{"id":"https://openalex.org/W4414359354","doi":"https://doi.org/10.24963/ijcai.2025/543","title":"Multimodal Inference with Incremental Tabular Attributes","display_name":"Multimodal Inference with Incremental Tabular Attributes","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359354","doi":"https://doi.org/10.24963/ijcai.2025/543"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/543","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088095069","display_name":"Xinda Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinda Chen","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079555419","display_name":"Zhen Xing","orcid":"https://orcid.org/0000-0001-6407-0321"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Xing","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035125034","display_name":"Zixian Zhang","orcid":"https://orcid.org/0000-0001-7348-0832"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixian Zhang","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048621299","display_name":"Weimin Tan","orcid":"https://orcid.org/0000-0001-7677-4772"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weimin Tan","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101804797","display_name":"Bo Yan","orcid":"https://orcid.org/0000-0001-5692-3486"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Yan","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4878","last_page":"4886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.9027000069618225,"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/T12031","display_name":"Speech and dialogue systems","score":0.9027000069618225,"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/inference","display_name":"Inference","score":0.6438000202178955},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5289000272750854},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.47929999232292175},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.47189998626708984},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.45730000734329224},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4453999996185303},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.38749998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7620999813079834},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6438000202178955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6219000220298767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5627999901771545},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/543","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Learning":[1],"with":[2,88,128,142,146,198],"visual":[3,144],"and":[4,10,53,127,136],"tabular":[5,32,90,104,123,166],"modalities":[6],"has":[7],"become":[8],"more":[9,11],"popular":[12],"nowadays,":[13],"especially":[14],"in":[15,51,75,103,171,186],"the":[16,21,31,37,107,118,129,139,143,175],"healthcare":[17],"area.":[18],"Due":[19],"to":[20,47,94,99,116,132,149],"adaptation":[22],"of":[23,40,121],"new":[24,27,66,84,192],"equipment":[25],"or":[26],"factors":[28],"being":[29],"introduced,":[30],"modality":[33,105,145],"keeps":[34],"changing.":[35],"However,":[36],"standard":[38],"process":[39],"training":[41,52,193],"multimodal":[42,76,86,97,196],"AI":[43],"models":[44,98],"requires":[45],"tables":[46,74],"have":[48],"fixed":[49],"columns":[50],"inference;":[54],"thus,":[55],"it":[56],"is":[57],"not":[58],"suitable":[59],"for":[60,70,195],"handling":[61,72],"dynamically":[62],"changed":[63],"tables.":[64,199],"Therefore,":[65],"methods":[67],"are":[68],"needed":[69],"efficiently":[71],"such":[73],"learning.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81,177],"introduce":[82],"a":[83,113,191],"task,":[85],"inference":[87,108],"incremental":[89,101,122,140,165],"attributes,":[91,167],"which":[92],"aims":[93],"enable":[95],"trained":[96],"leverage":[100],"attributes":[102,124,131,141],"during":[106],"stage":[109],"efficiently.":[110],"We":[111],"implement":[112],"specialized":[114],"encoder":[115],"disentangle":[117],"latent":[119],"representation":[120],"inside":[125],"itself":[126],"old":[130],"reduce":[133],"information":[134,151],"redundancy":[135],"further":[137],"align":[138],"consistency":[147],"loss":[148],"improve":[150],"richness.":[152],"Experimental":[153],"results":[154],"across":[155],"five":[156],"public":[157],"datasets":[158],"show":[159],"that":[160,180],"our":[161,181],"method":[162,182],"effectively":[163],"utilizes":[164],"achieving":[168],"state-of-the-art":[169],"performance":[170,185],"general":[172],"scenarios.":[173],"Beyond":[174],"inference,":[176],"also":[178],"find":[179],"achieved":[183],"better":[184],"fully":[187],"supervised":[188],"settings,":[189],"evoking":[190],"style":[194],"learning":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}