention to coherent attributes of interest. Based on the
Transformer, to perform effective attention, we explore image captioning from a
cross-modal perspective and propose the Global-and-Local Information
Exploring-and-Distilling approach that explores and distills the source
information in vision and language. It globally provides the aspect vector, a
spatial and relational representation of images based on caption contexts,
through the extraction of salient region groupings and attribute collocations,
and locally extracts the fine-grained regions and attributes in reference to
the aspect vector for word selection. Our Transformer-based model achieves a
CIDEr score of 129.3 in offline COCO evaluation on the COCO testing set with
remarkable efficiency in terms of accuracy, speed, and parameter budget.
