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

Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval