{"id":"https://openalex.org/W7162498987","doi":"https://doi.org/10.48550/arxiv.2605.26941","title":"The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval","display_name":"The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162498987","doi":"https://doi.org/10.48550/arxiv.2605.26941"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26941","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.2605.26941","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012498345","display_name":"Junchen Fu","orcid":"https://orcid.org/0000-0003-4759-2042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Junchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137098015","display_name":"Xuri Ge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ge, Xuri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137106694","display_name":"Xin Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082408006","display_name":"Alexandros Karatzoglou","orcid":"https://orcid.org/0000-0001-6063-9023"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karatzoglou, Alexandros","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137174749","display_name":"Ioannis Arapakis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arapakis, Ioannis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137090723","display_name":"Xi Wang","orcid":"https://orcid.org/0000-0001-5173-2234"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137180510","display_name":"Qijiong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qijiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137148998","display_name":"Qian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137158183","display_name":"Joemon M. Jose","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jose, Joemon M.","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.5213000178337097,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.5213000178337097,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.15369999408721924,"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":0.07349999994039536,"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/representation","display_name":"Representation (politics)","score":0.6600000262260437},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5558000206947327},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.48260000348091125},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.3716999888420105},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.35440000891685486},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.33719998598098755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7849000096321106},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6600000262260437},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45570001006126404},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3400999903678894},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3059999942779541},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C90288658","wikidata":"https://www.wikidata.org/wiki/Q3318149","display_name":"Human\u2013computer information retrieval","level":3,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26941","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.2605.26941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26941","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":[{"score":0.5336409211158752,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"representation":[1,79,119],"learning":[2,80,120],"has":[3],"attracted":[4],"increasing":[5],"attention":[6],"in":[7,81,121],"AI,":[8],"driven":[9],"by":[10],"the":[11,72,91,122],"strong":[12],"performance":[13,30],"of":[14,34,75],"large,":[15],"pretrained":[16],"multimodal":[17,35,117],"foundation":[18,76],"models":[19,27,77],"such":[20],"as":[21],"Qwen,":[22],"LLaVA,":[23],"and":[24,45,67,102,110,114],"CLIP.":[25],"These":[26,69],"deliver":[28],"impressive":[29],"on":[31],"a":[32],"range":[33],"information":[36,82],"retrieval":[37],"(MIR)":[38],"tasks,":[39],"including":[40],"web":[41],"search,":[42],"cross-modal":[43],"retrieval,":[44],"recommender":[46],"systems.":[47],"Yet":[48],"their":[49,59],"massive":[50],"parameter":[51],"counts":[52],"create":[53],"major":[54],"efficiency":[55,112],"bottlenecks":[56],"when":[57],"adapting":[58],"representations":[60],"for":[61,78,116],"IR":[62,118],"tasks":[63],"during":[64],"training,":[65],"deployment,":[66],"inference.":[68],"limitations":[70],"hinder":[71],"practical":[73],"use":[74],"retrieval.":[83],"To":[84],"address":[85],"these":[86],"issues,":[87],"we":[88],"propose":[89],"organizing":[90],"EReL@MIR":[92],"workshop":[93],"at":[94,131],"MM":[95],"2026,":[96],"bringing":[97],"together":[98],"researchers":[99],"from":[100],"academia":[101],"industry":[103],"to":[104],"discuss":[105],"emerging":[106],"solutions,":[107],"open":[108],"challenges,":[109],"new":[111],"metrics":[113],"benchmarks":[115],"foundation-model":[123],"era.":[124],"The":[125],"workshop's":[126],"official":[127],"website":[128],"is":[129],"available":[130],"https://erel-mir.github.io/.":[132]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}
