  This paper addresses the sensitivity of neural image caption generators to
their visual input. A sensitivity analysis and omission analysis based on image
foils is reported, showing that the extent to which image captioning
architectures retain and are sensitive to visual information varies depending
on the type of word being generated and the position in the caption as a whole.
We motivate this work in the context of broader goals in the field to achieve
more explainability in AI.
