ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models

Sarvadevabhatla, Ravi Kiran and Surya, Shiv and Mittal, Trisha and Babu, R Venkatesh (2020) Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models. In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 42 (1). pp. 221-231.

[img] PDF
IEEE_tra_pat_ana_mac_int_42-1_221-231_2020.pdf - Published Version
Restricted to Registered users only

Download (3MB)
Official URL: https://dx.doi.org/10.1109/TPAMI.2018.2877996


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.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
Keywords: Games; Computational modeling; Visualization; Task analysis; Knowledge discovery; Robots; Deep learning; pictionary; games; sketch; visual question answering
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 07 Jan 2020 09:55
Last Modified: 07 Jan 2020 09:55
URI: http://eprints.iisc.ac.in/id/eprint/64281

Actions (login required)

View Item View Item