{"id":"https://openalex.org/W7155091077","doi":"https://doi.org/10.48550/arxiv.2604.17782","title":"Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval","display_name":"Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155091077","doi":"https://doi.org/10.48550/arxiv.2604.17782"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17782","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.17782","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134117515","display_name":"Lin Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043694321","display_name":"Qingshan She","orcid":"https://orcid.org/0000-0001-5206-9833"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"She, Qingshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134170000","display_name":"Jiale Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiale","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058977200","display_name":"Haiqi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Haiqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134151390","display_name":"Duanpo Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Duanpo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134198987","display_name":"Zhenzhong Kuang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuang, Zhenzhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.6593000292778015,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.6593000292778015,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.0925000011920929,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.046799998730421066,"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/granularity","display_name":"Granularity","score":0.6690999865531921},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6187000274658203},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5060999989509583},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.49939998984336853},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.48890000581741333},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.46959999203681946},{"id":"https://openalex.org/keywords/visual-word","display_name":"Visual Word","score":0.46700000762939453},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43619999289512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7710999846458435},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6690999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6194000244140625},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6187000274658203},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5060999989509583},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.46700000762939453},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3531999886035919},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.335099995136261},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C2779345533","wikidata":"https://www.wikidata.org/wiki/Q75785","display_name":"Visual cortex","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2780103172","wikidata":"https://www.wikidata.org/wiki/Q1309721","display_name":"Visual Objects","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2524999976158142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17782","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.17782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17782","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5923581123352051}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-shot":[0],"EEG-to-image":[1,35,100],"retrieval":[2,36,174],"aims":[3],"to":[4,81,124],"decode":[5],"perceived":[6],"visual":[7,18,26,49,77,107],"content":[8],"from":[9,116],"electroencephalography":[10],"(EEG)":[11],"by":[12,110],"aligning":[13],"neural":[14,27],"responses":[15],"with":[16,149],"pretrained":[17,118],"representations,":[19],"providing":[20],"a":[21,46,52,105,117,141,150],"promising":[22],"route":[23],"toward":[24],"scalable":[25],"decoding":[28],"and":[29,75,162,167,190,198,201],"practical":[30],"brain-computer":[31],"interfaces.":[32],"However,":[33],"robust":[34],"remains":[37],"challenging,":[38],"because":[39],"prior":[40],"methods":[41],"usually":[42],"rely":[43],"on":[44,136,178],"either":[45],"single":[47],"fixed":[48],"target":[50,54,109,139],"or":[51],"subject-invariant":[53],"construction":[55],"scheme.":[56],"Such":[57],"designs":[58],"overlook":[59],"two":[60],"important":[61],"properties":[62],"of":[63],"visually":[64],"evoked":[65],"EEG":[66,82],"signals:":[67],"they":[68],"preserve":[69],"information":[70],"across":[71,85],"multiple":[72,113],"representational":[73],"scales,":[74],"the":[76,122,154,158,168,179,184,195,206],"granularity":[78,127],"best":[79],"matched":[80],"may":[83],"vary":[84],"subjects.":[86],"To":[87],"address":[88],"these":[89],"issues,":[90],"subject-aware":[91,106],"multi-granularity":[92],"alignment":[93,144],"(SAMGA)":[94],"framework":[95],"is":[96,146],"proposed":[97,185],"for":[98],"zero-shot":[99],"retrieval.":[101],"SAMGA":[102],"first":[103],"constructs":[104],"supervision":[108],"adaptively":[111],"aggregating":[112],"intermediate":[114],"representations":[115],"vision":[119],"encoder,":[120],"allowing":[121],"model":[123],"absorb":[125],"subject-dependent":[126],"deviations":[128],"during":[129],"training":[130],"while":[131],"preserving":[132],"subject-agnostic":[133],"inference.":[134],"Building":[135],"this":[137],"adaptive":[138],"construction,":[140],"coarse-to-fine":[142],"cross-modal":[143],"strategy":[145],"further":[147,171],"designed":[148],"shared":[151,159],"encoder":[152],"wherein":[153],"coarse":[155],"stage":[156,170],"stabilizes":[157],"semantic":[160],"geometry":[161],"reduces":[163],"subject-induced":[164],"distribution":[165],"shift,":[166],"fine":[169],"improves":[172],"instance-level":[173],"discrimination.":[175],"Extensive":[176],"experiments":[177],"THINGS-EEG":[180],"benchmark":[181],"demonstrate":[182],"that":[183],"method":[186],"achieves":[187],"91.3%":[188],"Top-1":[189,200],"98.8%":[191],"Top-5":[192,203],"accuracy":[193,204],"in":[194,205],"intra-subject":[196],"setting,":[197,208],"34.4%":[199],"64.8%":[202],"inter-subject":[207],"outperforming":[209],"recent":[210],"state-of-the-art":[211],"methods.":[212]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-22T00:00:00"}
