{"id":"https://openalex.org/W3198237452","doi":"https://doi.org/10.1145/3463945.3468170","title":"WenLan: Efficient Large-Scale Multi-Modal Pre-Training on Real World Data","display_name":"WenLan: Efficient Large-Scale Multi-Modal Pre-Training on Real World Data","publication_year":2021,"publication_date":"2021-08-21","ids":{"openalex":"https://openalex.org/W3198237452","doi":"https://doi.org/10.1145/3463945.3468170","mag":"3198237452"},"language":"en","primary_location":{"id":"doi:10.1145/3463945.3468170","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463945.3468170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Workshop on Multi-Modal Pre-Training for Multimedia Understanding","raw_type":"proceedings-article"},"type":"article","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/A5101505571","display_name":"Ruihua Song","orcid":"https://orcid.org/0000-0002-2163-7401"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruihua Song","raw_affiliation_strings":["Renmin University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101505571"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0961,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.39323529,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9805999994277954,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9506000280380249,"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/modal","display_name":"Modal","score":0.7711037397384644},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7429394721984863},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6682984828948975},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5347911715507507},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5191618204116821},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5083157420158386},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.501812219619751},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4464261531829834},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38435572385787964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3207986652851105},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09624746441841125},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07686477899551392}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7711037397384644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429394721984863},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6682984828948975},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5347911715507507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5191618204116821},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5083157420158386},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.501812219619751},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4464261531829834},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38435572385787964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3207986652851105},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09624746441841125},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07686477899551392},{"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/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3463945.3468170","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463945.3468170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Workshop on Multi-Modal Pre-Training for Multimedia Understanding","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":1,"referenced_works":["https://openalex.org/W3133825286"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W3107474891","https://openalex.org/W2387314715","https://openalex.org/W4232897744","https://openalex.org/W2916492174","https://openalex.org/W3127532647","https://openalex.org/W2401110621","https://openalex.org/W2790047899"],"abstract_inverted_index":{"Multi-modal":[0],"pre-training":[1,89,113],"models":[2],"have":[3],"been":[4],"intensively":[5],"explored":[6],"to":[7,53,127],"bridge":[8],"vision":[9],"and":[10,38,125,136],"language":[11],"in":[12,48],"recent":[13],"years.":[14],"However,":[15],"most":[16],"of":[17,67,107],"them":[18],"explicitly":[19],"model":[20,55,90],"the":[21,36,56,65,68,78,94],"cross-modal":[22,57,95],"interaction":[23],"between":[24,35,134],"image-text":[25,83,110],"pairs,":[26,84],"by":[27,73],"assuming":[28],"that":[29,119],"there":[30],"exists":[31],"strong":[32,43],"semantic":[33],"correlation":[34,58,80],"text":[37],"image":[39],"modalities.":[40],"Since":[41],"this":[42],"assumption":[44,81],"is":[45,64],"often":[46],"invalid":[47],"real-world":[49],"scenarios,":[50],"we":[51,85],"choose":[52],"implicitly":[54],"for":[59,112],"large-scale":[60],"multi-modal":[61],"pre-training,":[62],"which":[63],"focus":[66],"Chinese":[69,104],"project":[70],"'WenLan'":[71],"led":[72],"our":[74,114],"team.":[75],"Specifically,":[76],"with":[77],"weak":[79],"over":[82],"propose":[86],"a":[87,102],"two-tower":[88],"called":[91],"BriVL":[92],"within":[93],"contrastive":[96],"learning":[97],"framework":[98],"[1].":[99],"We":[100],"construct":[101],"large":[103],"multi-source":[105],"dataset":[106],"650":[108],"million":[109],"pairs":[111],"model.":[115],"Extensive":[116],"experiments":[117],"demonstrate":[118],"WenLan":[120],"on":[121,132],"various":[122],"downstream":[123],"tasks":[124],"easy":[126],"build":[128],"efficient":[129],"applications":[130],"based":[131],"searching":[133],"images":[135],"texts.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-02-14T06:23:00.392402","created_date":"2025-10-10T00:00:00"}