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Despite significant progress in a variety of vision-and-language problems,developing a method capable of asking intelligent, goal-oriented questionsabout images is proven to be an inscrutable challenge. Towards this end, wepropose a Deep Reinforcement Learning framework based on three new intermediaterewards, namely goal-achieved, progressive and informativeness that encouragethe generation of succinct questions, which in turn uncover valuableinformation towards the overall goal. By directly optimizing for questions thatwork quickly towards fulfilling the overall goal, we avoid the tendency ofexisting methods to generate long series of insane queries that add littlevalue. We evaluate our model on the GuessWhat?! dataset and show that theresulting questions can help a standard Guesser identify a specific object inan image at a much higher success rate.

Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards