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The images are\nsynthetic, scientific-style figures from five classes: line plots, dot-line\nplots, vertical and horizontal bar graphs, and pie charts. We formulate our\nreasoning task by generating questions from 15 templates; questions concern\nvarious relationships between plot elements and examine characteristics like\nthe maximum, the minimum, area-under-the-curve, smoothness, and intersection.\nTo resolve, such questions often require reference to multiple plot elements\nand synthesis of information distributed spatially throughout a figure. To\nfacilitate the training of machine learning systems, the corpus also includes\nside data that can be used to formulate auxiliary objectives. In particular, we\nprovide the numerical data used to generate each figure as well as bounding-box\nannotations for all plot elements. We study the proposed visual reasoning task\nby training several models, including the recently proposed Relation Network as\na strong baseline. Preliminary results indicate that the task poses a\nsignificant machine learning challenge. We envision FigureQA as a first step\ntowards developing models that can intuitively recognize patterns from visual\nrepresentations of data.","url_abs":"http://arxiv.org/abs/1710.07300v2","url_pdf":"http://arxiv.org/pdf/1710.07300v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"figureqa-an-annotated-figure-dataset-for","repo_url":"https://github.com/vmichals/FigureQA-baseline","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"chart-question-answering","task_name":"Chart Question Answering"},{"task_slug":"relation-network","task_name":"Relation Network"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"},{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"}],"methods":[],"datasets_introduced":[{"slug":"figureqa","name":"FigureQA","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-question-answering-on-figureqa-test-1","task":"Visual Question Answering (VQA)","dataset":"FigureQA - test 1","model":"RN","rank_in_archive_order":3,"of":3,"metrics":{"1:1 Accuracy":"76.52"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.07300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.07300"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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