MapQA：一个用于在Choropleth图上进行问答的数据集
MapQA: A Dataset for Question Answering on Choropleth Maps
摘要：
Choropleth地图是特定区域表格数据的常见视觉表示，并用于许多不同的场所（报纸，文章等）。这些地图是人类可读的，但在试图为屏幕阅读器、分析或其他相关任务提取数据时，通常很难处理。视觉问答（VQA）的最新研究已经研究了人工生成的图表（ChartQA）上的问答，例如条形图、折线图和饼图。然而，很少有工作注意到理解地图;一般的VQA模型，和ChartQA模型，当被要求执行这项任务时，遭受。为了促进和鼓励这一领域的研究，我们提出了MapQA，这是一个包含约60 K地图图像的约800 K问答对的大规模数据集。我们的任务测试了不同层次的地图理解，从关于地图样式的表面问题到需要对底层数据进行推理的复杂问题。我们提出了独特的挑战MapQA挫败最强大的基线算法设计的ChartQA和一般VQA任务。我们还提出了一种新的算法，视觉多输出数据提取基于QA（V-MODEQA）的MapQA。V-MODEQA利用多输出模型从地图图像中提取底层结构化数据，然后对提取的数据进行推理。我们的实验结果表明，V-MODEQA有更好的整体性能和鲁棒性比国家的最先进的ChartQA和VQA算法，通过捕捉独特的属性在地图问答。
英文摘要：
Choropleth maps are a common visual representation for region-specific tabular data and are used in a number of different venues (newspapers, articles, etc). These maps are human-readable but are often challenging to deal with when trying to extract data for screen readers, analyses, or other related tasks. Recent research into Visual-Question Answering (VQA) has studied question answering on human-generated charts (ChartQA), such as bar, line, and pie charts. However, little work has paid attention to understanding maps; general VQA models, and ChartQA models, suffer when asked to perform this task. To facilitate and encourage research in this area, we present MapQA, a large-scale dataset of ~800K question-answer pairs over ~60K map images. Our task tests various levels of map understanding, from surface questions about map styles to complex questions that require reasoning on the underlying data. We present the unique challenges of MapQA that frustrate most strong baseline algorithms designed for ChartQA and general VQA tasks. We also present a novel algorithm, Visual Multi-Output Data Extraction based QA (V-MODEQA) for MapQA. V-MODEQA extracts the underlying structured data from a map image with a multi-output model and then performs reasoning on the extracted data. Our experimental results show that V-MODEQA has better overall performance and robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by capturing the unique properties in map question answering.
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