  Image captioning has recently demonstrated impressive progress largely owing
to the introduction of neural network algorithms trained on curated dataset
like MS-COCO. Often work in this field is motivated by the promise of
deployment of captioning systems in practical applications. However, the
scarcity of data and contexts in many competition datasets renders the utility
of systems trained on these datasets limited as an assistive technology in
real-world settings, such as helping visually impaired people navigate and
accomplish everyday tasks. This gap motivated the introduction of the novel
VizWiz dataset, which consists of images taken by the visually impaired and
captions that have useful, task-oriented information. In an attempt to help the
machine learning computer vision field realize its promise of producing
technologies that have positive social impact, the curators of the VizWiz
dataset host several competitions, including one for image captioning. This
work details the theory and engineering from our winning submission to the 2020
captioning competition. Our work provides a step towards improved assistive
image captioning systems.
