乐胖代购免代理版

{"id":"https://openalex.org/W4389523744","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.629","title":"APoLLo : Unified Adapter and Prompt Learning for Vision Language Models","display_name":"APoLLo : Unified Adapter and Prompt Learning for Vision Language Models","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389523744","doi":"https://doi.org/10.18653/v1/2023.emnlp-main.629"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.629","pdf_url":"https://aclanthology.org/2023.emnlp-main.629.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2023.emnlp-main.629.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101778650","display_name":"Sanjoy Chowdhury","orcid":"https://orcid.org/0000-0003-4256-4720"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjoy Chowdhury","raw_affiliation_strings":["University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101977185","display_name":"Sayan Nag","orcid":"https://orcid.org/0000-0001-5652-125X"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sayan Nag","raw_affiliation_strings":["University of Toronto"],"affiliations":[{"raw_affiliation_string":"University of Toronto","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004194238","display_name":"Dinesh Manocha","orcid":"https://orcid.org/0000-0001-7047-9801"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinesh Manocha","raw_affiliation_strings":["University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.323,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":7,"citation_normalized_percentile":{"value":0.999599,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"10173","last_page":"10187"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9999,"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":0.9999,"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":0.9963,"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":0.9929,"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/adapter","display_name":"Adapter (computing)","score":0.68924797},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5583437},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.45667124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8186718},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.68924797},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.59249526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5819378},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5583437},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4800728},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.45667124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3685897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13543886},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.629","pdf_url":"https://aclanthology.org/2023.emnlp-main.629.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.01564","pdf_url":"http://arxiv.org/pdf/2312.01564","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.emnlp-main.629","pdf_url":"https://aclanthology.org/2023.emnlp-main.629.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"display_name":"Quality education","score":0.67,"id":"https://metadata.un.org/sdg/4"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":70,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1846799578","https://openalex.org/W2017814585","https://openalex.org/W2047643928","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2155904486","https://openalex.org/W24089286","https://openalex.org/W2533598788","https://openalex.org/W2896457183","https://openalex.org/W2947707615","https://openalex.org/W2953937638","https://openalex.org/W2955425717","https://openalex.org/W2962707369","https://openalex.org/W2962945654","https://openalex.org/W2963211188","https://openalex.org/W2964303773","https://openalex.org/W2965373594","https://openalex.org/W2971296908","https://openalex.org/W3005680577","https://openalex.org/W3037492894","https://openalex.org/W3094502228","https://openalex.org/W3126337491","https://openalex.org/W3128513979","https://openalex.org/W3134652006","https://openalex.org/W3138516171","https://openalex.org/W3166396011","https://openalex.org/W3174770825","https://openalex.org/W3177096435","https://openalex.org/W3198377975","https://openalex.org/W3205270560","https://openalex.org/W3213647938","https://openalex.org/W3215626407","https://openalex.org/W4205991051","https://openalex.org/W4224545477","https://openalex.org/W4226182655","https://openalex.org/W4229453513","https://openalex.org/W4280496682","https://openalex.org/W4281838252","https://openalex.org/W4281987380","https://openalex.org/W4284961860","https://openalex.org/W4288089799","https://openalex.org/W4292779060","https://openalex.org/W4296151208","https://openalex.org/W4310557340","https://openalex.org/W4311642023","https://openalex.org/W4312310776","https://openalex.org/W4312424618","https://openalex.org/W4312446817","https://openalex.org/W4312784228","https://openalex.org/W4312884055","https://openalex.org/W4312933868","https://openalex.org/W4313068342","https://openalex.org/W4321337794","https://openalex.org/W4322718191","https://openalex.org/W4362655426","https://openalex.org/W4366330698","https://openalex.org/W4366458249","https://openalex.org/W4368321032","https://openalex.org/W4378473852","https://openalex.org/W4385571139","https://openalex.org/W4385801086","https://openalex.org/W4386065353","https://openalex.org/W4386065763","https://openalex.org/W4386065836","https://openalex.org/W4386071547","https://openalex.org/W4386071687","https://openalex.org/W4386076084","https://openalex.org/W4386790226","https://openalex.org/W4389519448"],"related_works":["https://openalex.org/W4378510483","https://openalex.org/W4376166922","https://openalex.org/W4362597605","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4221142204","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W2490526372","https://openalex.org/W1574414179"],"abstract_inverted_index":{"The":[0],"choice":[1],"of":[2,13,47],"input":[3],"text":[4],"prompt":[5],"plays":[6],"a":[7,24,55,118],"critical":[8],"role":[9],"in":[10,54,64,93],"the":[11,44,73,76,83],"performance":[12],"Vision-Language":[14,35],"Pretrained":[15],"(VLP)":[16],"models":[17,49],"such":[18],"as":[19],"CLIP.":[20],"We":[21,58,79],"present":[22],"APoLLo,":[23],"unified":[25],"multi-modal":[26],"approach":[27],"that":[28],"combines":[29],"Adapter":[30],"and":[31,68,110],"Prompt":[32],"learning":[33],"for":[34,130],"models.":[36],"Our":[37,96],"method":[38,97],"is":[39,98],"designed":[40],"to":[41,71,90,105,122],"substantially":[42],"improve":[43],"generalization":[45,104],"capabilities":[46],"VLP":[48],"when":[50],"they":[51],"are":[52],"fine-tuned":[53],"few-shot":[56],"setting.":[57],"introduce":[59],"trainable":[60],"cross-attention-based":[61],"adapter":[62],"layers":[63],"conjunction":[65],"with":[66],"vision":[67],"language":[69],"encoders":[70],"strengthen":[72],"alignment":[74],"between":[75,82],"two":[77],"modalities.":[78],"enforce":[80],"consistency":[81],"respective":[84],"encoder":[85],"branches":[86],"(receiving":[87],"augmented":[88],"inputs)":[89],"prevent":[91],"overfitting":[92],"downstream":[94],"tasks.":[95],"evaluated":[99],"on":[100,127],"three":[101],"representative":[102],"tasks:":[103],"novel":[106,128],"classes,":[107],"cross-dataset":[108],"evaluation,":[109],"unseen":[111],"domain":[112],"shifts.":[113],"In":[114],"practice,":[115],"APoLLo":[116],"achieves":[117],"relative":[119],"gain":[120],"up":[121],"6.03%":[123],"over":[124],"MaPLe":[125],"(SOTA)":[126],"classes":[129],"10":[131],"diverse":[132],"image":[133],"recognition":[134],"datasets.":[135]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4389523744","counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2025-01-06T21:33:00.959446","created_date":"2023-12-11"}