FigureQA Dataset

Answering questions about a given image is a difficult task, requiring both an understanding of the image and the accompanying query. Microsoft research Montreal’s FigureQA dataset introduces a new visual reasoning task for research, specific to graphical plots and figures. The task comes with an additional twist: all of the questions are relational, requiring the comparison of several or all elements of the underlying plot.

Images are comprised on five types of figures commonly found in analytical documents. Fifteen question types were selected for the dataset concerning quantitative attributes in relational global and one-vs-one contexts. These include properties like minimum and maximum, greater and less than, medians, curve roughness, and area under the curve (AUC). All questions in the training and validation sets have either a yes or no answer.

For more details concerning the task, dataset, and our experiments, please read our paper: FigureQA: An Annotated Figure Dataset for Visual Reasoning (opens in new tab).

Click on a figure below to enlarge it and see some of its questions, answers, and bounding boxes.

Personne

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Samira Ebrahimi Kahou

Postdoctoral Researcher

McGill University, Mila

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Vincent Michalski

Research Intern

MILA

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Adam Atkinson

Software Developer

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Akos Kadar

Research Intern

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Yoshua Bengio

Founder and Scientific Director

Mila – Quebec AI Institute

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Mahmoud Adada

Principal Engineering Manager

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Rahul Mehrotra

Senior Program Manager