{"id":"https://openalex.org/W7162991666","doi":"https://doi.org/10.48550/arxiv.2605.30968","title":"Variational Adapter for Cross-modal Similarity Representation","display_name":"Variational Adapter for Cross-modal Similarity Representation","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7162991666","doi":"https://doi.org/10.48550/arxiv.2605.30968"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30968","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30968","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.30968","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101245937","display_name":"Wenzhang Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, WenZhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087201008","display_name":"Zhipeng Gui","orcid":"https://orcid.org/0000-0001-9467-9680"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gui, Zhipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064864952","display_name":"Dehua Peng","orcid":"https://orcid.org/0000-0002-9842-0796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Dehua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051708679","display_name":"Tiandi Ye","orcid":"https://orcid.org/0000-0003-0169-457X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Tiandi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030670883","display_name":"Huayi Wu","orcid":"https://orcid.org/0000-0003-3971-0512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Huayi","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9611999988555908,"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":0.9611999988555908,"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":0.026900000870227814,"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":0.0017999999690800905,"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/generalization","display_name":"Generalization","score":0.6197999715805054},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5623000264167786},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5527999997138977},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.51910001039505},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5127000212669373},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4927999973297119},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4894999861717224},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45419999957084656}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6197999715805054},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5623000264167786},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5527999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5435000061988831},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.51910001039505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5127000212669373},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.460099995136261},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4471000134944916},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3531999886035919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3228999972343445},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.2639999985694885},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2621000111103058},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2515999972820282},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25130000710487366},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30968","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30968","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30968","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30968","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"core":[1],"of":[2,50],"vision-language":[3],"models":[4],"lies":[5],"in":[6],"measuring":[7],"cross-modal":[8,26,51,110],"similarity":[9,32,111],"within":[10],"a":[11,82,100,106],"unified":[12],"representation":[13],"space.":[14],"However,":[15],"most":[16],"image-text":[17,93,124],"matching":[18,27,94],"or":[19],"multi-class":[20],"image":[21],"classification":[22,36],"datasets":[23],"lack":[24],"fine-grained":[25,96],"annotations,":[28],"forcing":[29],"the":[30,47,132],"continuous":[31],"space":[33,108],"into":[34],"binary":[35,120],"boundaries.":[37],"This":[38,90],"compression":[39],"induces":[40],"false":[41],"negative":[42],"samples":[43],"and":[44,112,128,136],"significantly":[45],"impairs":[46],"generalization":[48,130,138],"performance":[49],"tasks.":[52],"While":[53],"prior":[54],"research":[55],"has":[56],"attempted":[57],"to":[58,72,116,119],"mitigate":[59,117],"this":[60],"by":[61],"modeling":[62],"intra-modal":[63],"ambiguity,":[64],"it":[65],"often":[66],"overlooks":[67],"inherent":[68],"annotation":[69],"flaws,":[70],"leading":[71],"suboptimal":[73],"uncertainty":[74],"allocation.":[75],"To":[76],"address":[77],"these":[78],"challenges,":[79],"we":[80],"propose":[81],"Variational":[83],"Adapter":[84],"for":[85,109],"Cross-modal":[86],"Similarity":[87],"Representation":[88],"(VACSR).":[89],"approach":[91],"reformulates":[92],"with":[95],"semantic":[97],"scarcity":[98],"as":[99],"variational":[101],"inference":[102],"problem.":[103],"It":[104],"constructs":[105],"latent":[107],"uses":[113],"regularization":[114],"techniques":[115],"overfitting":[118],"annotations.":[121],"Experiments":[122],"on":[123],"retrieval,":[125],"domain":[126],"generalization,":[127],"base-to-novel":[129],"demonstrate":[131],"proposed":[133],"method's":[134],"effectiveness":[135],"robust":[137],"ability.":[139]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-02T00:00:00"}
