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    <titleInfo>
        <title>Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences</title>
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    <name type="personal">
        <namePart type="given">Xudong</namePart>
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    <name type="personal">
        <namePart type="given">Khushboo</namePart>
        <namePart type="family">Mehra</namePart>
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    <name type="personal">
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    <abstract>Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00553</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
%A Hong, Xudong
%A Sayeed, Asad
%A Mehra, Khushboo
%A Demberg, Vera
%A Schiele, Bernt
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F hong-etal-2023-visual-writing
%X Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.
%R 10.1162/tacl_a_00553
%U https://aclanthology.org/2023.tacl-1.33
%U https://doi.org/10.1162/tacl_a_00553
%P 565-581

Markdown (Informal)

[Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences](https://aclanthology.org/2023.tacl-1.33) (Hong et al., TACL 2023)

• Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences (Hong et al., TACL 2023)

ACL

• Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, and Bernt Schiele. 2023. Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences. Transactions of the Association for Computational Linguistics, 11:565–581.