{"id":"https://openalex.org/W2932319663","doi":"https://doi.org/10.1109/ijcnn.2019.8851830","title":"FigureNet : A Deep Learning model for Question-Answering on Scientific Plots","display_name":"FigureNet : A Deep Learning model for Question-Answering on Scientific Plots","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2932319663","doi":"https://doi.org/10.1109/ijcnn.2019.8851830","mag":"2932319663"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1806.04655","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103843662","display_name":"Revanth Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Revanth Reddy","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085453702","display_name":"Rahul Ramesh","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul Ramesh","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041777363","display_name":"Ameet Deshpande","orcid":"https://orcid.org/0000-0001-9885-0385"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ameet Deshpande","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050036814","display_name":"Mitesh M. Khapra","orcid":"https://orcid.org/0009-0008-3687-9922"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mitesh M. Khapra","raw_affiliation_strings":["Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India","Indian Institute of Technology  Madras"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Indian Institute of Technology, Chennai, Madras, India","institution_ids":["https://openalex.org/I24676775"]},{"raw_affiliation_string":"Indian Institute of Technology  Madras","institution_ids":["https://openalex.org/I24676775"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24676775"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7743946313858032},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6919461488723755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6735933423042297},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6484546065330505},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5866458415985107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5630847215652466},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5492874383926392},{"id":"https://openalex.org/keywords/plot","display_name":"Plot (graphics)","score":0.5363416075706482},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5201159715652466},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5096564292907715},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4948047995567322},{"id":"https://openalex.org/keywords/bar-chart","display_name":"Bar chart","score":0.42041948437690735},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.42021456360816956},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41846805810928345},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2015710473060608},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15236178040504456},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12420105934143066},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08671566843986511}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7743946313858032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919461488723755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6735933423042297},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6484546065330505},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5866458415985107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5630847215652466},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5492874383926392},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.5363416075706482},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5201159715652466},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5096564292907715},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4948047995567322},{"id":"https://openalex.org/C61122496","wikidata":"https://www.wikidata.org/wiki/Q1124595","display_name":"Bar chart","level":2,"score":0.42041948437690735},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.42021456360816956},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41846805810928345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2015710473060608},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15236178040504456},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12420105934143066},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08671566843986511},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851830","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1806.04655","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.04655","pdf_url":"https://arxiv.org/pdf/1806.04655","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2932319663","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1806.04655.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1806.04655","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1806.04655","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1806.04655","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.04655","pdf_url":"https://arxiv.org/pdf/1806.04655","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2932319663.pdf","grobid_xml":"https://content.openalex.org/works/W2932319663.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1544827683","https://openalex.org/W1575833922","https://openalex.org/W1861492603","https://openalex.org/W2064675550","https://openalex.org/W2117499988","https://openalex.org/W2138857742","https://openalex.org/W2163605009","https://openalex.org/W2531409750","https://openalex.org/W2546696630","https://openalex.org/W2561715562","https://openalex.org/W2766732270","https://openalex.org/W2962965405","https://openalex.org/W2963224792","https://openalex.org/W2963717374","https://openalex.org/W2963907629","https://openalex.org/W2963921132","https://openalex.org/W2963981422","https://openalex.org/W2964118342","https://openalex.org/W2964308564","https://openalex.org/W6632455782","https://openalex.org/W6634232107","https://openalex.org/W6639102338","https://openalex.org/W6677604277","https://openalex.org/W6679434410","https://openalex.org/W6684191040","https://openalex.org/W6728184133","https://openalex.org/W6729399837","https://openalex.org/W6738893770","https://openalex.org/W6740674931","https://openalex.org/W6745915114","https://openalex.org/W6754450524"],"related_works":["https://openalex.org/W2977761974","https://openalex.org/W3039727666","https://openalex.org/W3173675718","https://openalex.org/W3212452301","https://openalex.org/W2900714358","https://openalex.org/W2956506767","https://openalex.org/W2823604564","https://openalex.org/W1992130648","https://openalex.org/W2965572476","https://openalex.org/W2904366287","https://openalex.org/W3153624450","https://openalex.org/W3153839026","https://openalex.org/W2782124856","https://openalex.org/W2806507179","https://openalex.org/W2496096353","https://openalex.org/W2556088399","https://openalex.org/W2914045865","https://openalex.org/W3131603048","https://openalex.org/W3194951844","https://openalex.org/W3057365024"],"abstract_inverted_index":{"Deep":[0],"Learning":[1],"has":[2],"managed":[3],"to":[4,18,28,59],"push":[5],"boundaries":[6],"in":[7,21],"a":[8,37,53,70,119],"wide":[9],"variety":[10],"of":[11,15,46,73,127],"tasks.":[12],"One":[13],"area":[14],"interest":[16],"is":[17,123],"tackle":[19],"problems":[20],"reasoning":[22,44],"and":[23,68,88,97],"understanding,":[24],"with":[25,101,118],"an":[26,125],"aim":[27],"emulate":[29],"human":[30],"intelligence.":[31],"In":[32],"this":[33,116],"work,":[34],"we":[35],"describe":[36],"deep":[38],"learning":[39],"model":[40,80],"that":[41,57,122],"addresses":[42],"the":[43,65,82,107],"task":[45],"question-answering":[47],"on":[48,81,115],"categorical":[49],"plots.":[50],"We":[51,77],"introduce":[52],"novel":[54],"architecture":[55],"FigureNet,":[56],"learns":[58],"identify":[60],"various":[61],"plot":[62],"elements,":[63],"quantify":[64],"represented":[66],"values":[67],"determine":[69],"relative":[71],"ordering":[72],"these":[74],"statistical":[75],"values.":[76],"test":[78],"our":[79],"FigureQA":[83],"dataset":[84],"which":[85],"provides":[86],"images":[87],"accompanying":[89],"questions":[90],"for":[91],"scientific":[92],"plots":[93],"like":[94],"bar":[95],"graphs":[96],"pie":[98],"charts,":[99],"augmented":[100],"rich":[102],"annotations.":[103],"Our":[104],"approach":[105],"outperforms":[106],"state-of-the-art":[108],"Relation":[109],"Networks":[110],"baseline":[111],"by":[112],"approximately":[113],"7%":[114],"dataset,":[117],"training":[120],"time":[121],"over":[124],"order":[126],"magnitude":[128],"lesser.":[129]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}