  A great deal of progress has been made in image captioning, driven by
research into how to encode the image using pre-trained models. This includes
visual encodings (e.g. image grid features or detected objects) and more
recently textual encodings (e.g. image tags or text descriptions of image
regions). As more advanced encodings are available and incorporated, it is
natural to ask: how to efficiently and effectively leverage the heterogeneous
set of encodings? In this paper, we propose to regard the encodings as
augmented views of the input image. The image captioning model encodes each
view independently with a shared encoder efficiently, and a contrastive loss is
incorporated across the encoded views in a novel way to improve their
representation quality and the model's data efficiency. Our proposed
hierarchical decoder then adaptively weighs the encoded views according to
their effectiveness for caption generation by first aggregating within each
view at the token level, and then across views at the view level. We
demonstrate significant performance improvements of +5.6% CIDEr on MS-COCO and
+12.9% CIDEr on Flickr30k compared to state of the arts, and conduct rigorous
analyses to demonstrate the importance of each part of our design.
