  Generating natural questions from an image is a semantic task that requires
using visual and language modality to learn multimodal representations. Images
can have multiple visual and language contexts that are relevant for generating
questions namely places, captions, and tags. In this paper, we propose the use
of exemplars for obtaining the relevant context. We obtain this by using a
Multimodal Differential Network to produce natural and engaging questions. The
generated questions show a remarkable similarity to the natural questions as
validated by a human study. Further, we observe that the proposed approach
substantially improves over state-of-the-art benchmarks on the quantitative
metrics (BLEU, METEOR, ROUGE, and CIDEr).
