  Image captioning, a.k.a. "image-to-text," which generates descriptive text
from given images, has been rapidly developing throughout the era of deep
learning. To what extent is the information in the original image preserved in
the descriptive text generated by an image captioner? To answer that question,
we have performed experiments involving the classification of images from
descriptive text alone, without referring to the images at all, and compared
results with those from standard image-based classifiers. We have evaluate
several image captioning models with respect to a disaster image classification
task, CrisisNLP, and show that descriptive text classifiers can sometimes
achieve higher accuracy than standard image-based classifiers. Further, we show
that fusing an image-based classifier with a descriptive text classifier can
provide improvement in accuracy.
