{"id":"https://openalex.org/W7163638868","doi":"https://doi.org/10.48550/arxiv.2606.05455","title":"Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification","display_name":"Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163638868","doi":"https://doi.org/10.48550/arxiv.2606.05455"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05455","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.05455","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025874707","display_name":"Feixiang Zhou","orcid":"https://orcid.org/0000-0003-4939-9393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Feixiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137975339","display_name":"Jianyang Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Jianyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114206333","display_name":"Zhuangzhi Gao","orcid":"https://orcid.org/0009-0000-4339-8088"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Zhuangzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137969612","display_name":"Qinkai Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Qinkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137982585","display_name":"Fu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114160787","display_name":"Yuheng Fan","orcid":"https://orcid.org/0009-0009-0611-0589"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138010287","display_name":"Jing Li (10611)","orcid":"https://orcid.org/0000-0002-0013-9139"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009437198","display_name":"Zheheng Jiang","orcid":"https://orcid.org/0000-0003-1401-7615"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zheheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137954439","display_name":"Yitian Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yitian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137926852","display_name":"Yanda Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Yanda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137973720","display_name":"He Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137968703","display_name":"Gregory Y. H. Lip","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lip, Gregory Y. H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081186911","display_name":"Yalin Zheng","orcid":"https://orcid.org/0000-0002-7873-0922"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yalin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.32330000400543213,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.32330000400543213,"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":0.2069000005722046,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11779999732971191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.7121999859809875},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.6747999787330627},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6668000221252441},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5956000089645386},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5817999839782715},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4641000032424927},{"id":"https://openalex.org/keywords/semantic-heterogeneity","display_name":"Semantic heterogeneity","score":0.37290000915527344},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.37049999833106995},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.36719998717308044}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8158000111579895},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7121999859809875},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6747999787330627},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6668000221252441},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5956000089645386},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4812000095844269},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3837999999523163},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.37290000915527344},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.37049999833106995},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3273000121116638},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05455","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":"doi:10.48550/arxiv.2606.05455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05455","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"missing-modality":[1,202],"problem":[2],"poses":[3],"a":[4,12,93,126,143,160],"significant":[5],"challenge":[6,27],"in":[7,45],"image-tabular":[8,187],"multimodal":[9],"learning":[10],"across":[11,155],"wide":[13],"range":[14],"of":[15,81,192],"multimedia":[16],"applications,":[17],"including":[18],"product":[19],"understanding,":[20],"recommendation":[21],"systems,":[22],"and":[23,40,49,59,70,74,107,110,113,138,152,171,176],"medical":[24],"diagnosis.":[25],"This":[26],"is":[28],"particularly":[29],"pronounced":[30],"when":[31],"the":[32,79,190,197],"two":[33],"modalities":[34],"are":[35],"highly":[36],"heterogeneous,":[37],"as":[38],"images":[39],"tabular":[41],"attributes":[42],"differ":[43],"substantially":[44],"their":[46],"semantic":[47,73,140,153],"granularity":[48],"data":[50],"distributions.":[51],"Existing":[52],"methods":[53],"learn":[54],"modality-invariant":[55],"representations":[56],"through":[57],"disentanglement":[58,117],"alignment":[60,141],"over":[61],"global":[62,175],"token-averaged":[63],"features,":[64],"capturing":[65],"only":[66],"coarse":[67],"cross-modal":[68],"consistency":[69,154],"overlooking":[71],"fine-grained":[72,97,150],"distributional":[75,151],"misalignment,":[76],"which":[77],"hampers":[78],"exploitation":[80],"complementary":[82],"cues":[83],"under":[84,200],"missing":[85],"modalities.":[86,156],"To":[87],"address":[88],"this,":[89],"we":[90,124],"propose":[91,125],"DFPL,":[92],"novel":[94],"framework":[95],"for":[96,179],"prototype":[98,145,177],"learning.":[99],"Specifically,":[100],"Shared-Specific":[101],"Prototype":[102],"Modeling":[103],"(SSPM)":[104],"extracts":[105],"compact":[106],"diverse":[108,186],"shared":[109,169],"modality-specific":[111,172],"prototypes,":[112],"further":[114,158],"performs":[115],"prototype-level":[116,135],"to":[118,166,196],"suppress":[119],"redundant":[120],"intra-modality":[121],"correlations.":[122],"Additionally,":[123],"Prototype-guided":[127],"Fine-grained":[128],"Alignment":[129],"(PFA)":[130],"module":[131,165],"that":[132],"jointly":[133],"enforces":[134],"distribution":[136],"matching":[137],"prototype-to-class":[139],"within":[142],"unified":[144],"space,":[146],"thereby":[147],"preserving":[148],"both":[149],"We":[157],"introduce":[159],"Class-aware":[161],"Multi-scale":[162],"Aggregation":[163],"(CMA)":[164],"adaptively":[167],"aggregate":[168],"semantics":[170],"characteristics":[173],"from":[174],"levels":[178],"robust":[180],"predictions.":[181],"Extensive":[182],"experiments":[183],"on":[184],"three":[185],"benchmarks":[188],"demonstrate":[189],"superiority":[191],"our":[193],"method":[194],"compared":[195],"previous":[198],"approaches":[199],"various":[201],"settings.":[203],"Code":[204],"will":[205],"be":[206],"made":[207],"publicly":[208],"available.":[209]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}
