  The ability of intelligent agents to play games in human-like fashion is
popularly considered a benchmark of progress in Artificial Intelligence.
Similarly, performance on multi-disciplinary tasks such as Visual Question
Answering (VQA) is considered a marker for gauging progress in Computer Vision.
In our work, we bring games and VQA together. Specifically, 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. We analyze the resulting dataset
and present many interesting findings therein. To mimic Pictionary-style
guessing, we subsequently 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.
