In recent years, visual question answering (VQA) has become topical. The
premise of VQA's significance as a benchmark in AI, is that both the image and
textual question need to be well understood and mutually grounded in order to
infer the correct answer. However, current VQA models perhaps `understand' less
than initially hoped, and instead master the easier task of exploiting cues
given away in the question and biases in the answer distribution. In this paper
we propose the inverse problem of VQA (iVQA). The iVQA task is to generate a
question that corresponds to a given image and answer pair. We propose a
variational iVQA model that can generate diverse, grammatically correct and
content correlated questions that match the given answer. Based on this model,
we show that iVQA is an interesting benchmark for visuo-linguistic
understanding, and a more challenging alternative to VQA because an iVQA model
needs to understand the image better to be successful. As a second
contribution, we show how to use iVQA in a novel reinforcement learning
framework to diagnose any existing VQA model by way of exposing its belief set:
the set of question-answer pairs that the VQA model would predict true for a
given image. This provides a completely new window into what VQA models
`believe' about images. We show that existing VQA models have more erroneous
beliefs than previously thought, revealing their intrinsic weaknesses.
Suggestions are then made on how to address these weaknesses going forward.

本篇文章提出了 iVQA 问题，利用变分模型来生成多样化、语法正确、与答案相关性较强的问题，并将其作为一种新型的视觉 - 语言理解基准测试。同时，还提出了使用强化学习框架来诊断现有 VQA 模型，揭示其内在缺陷的方法。

逆向视觉问答：一个新的基准和 VQA 诊断工具

Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis  Tool

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.

本文提出了逆向视觉问答（iVQA）问题，并探讨其作为视觉语言理解基准的适用性。我们提出了一种 iVQA 模型，可以逐渐调整其注意力焦点，以便更好地理解给定的图像。实验结果表明，我们的模型可以生成多样性、语法正确且内容相关的问题，与给定的答案相匹配。