{"id":"https://openalex.org/W7166145012","doi":"https://doi.org/10.48550/arxiv.2606.27307","title":"See & Sniff: Learning Visuo-Olfactory Representations","display_name":"See & Sniff: Learning Visuo-Olfactory Representations","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166145012","doi":"https://doi.org/10.48550/arxiv.2606.27307"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27307","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.2606.27307","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133600396","display_name":"Seongyu Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Seongyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403754","display_name":"Seungwoo Lee","orcid":"https://orcid.org/0000-0002-2987-150X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Seungwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014059162","display_name":"Hyeonggon Ryu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryu, Hyeonggon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139428702","display_name":"Joon Son Chung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chung, Joon Son","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070707693","display_name":"Arda Senocak","orcid":"https://orcid.org/0000-0001-9141-3270"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Senocak, Arda","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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.00800000037997961,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.004800000227987766,"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/benchmark","display_name":"Benchmark (surveying)","score":0.5916000008583069},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.503600001335144},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.49790000915527344},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.43639999628067017},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4205000102519989},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.37299999594688416},{"id":"https://openalex.org/keywords/olfaction","display_name":"Olfaction","score":0.3637000024318695},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.30790001153945923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228000164031982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.694100022315979},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5916000008583069},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.503600001335144},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.49790000915527344},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C163214680","wikidata":"https://www.wikidata.org/wiki/Q1541064","display_name":"Olfaction","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3467000126838684},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C135641252","wikidata":"https://www.wikidata.org/wiki/Q738567","display_name":"Multimodal interaction","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27307","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.2606.27307","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27307","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"modern":[1],"multimodal":[2,147],"models":[3],"integrate":[4],"vision":[5],"with":[6,55],"language,":[7],"audio,":[8],"or":[9],"touch,":[10],"olfaction":[11],"remains":[12],"largely":[13,38],"unexplored":[14],"due":[15],"to":[16,40,50,133],"the":[17,32],"lack":[18],"of":[19,103],"paired":[20],"visuo-olfactory":[21,28,88,140],"data.":[22],"We":[23,106],"introduce":[24,108],"SmellNet-V,":[25],"a":[26,44,62,67,82,114,143],"scalable":[27],"dataset":[29,65],"built":[30],"on":[31,74],"insight":[33],"that":[34,85],"odor":[35,104],"identity":[36],"is":[37],"invariant":[39],"visual":[41],"transformations":[42],"within":[43],"semantic":[45],"category.":[46],"This":[47],"allows":[48],"us":[49],"synthetically":[51],"pair":[52],"smell-only":[53,121],"samples":[54],"semantically":[56],"aligned":[57],"in-the-wild":[58],"web":[59],"images,":[60],"converting":[61],"unimodal":[63],"olfactory":[64],"into":[66],"cross-modal":[68,134],"benchmark":[69,115],"without":[70],"costly":[71],"co-collection.":[72],"Building":[73],"this":[75],"dataset,":[76],"we":[77],"propose":[78],"See":[79],"&":[80],"Sniff,":[81],"self-supervised":[83],"framework":[84],"learns":[86],"joint":[87],"representations":[89],"via":[90],"dense":[91],"local":[92],"alignment":[93],"and":[94,113,131,136],"naturally":[95],"produces":[96],"smell":[97,110,126,129,137],"saliency":[98],"maps":[99],"for":[100,116],"spatial":[101],"grounding":[102],"sources.":[105],"further":[107],"pixel-level":[109],"localization":[111],"task":[112],"evaluation.":[117],"Our":[118],"method":[119],"surpasses":[120],"baselines":[122],"by":[123],"7%":[124],"in":[125,146],"classification":[127],"from":[128],"alone":[130],"generalizes":[132],"retrieval":[135],"localization,":[138],"establishing":[139],"learning":[141],"as":[142],"new":[144],"direction":[145],"perception.":[148]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-27T00:00:00"}