{"id":"https://openalex.org/W7163169254","doi":"https://doi.org/10.48550/arxiv.2606.01334","title":"HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition","display_name":"HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition","publication_year":2026,"publication_date":"2026-05-31","ids":{"openalex":"https://openalex.org/W7163169254","doi":"https://doi.org/10.48550/arxiv.2606.01334"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01334","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01334","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":"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.2606.01334","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137618247","display_name":"Koby Aharonov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aharonov, Koby","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007071175","display_name":"Oren Shrout","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shrout, Oren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109259472","display_name":"Ayellet Tal","orcid":"https://orcid.org/0000-0002-4967-7309"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tal, Ayellet","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/T10036","display_name":"Advanced Neural Network Applications","score":0.30889999866485596,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.30889999866485596,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2102999985218048,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.12890000641345978,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7771999835968018},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5755000114440918},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5059000253677368},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.436599999666214},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.400299996137619},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.366100013256073},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3643999993801117},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.3589000105857849}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7876999974250793},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7771999835968018},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5755000114440918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5059000253677368},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.275299996137619},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01334","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01334","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":"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.2606.01334","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01334","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":"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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6662006378173828}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-set":[0],"3D":[1,21,65,84],"recognition":[2],"requires":[3],"models":[4],"that":[5,127],"generalize":[6],"to":[7,43,58,74,150,157],"rare":[8],"or":[9,38],"unseen":[10],"categories.":[11],"Recent":[12],"approaches":[13],"address":[14],"this":[15,69],"by":[16],"distilling":[17],"language-vision":[18],"knowledge":[19],"into":[20],"encoders,":[22],"typically":[23],"relying":[24],"on":[25,120,174],"heavy":[26],"2D":[27],"ViTs":[28],"and":[29,55,92,160],"aligning":[30,48,82],"each":[31,49],"point":[32,50],"cloud":[33,51],"with":[34,52,86,136],"a":[35,60,76,83,105,144],"single":[36],"image":[37],"caption,":[39],"thus":[40],"anchoring":[41],"representations":[42],"partial":[44],"views.":[45],"We":[46,102],"propose":[47],"multiple":[53,87,93],"images":[54,91],"textual":[56],"descriptions":[57],"capture":[59],"more":[61],"holistic":[62],"understanding":[63],"of":[64,80,164],"objects.":[66],"To":[67],"realize":[68],"idea,":[70],"it":[71],"is":[72],"essential":[73],"design":[75],"loss":[77],"function":[78],"capable":[79],"jointly":[81],"instance":[85],"matched":[88],"signals,":[89],"multi-view":[90],"texts,":[94],"while":[95,181],"separating":[96],"positive":[97],"aggregation":[98],"from":[99],"negative":[100],"competition.":[101],"introduce":[103],"such":[104],"function,":[106],"termed":[107],"the":[108,116,124,133,138,154],"decoupled":[109],"multi-positive":[110],"contrastive":[111],"loss.":[112],"Our":[113,168],"formulation":[114],"enhances":[115],"loss's":[117],"hardness-aware":[118],"focus":[119],"challenging":[121],"negatives,":[122],"avoiding":[123],"\"spotlight":[125],"crowding\"":[126],"occurs":[128],"when":[129],"many":[130],"positives":[131],"share":[132],"same":[134],"softmax":[135],"all":[137],"negatives.":[139],"Complementing":[140],"this,":[141],"we":[142],"present":[143],"lightweight":[145],"text":[146],"adapter":[147],"applied":[148],"only":[149],"web":[151],"captions,":[152],"reducing":[153],"domain":[155],"gap":[156],"curated":[158],"annotations":[159],"enabling":[161],"effective":[162],"use":[163],"large-scale":[165],"unsupervised":[166],"text.":[167],"model":[169],"demonstrates":[170],"state-of-the-art":[171],"open-vocabulary":[172],"performance":[173],"long-tail":[175],"benchmarks,":[176],"yielding":[177],"substantial":[178],"zero-shot":[179],"improvements":[180],"sustaining":[182],"high":[183],"frame":[184],"rates.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}
