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arXiv · 1708.02760

Learning to Disambiguate by Asking Discriminative Questions

Yining Li·Chen Huang·Xiaoou Tang·Chen-Change Loy
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Abstract

The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of generating discriminative questions to help disambiguate visual instances. Our work can be seen as a complement and new extension to the rich research studies on image captioning and question answering. We introduce the first large-scale dataset with over 10,000 carefully annotated images-question tuples to facilitate benchmarking. In particular, each tuple consists of a pair of images and 4.6 discriminative questions (as positive samples) and 5.9 non-discriminative questions (as negative samples) on average. In addition, we present an effective method for visual discriminative question generation. The method can be trained in a weakly supervised manner without discriminative images-question tuples but just existing visual question answering datasets. Promising results are shown against representative baselines through quantitative evaluations and user studies.

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Yining Li, Chen Huang, Xiaoou Tang, Chen-Change Loy. 2017-08-09. Learning to Disambiguate by Asking Discriminative Questions. https://arxiv.org/abs/1708.02760

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