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    + + \ No newline at end of file
    +
    +

    + Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning + Based on Visually Grounded Conversations
    + +

    +
    + + +
    +
    + +
    +
    + + +
    +
    + overview +
    +
    +
    +
    + +
    +
    +

    + + Links +

    + +
    +
    + + + + +
    +
    +

    + Overview +

    +

    + We introduce Affective Visual Dialog, an emotion explanation + and reasoning task as a testbed for research on understanding + the formation of emotions in visually-grounded + conversations. The task involves three skills: + (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction + and (3) Affective emotion explanation generation + based on the dialog. Our key contribution is the collection of a + large-scale dataset, dubbed AffectVisDial, consisting of 50K + 10-turn visually grounded dialogs as well as + concluding emotion attributions and dialog-informed textual emotion + explanations, resulting in a total of 27,180 + working hours. We explain our design decisions in collecting the + dataset and introduce the questioner and answerer tasks that are + associated with the participants in the + conversation. We train and demonstrate solid Affective Visual Dialog + baselines adapted from state-of-the-art models. Remarkably, + the responses generated by our models show promising emotional + reasoning abilities in response to visually grounded conversations +

    +
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    + + + +
    +
    +

    + Data Collection Process +

    + +
    + +
    +
    +
    + +
    +
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    + Qualitative Results +

    + +
    +
    + dialog_task +
    + Qualitative Examples of Dialog-Based Question Answering Task. Open the image in new tab for better view. + +
    +
    +
    + +
    + explanation_task +
    + Qualitative Examples of Emotion Explanation Generation Task. Open the image in new tab for better view. +
    +
    + +
    +
    +
    +
    +

    + Acknowledgements +

    +

    + This project is funded by KAUST + BAS/1/1685-01-01, SDAIA-KAUST Center of Excellence + in Data Science and Artificial Intelligence. The authors express + their appreciation to Jack Urbanek, Sirojiddin Karimov, and Umid Nejmatullayev + for their valuable assistance in data collection setup. Lastly, the authors extend their + gratitude to the diligent efforts of the Amazon Mechanical + Turkers, DeepenAI, and SmartOne teams, as their contributions were indispensable for the successful completion of + this work. +

    +
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    +

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