iVQA：逆向视觉问答
iVQA: Inverse Visual Question Answering
摘要：
我们提出了视觉问答（iVQA）的逆问题，并探讨其作为视觉语言理解基准的适用性。iVQA任务是生成与给定图像和答案对相对应的问题。由于答案的信息量比问题少，而且问题的可学习偏差也少，因此iVQA模型需要比VQA模型更好地理解图像才能成功。我们提出的问题生成作为一个多模态的动态推理过程，并提出了一个iVQA模型，可以逐步调整其注意力的焦点，引导部分生成的问题和答案。对于评估，除了现有的语言指标，我们提出了一个新的排名指标。该指标比较了地面实况问题在干扰项列表中的排名，这允许研究不同算法和错误来源的缺点。实验结果表明，我们的模型可以生成多样化的，语法正确的和内容相关的问题，匹配给定的答案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
