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        <title>Informative Image Captioning with External Sources of Information</title>
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        <namePart type="given">Sanqiang</namePart>
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    <name type="personal">
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    <abstract>An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important “informativeness” dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1650</identifier>
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%0 Conference Proceedings
%T Informative Image Captioning with External Sources of Information
%A Zhao, Sanqiang
%A Sharma, Piyush
%A Levinboim, Tomer
%A Soricut, Radu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhao-etal-2019-informative
%X An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important “informativeness” dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative.
%R 10.18653/v1/P19-1650
%U https://aclanthology.org/P19-1650
%U https://doi.org/10.18653/v1/P19-1650
%P 6485-6494

Markdown (Informal)

[Informative Image Captioning with External Sources of Information](https://aclanthology.org/P19-1650) (Zhao et al., ACL 2019)

• Informative Image Captioning with External Sources of Information (Zhao et al., ACL 2019)

ACL

• Sanqiang Zhao, Piyush Sharma, Tomer Levinboim, and Radu Soricut. 2019. Informative Image Captioning with External Sources of Information. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6485–6494, Florence, Italy. Association for Computational Linguistics.