ML Anthology

DVQA: Understanding Data Visualizations via Question Answering

Kushal Kafle, Brian Price, Scott Cohen, Christopher Kanan
CVPR 2018
doi:10.1109/CVPR.2018.00592 /cvpr/2018/kafle2018cvpr-dvqa/

Abstract

Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them. Existing methods fail when faced with even minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract numeric and semantic information from vast quantities of bar charts found in scientific publications, Internet articles, business reports, and many other areas.

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Cite

Text

Kafle et al. "DVQA: Understanding Data Visualizations via Question Answering." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00592

Markdown

[Kafle et al. "DVQA: Understanding Data Visualizations via Question Answering." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/kafle2018cvpr-dvqa/) doi:10.1109/CVPR.2018.00592

BibTeX

@inproceedings{kafle2018cvpr-dvqa,
  title     = {{DVQA: Understanding Data Visualizations via Question Answering}},
  author    = {Kafle, Kushal and Price, Brian and Cohen, Scott and Kanan, Christopher},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00592},
  url       = {https://mlanthology.org/cvpr/2018/kafle2018cvpr-dvqa/}
}