Improving image captioning by leveraging knowledge graphs

Yimin Zhou, Yiwei Sun, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. We compare the performance of image captioning systems that as measured by CIDEr-D, a performance measure that is explicitly designed for evaluating image captioning systems, on several benchmark data sets such as MS COCO. The results of our experiments show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-293
Number of pages11
ISBN (Electronic)9781728119755
DOIs
StatePublished - Mar 4 2019
Event19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
CountryUnited States
CityWaikoloa Village
Period1/7/191/11/19

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Improving image captioning by leveraging knowledge graphs'. Together they form a unique fingerprint.

Cite this