{"id":"https://openalex.org/W4386644692","doi":"https://doi.org/10.1145/3581783.3612413","title":"Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering","display_name":"Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4386644692","doi":"https://doi.org/10.1145/3581783.3612413"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612413","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612413","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612413","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090304750","display_name":"Yifan Dong","orcid":"https://orcid.org/0009-0004-9654-6792"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Dong","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0009-0004-9654-6792","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101686860","display_name":"Suhang Wu","orcid":"https://orcid.org/0009-0000-1010-1890"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suhang Wu","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0009-0000-1010-1890","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024849044","display_name":"Fandong Meng","orcid":"https://orcid.org/0000-0002-8158-2377"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fandong Meng","raw_affiliation_strings":["Tencent WeChat AI, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8158-2377","affiliations":[{"raw_affiliation_string":"Tencent WeChat AI, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770464","display_name":"Jie Zhou","orcid":"https://orcid.org/0000-0002-5899-5165"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zhou","raw_affiliation_strings":["Tencent WeChat AI, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5899-5165","affiliations":[{"raw_affiliation_string":"Tencent WeChat AI, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456913","display_name":"Xiaoli Wang","orcid":"https://orcid.org/0000-0002-8677-9080"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoli Wang","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-8677-9080","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052507628","display_name":"Jianxin Lin","orcid":"https://orcid.org/0000-0003-0359-8821"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Lin","raw_affiliation_strings":["Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-0359-8821","affiliations":[{"raw_affiliation_string":"Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066326238","display_name":"Jinsong Su","orcid":"https://orcid.org/0000-0001-5606-7122"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsong Su","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0001-5606-7122","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6518,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68824775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3897","last_page":"3907"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9983999729156494,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9983999729156494,"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":0.9933000206947327,"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/T10028","display_name":"Topic Modeling","score":0.986299991607666,"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/computer-science","display_name":"Computer science","score":0.8267043232917786},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6236220002174377},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5847890377044678},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5642141103744507},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.560113787651062},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5313352346420288},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5169222354888916},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4714661240577698},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.46037963032722473},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4564095437526703},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4187034070491791},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3994409739971161},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3244086503982544}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8267043232917786},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6236220002174377},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5847890377044678},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5642141103744507},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.560113787651062},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5313352346420288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5169222354888916},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4714661240577698},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.46037963032722473},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4564095437526703},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4187034070491791},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3994409739971161},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3244086503982544},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3581783.3612413","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612413","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2309.04734","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.04734","pdf_url":"https://arxiv.org/pdf/2309.04734","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"}],"best_oa_location":{"id":"doi:10.1145/3581783.3612413","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3581783.3612413","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3581783.3612413","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4220311613","display_name":null,"funder_award_id":"2021J01003","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G5630136037","display_name":null,"funder_award_id":"2020AAA0108004","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8535310050","display_name":null,"funder_award_id":"62276219","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8860667405","display_name":null,"funder_award_id":"2020J06001","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386644692.pdf","grobid_xml":"https://content.openalex.org/works/W4386644692.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1978394996","https://openalex.org/W2062233052","https://openalex.org/W2103567420","https://openalex.org/W2133564696","https://openalex.org/W2250539671","https://openalex.org/W2546696630","https://openalex.org/W2560674852","https://openalex.org/W2577986441","https://openalex.org/W2592249290","https://openalex.org/W2606974598","https://openalex.org/W2739671343","https://openalex.org/W2745461083","https://openalex.org/W2790109590","https://openalex.org/W2888766462","https://openalex.org/W2891585127","https://openalex.org/W2905317234","https://openalex.org/W2918609062","https://openalex.org/W2932847124","https://openalex.org/W2949925172","https://openalex.org/W2949963192","https://openalex.org/W2962850006","https://openalex.org/W2963265326","https://openalex.org/W2963521239","https://openalex.org/W2963531963","https://openalex.org/W2963821229","https://openalex.org/W2964045208","https://openalex.org/W2964072591","https://openalex.org/W2964138017","https://openalex.org/W2964165364","https://openalex.org/W2985076077","https://openalex.org/W3034379969","https://openalex.org/W3034871396","https://openalex.org/W3035328829","https://openalex.org/W3083614545","https://openalex.org/W3102475290","https://openalex.org/W3103263533","https://openalex.org/W3109387650","https://openalex.org/W3117216739","https://openalex.org/W3126792443","https://openalex.org/W3127151332","https://openalex.org/W3167469277","https://openalex.org/W3173799534","https://openalex.org/W4240935049","https://openalex.org/W4284701759","https://openalex.org/W4285600277","https://openalex.org/W4285605356","https://openalex.org/W4287854428","https://openalex.org/W4287887917","https://openalex.org/W4312910992","https://openalex.org/W4366780218","https://openalex.org/W4385245566","https://openalex.org/W4385570660","https://openalex.org/W4385571272","https://openalex.org/W4385571275","https://openalex.org/W4385572291","https://openalex.org/W4385574237"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W2999756192","https://openalex.org/W103652678","https://openalex.org/W4226090359","https://openalex.org/W2059697060","https://openalex.org/W2743738614","https://openalex.org/W4323323165","https://openalex.org/W2745033168"],"abstract_inverted_index":{"Multi-modal":[0],"keyphrase":[1,31,67,102,144],"generation":[2,103],"aims":[3],"to":[4,54,134,159,177],"produce":[5],"a":[6,42,99],"set":[7],"of":[8,15,45,127,181,190,197],"keyphrases":[9,176],"that":[10,209],"represent":[11],"the":[12,16,65,70,82,88,109,128,131,135,139,168,179,182,188,200,213],"core":[13],"points":[14],"input":[17,71,111,133],"text-image":[18],"pair.":[19],"In":[20],"this":[21,95],"regard,":[22],"dominant":[23],"methods":[24],"mainly":[25],"focus":[26],"on":[27,199,220],"multi-modal":[28,101],"fusion":[29],"for":[30,64,143],"generation.":[32,68,145],"Nevertheless,":[33],"there":[34],"are":[35,75],"still":[36],"two":[37],"main":[38],"drawbacks:":[39],"1)":[40],"only":[41,107],"limited":[43],"number":[44],"sources,":[46],"such":[47],"as":[48,130],"image":[49,74,83,119,129,155,162,172],"captions,":[50],"can":[51],"be":[52,62],"utilized":[53],"provide":[55],"auxiliary":[56],"information.":[57],"However,":[58],"they":[59],"may":[60,84],"not":[61,77,106],"sufficient":[63],"subsequent":[66],"2)":[69],"text":[72],"and":[73,80,154,174,205],"often":[76],"perfectly":[78],"matched,":[79],"thus":[81],"introduce":[85,123,167],"noise":[86,163],"into":[87],"model.":[89],"To":[90,186],"address":[91],"these":[92],"limitations,":[93],"in":[94],"paper,":[96],"we":[97,122,147,166,193],"propose":[98],"novel":[100],"model,":[104,136,192],"which":[105,137],"enriches":[108],"model":[110,211],"with":[112],"external":[113,124],"knowledge,":[114],"but":[115],"also":[116],"effectively":[117],"filters":[118],"noise.":[120],"First,":[121],"visual":[125],"entities":[126],"supplementary":[132],"benefits":[138],"cross-modal":[140],"semantic":[141],"alignment":[142],"Second,":[146],"simultaneously":[148],"calculate":[149],"an":[150],"image-text":[151],"matching":[152],"score":[153],"region-text":[156],"correlation":[157,169,184],"scores":[158,170],"perform":[160],"multi-granularity":[161],"filtering.":[164],"Particularly,":[165],"between":[171],"regions":[173],"ground-truth":[175],"refine":[178],"calculation":[180],"previously-mentioned":[183],"scores.":[185],"demonstrate":[187],"effectiveness":[189],"our":[191,210],"conduct":[194],"several":[195],"groups":[196],"experiments":[198],"benchmark":[201],"dataset.":[202],"Experimental":[203],"results":[204],"in-depth":[206],"analyses":[207],"show":[208],"achieves":[212],"state-of-the-art":[214],"performance.":[215],"Our":[216],"code":[217],"is":[218],"available":[219],"https://github.com/DeepLearnXMU/MM-MKP.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}