@inproceedings{ae69043b3c284a5fa16a5609a47e153c,
title = "Image captioning with relational knowledge",
abstract = "People have learned extensive relational knowledge from daily life. This is one of the facts that enables human to describe the information from images easily. In this paper, we propose a novel framework called Image Captioning with Relational Knowledge (ICRK) that combines relational knowledge with image captioning model and utilizes relational knowledge to strengthen the learning process of representing words. As more precise syntactic and semantic word relationships were learned, the image captioning model acquires more semantic features that help to generate more accurate image descriptions. Experiments on several benchmark datasets, using automatic evaluation metrics, have all demonstrated that our model can significantly improve the quality of image captioning.",
keywords = "Image captioning, Relational knowledge, Word embedding",
author = "Huan Yang and Dandan Song and Lejian Liao",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97310-4_43",
language = "English",
isbn = "9783319973098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "378--386",
editor = "Xin Geng and Byeong-Ho Kang",
booktitle = "PRICAI 2018",
address = "Germany",
}
