Neural Caption Generation for News Images

Batra, V; He, Y; Vogiatzis, G

Abstract

Automatic caption generation of images has gained significant interest. It gives rise to a lot of interesting image-related applications. For example, it could help in image/video retrieval and management of vast amount of multimedia data available on the Internet. It could also help in development of tools that can aid visually impaired individuals in accessing multimedia content. In this paper, we particularly focus on news images and propose a methodology for automatically generating captions for news paper articles consisting of a text paragraph and an image. We propose several deep neural network architectures built upon Recurrent Neural Networks. Results on a BBC News dataset show that our proposed approach outperforms a traditional method based on Latent Dirichlet Allocation using both automatic evaluation based on BLEU scores and human evaluation.

Citation

Batra, V., He, Y., & Vogiatzis, G. (2018). Neural Caption Generation for News Images

• Acceptance Date: May 7, 2018
• Publication Date: May 12, 2018
• Journal: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
• Print ISSN: 979-10-95546-00-9
• Series Title: International Conference on Language Resources and Evaluation (LREC)
• Keywords: Recurrent Neural Networks, Image caption generation, Deep learning, Order Embedding
• Publisher URL: https://www.aclweb.org/anthology/volumes/L18-1/