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.
CNet-NIC REF incorporates knowledge graphs to augment information extracted from images for captioning.
Improving Image Captioning by Leveraging Knowledge Graphs
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science