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.

本研究探讨了图像字幕生成器将原始图像信息转化为文本时信息的损失程度，并通过对比使用标准图像分类器和仅使用文本分类器进行的灾难图像分类任务的结果来评估了几种图像字幕生成模型，表明在某些情况下文本分类器可以比标准图像分类器实现更高的准确度，同时还表明将图像分类器与文本分类器进行融合可以提高准确性。