  Image captioning systems have made substantial progress, largely due to the
availability of curated datasets like Microsoft COCO or Vizwiz that have
accurate descriptions of their corresponding images. Unfortunately, scarce
availability of such cleanly labeled data results in trained algorithms
producing captions that can be terse and idiosyncratically specific to details
in the image. We propose a new technique, cooperative distillation that
combines clean curated datasets with the web-scale automatically extracted
captions of the Google Conceptual Captions dataset (GCC), which can have poor
descriptions of images, but is abundant in size and therefore provides a rich
vocabulary resulting in more expressive captions.
