A Deep Learning Approach to Automatic Caption Generation for News Images

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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.

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  • Neural Caption Generation for News Images

    Accepted author manuscript, 869 KB, PDF-document

    Embargo ends: 1/01/50

Details

Publication date20 Dec 2018
Publication titleThe 11th International Conference on Language Resources and Evaluation (LREC)
Original languageEnglish
EventThe 11th International Conference on Language Resources and Evaluation (LREC) - Miyazaki, Japan
Duration: 7 May 201812 May 2018
http://lrec2018.lrec-conf.org/en/

Conference

ConferenceThe 11th International Conference on Language Resources and Evaluation (LREC)
CountryJapan
CityMiyazaki
Period7/05/1812/05/18
Internet address

    Keywords

  • Recurrent Neural Networks, Image caption generation, Deep learning, Order Embedding

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