{"id":"https://openalex.org/W4392341741","doi":"https://doi.org/10.48550/arxiv.2402.18490","title":"TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding","display_name":"TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding","publication_year":2024,"publication_date":"2024-02-28","ids":{"openalex":"https://openalex.org/W4392341741","doi":"https://doi.org/10.48550/arxiv.2402.18490"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2402.18490","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.18490","pdf_url":"https://arxiv.org/pdf/2402.18490","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.18490","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100361535","display_name":"Zhihao Zhang","orcid":"https://orcid.org/0009-0008-2776-4103"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102623860","display_name":"Shengcao Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Shengcao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039231803","display_name":"Yu-Xiong Wang","orcid":"https://orcid.org/0000-0003-1195-2739"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yu-Xiong","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":true,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9977999925613403,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9977999925613403,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"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":0.986299991607666,"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/modal","display_name":"Modal","score":0.6424813270568848},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46154409646987915},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.18524590134620667}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6424813270568848},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46154409646987915},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.18524590134620667},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2402.18490","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.18490","pdf_url":"https://arxiv.org/pdf/2402.18490","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2402.18490","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.18490","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":"pmh:oai:arXiv.org:2402.18490","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.18490","pdf_url":"https://arxiv.org/pdf/2402.18490","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2926720356","display_name":null,"funder_award_id":"32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G539587745","display_name":"RI: Medium: Creating Knowledge with All-Novel-Class Computer Vision","funder_award_id":"2106825","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5620962805","display_name":null,"funder_award_id":"67021","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G6770826516","display_name":null,"funder_award_id":"2020-67021-32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320312143","display_name":"National Centre for Supercomputing Applications","ror":"https://ror.org/03r10zj06"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392341741.pdf","grobid_xml":"https://content.openalex.org/works/W4392341741.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"The":[0],"limited":[1],"scale":[2],"of":[3,86,135,188],"current":[4],"3D":[5,12,32,67,147,182,189],"shape":[6,13,148],"datasets":[7],"hinders":[8],"the":[9,37,53,62,75,79,83,96,121,132,146,161,200,212],"advancements":[10],"in":[11,64,78,95],"understanding,":[14,165],"and":[15,28,39,82,127,160,171,194,210],"motivates":[16],"multi-modal":[17,66,173],"learning":[18,69,108],"approaches":[19],"which":[20,166],"transfer":[21],"learned":[22],"knowledge":[23],"from":[24,206,221],"data-abundant":[25],"2D":[26,80],"image":[27,38,54],"language":[29,40,63],"modalities":[30,94],"to":[31,57,74,208,223],"shapes.":[33],"However,":[34],"even":[35],"though":[36],"representations":[41,134,183],"have":[42],"been":[43],"aligned":[44],"by":[45,130],"cross-modal":[46],"models":[47],"like":[48],"CLIP,":[49],"we":[50,98,198],"find":[51],"that":[52,178],"modality":[55],"fails":[56],"contribute":[58],"as":[59,61],"much":[60],"existing":[65],"representation":[68,149],"methods.":[70],"This":[71],"is":[72],"attributed":[73],"domain":[76,122],"shift":[77],"images":[81,126],"distinct":[84],"focus":[85],"each":[87],"modality.":[88],"To":[89],"more":[90,169],"effectively":[91],"leverage":[92],"both":[93],"pre-training,":[97],"introduce":[99],"TriAdapter":[100],"Multi-Modal":[101],"Learning":[102],"(TAMM)":[103],"--":[104],"a":[105,168,185],"novel":[106],"two-stage":[107],"approach":[109],"based":[110],"on":[111,157,204,219],"three":[112],"synergistic":[113],"adapters.":[114],"First,":[115],"our":[116,142],"CLIP":[117,136],"Image":[118],"Adapter":[119],"mitigates":[120],"gap":[123],"between":[124],"3D-rendered":[125],"natural":[128],"images,":[129],"adapting":[131],"visual":[133,158],"for":[137,163,184],"synthetic":[138],"image-text":[139],"pairs.":[140],"Subsequently,":[141],"Dual":[143],"Adapters":[144],"decouple":[145],"space":[150],"into":[151],"two":[152],"complementary":[153],"sub-spaces:":[154],"one":[155],"focusing":[156],"attributes":[159],"other":[162],"semantic":[164],"ensure":[167],"comprehensive":[170],"effective":[172],"pre-training.":[174],"Extensive":[175],"experiments":[176],"demonstrate":[177],"TAMM":[179],"consistently":[180],"enhances":[181],"wide":[186],"range":[187],"encoder":[190],"architectures,":[191],"pre-training":[192],"datasets,":[193],"downstream":[195],"tasks.":[196],"Notably,":[197],"boost":[199],"zero-shot":[201],"classification":[202,217],"accuracy":[203,218],"Objaverse-LVIS":[205],"46.8\\%":[207],"50.7\\%,":[209],"improve":[211],"5-way":[213],"10-shot":[214],"linear":[215],"probing":[216],"ModelNet40":[220],"96.1\\%":[222],"99.0\\%.":[224],"Project":[225],"page:":[226],"https://alanzhangcs.github.io/tamm-page.":[227]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}