Abstract
We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark
for visuo-linguistic understanding. The iVQA task is to generate a question that corresponds to a given image and answer pair. Since the answers are less informative than the
questions, and the questions have less learnable bias, an
iVQA model needs to better understand the image to be successful than a VQA model. We pose question generation as
a multi-modal dynamic inference process and propose an
iVQA model that can gradually adjust its focus of attention
guided by both a partially generated question and the answer. For evaluation, apart from existing linguistic metrics,
we propose a new ranking metric. This metric compares
the ground truth question’s rank among a list of distractors, which allows the drawbacks of different algorithms
and sources of error to be studied. Experimental results show that our model can generate diverse, grammatically
correct and content correlated questions that match the given answer