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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Playing games in a human-like manner is a key benchmark for Artificial Intelligence (AI) progress.
    • Pictionary, a popular social word-guessing game, presents unique challenges for AI due to its reliance on visual interpretation and sequential information.

    Purpose of the Study:

    • To introduce the first computational model specifically designed for the game of Pictionary.
    • To develop an AI system capable of generating human-like guess-words from evolving sketch data.
    • To analyze a new dataset, Sketch-QA, created for evaluating Pictionary-style AI.

    Main Methods:

    • Introduction of Sketch-QA, a novel guessing task using incrementally accumulated sketch stroke sequences as visual input.
    • Development of a deep neural network model to generate guess-words in response to temporally evolving sketches.
    • Evaluation of the model on the large-scale Sketch-QA dataset and comparison with baseline methods.
    • Conducting a Visual Turing Test to compare AI-generated guesses with human guesses.

    Main Results:

    • The proposed deep neural model successfully generates guess-words from sketches, mimicking human-like guessing patterns.
    • The model exhibits human-like errors, enhancing its ability to replicate human performance in Pictionary.
    • Analysis of the Sketch-QA dataset revealed interesting findings regarding human guessing strategies.
    • The Visual Turing Test indicated promising results for the AI's ability to generate plausible guesses.

    Conclusions:

    • The developed computational model shows significant promise for Pictionary and similar visual guessing games.
    • The approach advances AI's capability in understanding and generating responses based on dynamic visual input.
    • The Sketch-QA task and dataset provide a valuable resource for future research in AI game playing and visual reasoning.