Learning to Disambiguate by Asking Discriminative Questions
Yining Li, Chen Huang, Xiaoou Tang, Chen Change Loy
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.
BibTeX
@inproceedings{iccv2017_learningtodisamb,
title = {Learning to Disambiguate by Asking Discriminative Questions},
author = {Yining Li and Chen Huang and Xiaoou Tang and Chen Change Loy},
booktitle = {ICCV 2017},
year = {2017}
}