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Diverse Image Captioning with Grounded Style

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Pattern Recognition (DAGM GCPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13024))

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Abstract

Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to express a certain global style in the caption, e.g. positive or negative, however without taking into account the stylistic content of the visual scene. To address this shortcoming, we first analyze the limitations of current stylized captioning datasets and propose COCO attribute-based augmentations to obtain varied stylized captions from COCO annotations. Furthermore, we encode the stylized information in the latent space of a Variational Autoencoder; specifically, we leverage extracted image attributes to explicitly structure its sequential latent space according to different localized style characteristics. Our experiments on the Senticap and COCO datasets show the ability of our approach to generate accurate captions with diversity in styles that are grounded in the image.

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Acknowledgement

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 866008).

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Authors and Affiliations

  1. Department of Computer Science, TU Darmstadt, Darmstadt, Germany

    Franz Klein, Shweta Mahajan & Stefan Roth

  2. hessian.AI, Darmstadt, Germany

    Stefan Roth

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  1. Franz Klein
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  2. Shweta Mahajan
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  3. Stefan Roth
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Corresponding author

Correspondence to Shweta Mahajan .

Editor information

Editors and Affiliations

  1. Fraunhofer IAIS, Sankt Augustin, Germany

    Christian Bauckhage

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Alexander Schwing

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Klein, F., Mahajan, S., Roth, S. (2021). Diverse Image Captioning with Grounded Style. In: Bauckhage, C., Gall, J., Schwing, A. (eds) Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science(), vol 13024. Springer, Cham. https://doi.org/10.1007/978-3-030-92659-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-92659-5_27

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