Semantic compositional networks for visual captioning

Published

Conference Paper

© 2017 IEEE. A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is used to compose the parameters in a long short-term memory (LSTM) network. The SCN extends each weight matrix of the LSTM to an ensemble of tag-dependent weight matrices. The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag. In addition to captioning images, we also extend the SCN to generate captions for video clips. We qualitatively analyze semantic composition in SCNs, and quantitatively evaluate the algorithm on three benchmark datasets: COCO, Flickr30k, and Youtube2Text. Experimental results show that the proposed method significantly outperforms prior state-of-the-art approaches, across multiple evaluation metrics.

Full Text

Duke Authors

Cited Authors

  • Gan, Z; Gan, C; He, X; Pu, Y; Tran, K; Gao, J; Carin, L; Deng, L

Published Date

  • November 6, 2017

Published In

  • Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017

Volume / Issue

  • 2017-January /

Start / End Page

  • 1141 - 1150

International Standard Book Number 13 (ISBN-13)

  • 9781538604571

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2017.127

Citation Source

  • Scopus