{"id":"https://openalex.org/W4399435816","doi":"https://doi.org/10.1145/3652583.3658103","title":"CGI-MRE: A Comprehensive Genetic-Inspired Model For Multimodal Relation Extraction","display_name":"CGI-MRE: A Comprehensive Genetic-Inspired Model For Multimodal Relation Extraction","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399435816","doi":"https://doi.org/10.1145/3652583.3658103"},"language":"en","primary_location":{"id":"doi:10.1145/3652583.3658103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658103","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658103","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658103","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010866485","display_name":"Pengfei Wei","orcid":"https://orcid.org/0000-0003-4479-8248"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengfei Wei","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4479-8248","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101487795","display_name":"Zhaokang Huang","orcid":"https://orcid.org/0009-0005-0872-0111"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaokang Huang","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-0872-0111","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027774672","display_name":"H. F. Ouyang","orcid":"https://orcid.org/0009-0002-2815-1524"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongjun Ouyang","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0002-2815-1524","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087432572","display_name":"Qintai Hu","orcid":"https://orcid.org/0000-0003-3199-2631"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qintai Hu","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3199-2631","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073040865","display_name":"Bi Zeng","orcid":"https://orcid.org/0000-0001-8596-8333"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bi Zeng","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8596-8333","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073429705","display_name":"Guang Feng","orcid":"https://orcid.org/0009-0005-4450-597X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Feng","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0005-4450-597X","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5010866485"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.9934,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.79136317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"524","last_page":"532"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9994999766349792,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9994999766349792,"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/T10028","display_name":"Topic Modeling","score":0.9993000030517578,"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":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.7007628679275513},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6203374862670898},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5967551469802856},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5881849527359009},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5338718891143799},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.48580431938171387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35190486907958984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7007628679275513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6203374862670898},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5967551469802856},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5881849527359009},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5338718891143799},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.48580431938171387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35190486907958984},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652583.3658103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658103","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658103","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3652583.3658103","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658103","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658103","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399435816.pdf"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2162590473","https://openalex.org/W2251135946","https://openalex.org/W2619383789","https://openalex.org/W2788647998","https://openalex.org/W2798298921","https://openalex.org/W2949922292","https://openalex.org/W2963021258","https://openalex.org/W3176858586","https://openalex.org/W3190695173","https://openalex.org/W3207972321","https://openalex.org/W4288045405","https://openalex.org/W4382317693","https://openalex.org/W4390479123"],"related_works":["https://openalex.org/W2601157893","https://openalex.org/W2373006798","https://openalex.org/W2131735617","https://openalex.org/W2056912418","https://openalex.org/W2123759770","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2049538278","https://openalex.org/W2886173746"],"abstract_inverted_index":{"Multimodal":[0,62],"Relation":[1,63],"Extraction":[2,64,73,119,133],"(MRE)":[3],"is":[4,218],"an":[5,52],"entity":[6],"relationship":[7],"extraction":[8],"method":[9,55],"based":[10],"on":[11,231],"multimodal":[12],"information.":[13],"Most":[14],"existing":[15],"MRE":[16,54,223],"methods":[17],"have":[18,164],"two":[19,69],"issues:":[20],"1)":[21],"Weak":[22],"cross-modal":[23],"correlation":[24],"and":[25,78,92,100,108,144,163,174,197,220,243],"poor":[26],"semantic":[27,166],"consistency.":[28,167],"2)":[29],"They":[30],"do":[31],"not":[32],"achieve":[33],"text-guided":[34,193,204,219],"fusion":[35],"of":[36,43,68,246],"different":[37,128,161],"modalities,":[38,129],"resulting":[39],"in":[40,127,141,178],"excessive":[41],"introduction":[42],"image":[44,226],"noise.":[45],"To":[46],"address":[47],"these":[48,112],"issues,":[49],"we":[50,87,114,145,171],"propose":[51],"innovative":[53],"inspired":[56],"by":[57],"genetics-A":[58],"Comprehensive":[59],"Genetic-Inspired":[60],"For":[61,111],"(CGI-MRE).":[65],"It":[66],"consists":[67],"main":[70],"modules:":[71],"Gene":[72,118,132,149],"And":[74],"Recombination":[75,150],"Module":[76,81],"(GERM)":[77],"Text-Guided":[79],"Fusion":[80],"(TGFM).":[82],"In":[83,168],"the":[84,89,176,179,192,203,225,232,240],"GERM":[85],"module,":[86,170],"regard":[88],"text":[90],"features":[91,94,156,177,181],"visual":[93],"as":[95],"a":[96,116,130,148],"feature":[97,103],"body":[98,104],"respectively,":[99],"decompose":[101],"each":[102,142],"into":[105],"common":[106,124],"sub-features":[107],"unique":[109,138],"sub-features.":[110],"sub-features,":[113],"designed":[115],"Common":[117],"Mechanism":[120,134,151],"(CGEM)":[121],"to":[122,136,153,185,190,201,213,222],"extract":[123,137,175,215],"advantageous":[125,139],"genes":[126,140],"Unique":[131],"(UGEM)":[135],"modality,":[143],"finally":[146],"use":[147,188,210],"(GRM)":[152],"obtain":[154,202],"recombinant":[155,227],"that":[157,182,217,236],"highly":[158],"correlated":[159],"with":[160],"modalities":[162],"strong":[165],"TGFM":[169],"organically":[172],"fuse":[173],"recombined":[180],"are":[183],"beneficial":[184,221],"MRE.":[186],"We":[187,208],"gate":[189],"adjust":[191],"original":[194],"attention":[195,199,206],"score":[196,200,212],"pooling":[198],"saliency":[205],"score.":[207],"can":[209],"this":[211],"strictly":[214],"information":[216],"from":[224],"feature.":[228],"Experimental":[229],"results":[230],"MNRE":[233],"dataset":[234],"show":[235],"our":[237],"model":[238],"outperforms":[239],"state-of-the-art":[241],"performance":[242],"achieves":[244],"F1-score":[245],"84.62%.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
