We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous efforts, we are facing a multi-modal problem where the
language output (answer) is conditioned on visual and natural language input
(image and question). Our approach Neural-Image-QA doubles the performance of
the previous best approach on this problem. We provide additional insights into
the problem by analyzing how much information is contained only in the language
part for which we provide a new human baseline. To study human consensus, which
is related to the ambiguities inherent in this challenging task, we propose two
novel metrics and collect additional answers which extends the original DAQUAR
dataset to DAQUAR-Consensus.

利用图像表示和自然语言处理的最新进展，我们提出了一种端到端形式的 Neural-Image-QA 解决方案来回答真实世界图像上的问题，我们面临一个多模态问题，其中语言输出（答案）是基于视觉和自然语言输入（图像和问题）的，我们的方法可以使已有研究成果的表现效果成倍增加，并提供了关于该问题的额外见解，包括研究人类共识的两个新指标并扩展了初始数据集到 DAQUAR-Consensus。