Visual-semantic embedding is an interesting research topic because it is
useful for various tasks, such as visual question answering (VQA), image-text
retrieval, image captioning, and scene graph generation. In this paper, we
focus on zero-shot image retrieval using sentences as queries and present a
survey of the technological trends in this area. First, we provide a
comprehensive overview of the history of the technology, starting with a
discussion of the early studies of image-to-text matching and how the
technology has evolved over time. In addition, a description of the datasets
commonly used in experiments and a comparison of the evaluation results of each
method are presented. We also introduce the implementation available on github
for use in confirming the accuracy of experiments and for further improvement.
We hope that this survey paper will encourage researchers to further develop
their research on bridging images and languages.

本文关注零样本图像检索，使用句子作为查询条件，概述了该领域的技术趋势，包括图像与文本匹配的历史、常用的数据集和方法的评估结果， 并介绍了 Github 上的实现，旨在鼓励研究者进一步发掘语言与图像之间的联系。