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Image captioning, a.k.a. "image-to-text," which generates descriptive textfrom given images, has been rapidly developing throughout the era of deeplearning. To what extent is the information in the original image preserved inthe descriptive text generated by an image captioner? To answer that question,we have performed experiments involving the classification of images fromdescriptive text alone, without referring to the images at all, and comparedresults with those from standard image-based classifiers. We have evaluateseveral image captioning models with respect to a disaster image classificationtask, CrisisNLP, and show that descriptive text classifiers can sometimesachieve higher accuracy than standard image-based classifiers. Further, we showthat fusing an image-based classifier with a descriptive text classifier canprovide improvement in accuracy.

Image Captioners Sometimes Tell More Than Images They See