图像描述生成器有时比它们所见的图像透露更多信息
Image Captioners Sometimes Tell More Than Images They See
AI总结：
本文探究图像描述生成器生成的文本对原始图像信息的保留程度，实验表明仅凭描述文本分类有时优于图像分类，且融合两者可提升准确率。
摘要：
图像描述生成，又称“图像到文本”，是从给定图像生成描述性文本的技术，在整个深度学习时代得到了快速发展。原始图像中的信息在图像描述生成器生成的描述性文本中保留到了何种程度？为了回答这个问题，我们进行了实验，仅凭描述性文本对图像进行分类，完全不参考图像本身，并将结果与基于图像的标准分类器结果进行比较。我们针对灾难图像分类任务CrisisNLP评估了多个图像描述生成模型，结果表明，描述性文本分类器有时能达到比标准图像分类器更高的准确率。此外，我们展示了将基于图像的分类器与描述性文本分类器融合可以进一步提高准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
