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Image captioning systems have made substantial progress, largely due to theavailability of curated datasets like Microsoft COCO or Vizwiz that haveaccurate descriptions of their corresponding images. Unfortunately, scarceavailability of such cleanly labeled data results in trained algorithmsproducing captions that can be terse and idiosyncratically specific to detailsin the image. We propose a new technique, cooperative distillation thatcombines clean curated datasets with the web-scale automatically extractedcaptions of the Google Conceptual Captions dataset (GCC), which can have poordescriptions of images, but is abundant in size and therefore provides a richvocabulary resulting in more expressive captions.

Alleviating Noisy Data in Image Captioning with Cooperative Distillation