{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classification-regression-for-chart","title":"Classification-Regression for Chart Comprehension","arxiv_id":"2111.14792","date":"2021-11-29","proceeding":null,"authors":["Matan Levy","Rami Ben-Ari","Dani Lischinski"],"abstract":"Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most existing CQA datasets and models are based on simplifying assumptions that often enable surpassing human performance. In this work, we address this outcome and propose a new model that jointly learns classification and regression. Our language-vision setup uses co-attention transformers to capture the complex real-world interactions between the question and the textual elements. We validate our design with extensive experiments on the realistic PlotQA dataset, outperforming previous approaches by a large margin, while showing competitive performance on FigureQA. Our model is particularly well suited for realistic questions with out-of-vocabulary answers that require regression.","url_abs":"https://arxiv.org/abs/2111.14792v2","url_pdf":"https://arxiv.org/pdf/2111.14792v2.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":"classification-regression-for-chart","repo_url":"https://github.com/levymsn/cqa-crct","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"chart-question-answering","task_name":"Chart Question Answering"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/chart-question-answering-on-plotqa","task":"Chart Question Answering","dataset":"PlotQA","model":"CRCT","rank_in_archive_order":5,"of":6,"metrics":{"1:1 Accuracy":"55.7"},"uses_additional_data":false},{"leaderboard":"/sota/visual-question-answering-on-figureqa-test-1","task":"Visual Question Answering (VQA)","dataset":"FigureQA - test 1","model":"CRCT","rank_in_archive_order":2,"of":3,"metrics":{"1:1 Accuracy":"94.23"},"uses_additional_data":false},{"leaderboard":"/sota/visual-question-answering-on-plotqa-d1","task":"Visual Question Answering (VQA)","dataset":"PlotQA-D1","model":"CRCT","rank_in_archive_order":2,"of":4,"metrics":{"1:1 Accuracy":"76.94"},"uses_additional_data":false},{"leaderboard":"/sota/visual-question-answering-on-plotqa-d2","task":"Visual Question Answering (VQA)","dataset":"PlotQA-D2","model":"CRCT","rank_in_archive_order":2,"of":4,"metrics":{"1:1 Accuracy":"34.44"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.14792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.14792"}},"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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