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.
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Notes
- 1.
Note that although we use the continuous skip-gram model as an example to illustrate our framework, the similar framework can be developed on the basis of any other word embedding models.
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Acknowledgments
This work was supported by National Key Research and Development Program of China (Grant No. 2016YFB1000902), National Program on Key Basic Research Project (973 Program, Grant No. 2013CB329600), and National Natural Science Foundation of China (Grant No. 61472040).
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Yang, H., Song, D., Liao, L. (2018). Image Captioning with Relational Knowledge. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_43
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