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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models
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Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models

机译：手绘对象草图的单词式猜测：数据集，分析和深度网络模型

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摘要

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Sketch-QA involves asking a fixed question ("What object is being drawn?") and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.
机译：智能代理以类似于人的方式玩游戏的能力被普遍认为是人工智能进步的基准。在我们的工作中，我们引入了第一个针对Pictionary的计算模型，Pictionary是一种流行的猜词社交游戏。我们首先介绍Sketch-QA，这是一个猜测任务。 Sketch-QA以Pictionary为风格，使用递增累积的草图笔划序列作为可视数据。 Sketch-QA涉及询问一个固定的问题（“正在绘制什么对象？”），并从人类猜测者那里收集开放式猜测词。我们分析了结果数据集，并在其中提出了许多有趣的发现。为了模仿Pictionary风格的猜测，我们提出了一种深层神经模型，该模型可响应随时间变化的人类绘制的对象草图而生成猜测词。我们的模型甚至在猜测时会犯类似人的错误，从而放大了人的模仿因素。我们对通过Sketch-QA任务生成的大规模猜词数据集评估模型，并与各种基准进行比较。我们还进行了视觉图灵测试，以获取人类对人类和我们的模型产生的猜词的印象。实验结果证明了我们的方法对于Pictionary和类似主题游戏的希望。

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