Game of sketches: Deep recurrent models of pictionary-style word guessing

Sarvadevabhatla, RK and Surya, S and Mittal, T and Venkatesh Babu, R (2018) Game of sketches: Deep recurrent models of pictionary-style word guessing. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2 - 7 February 2018, New Orleans, pp. 7331-7338.

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Abstract

The ability of machine-based agents to play games in human-like fashion is considered a benchmark of progress in AI. In this paper, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn 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.

Item Type:	Conference Paper
Publication:	32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Publisher:	AAAI press
Additional Information:	The copyright for this article belongs to the AAAI press.
Keywords:	Artificial intelligence, Computational model; Human impressions; Neural modeling; Question Answering Task; Recurrent models; Social games; Stroke sequences; Turing tests, Drawing (graphics)
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	18 Aug 2022 06:06
Last Modified:	18 Aug 2022 06:06
URI:	https://eprints.iisc.ac.in/id/eprint/75954

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