{"id":"https://openalex.org/W4304092041","doi":"https://doi.org/10.1145/3503161.3548251","title":"Crossmodal Few-shot 3D Point Cloud Semantic Segmentation","display_name":"Crossmodal Few-shot 3D Point Cloud Semantic Segmentation","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304092041","doi":"https://doi.org/10.1145/3503161.3548251"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3548251","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548251","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100649889","display_name":"Ziyu Zhao","orcid":"https://orcid.org/0009-0009-0677-190X"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyu Zhao","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052186627","display_name":"Zhenyao Wu","orcid":"https://orcid.org/0000-0002-5827-7155"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenyao Wu","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040568380","display_name":"Xinyi Wu","orcid":"https://orcid.org/0000-0002-6896-9251"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyi Wu","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021971528","display_name":"Canyu Zhang","orcid":"https://orcid.org/0000-0003-4660-6772"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Canyu Zhang","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082259804","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-4152-5295"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":3.6603,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.95690259,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4760","last_page":"4768"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9988999962806702,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9948999881744385,"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/point-cloud","display_name":"Point cloud","score":0.8777233362197876},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8314245939254761},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7461339235305786},{"id":"https://openalex.org/keywords/crossmodal","display_name":"Crossmodal","score":0.7054563760757446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5293498635292053},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5029045939445496},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.49860167503356934},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.46357256174087524},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46041208505630493},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.44248145818710327},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.436368465423584},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.42433255910873413},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39951053261756897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34746336936950684},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26070642471313477}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8777233362197876},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8314245939254761},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7461339235305786},{"id":"https://openalex.org/C60115397","wikidata":"https://www.wikidata.org/wiki/Q5188732","display_name":"Crossmodal","level":4,"score":0.7054563760757446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5293498635292053},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5029045939445496},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.49860167503356934},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.46357256174087524},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46041208505630493},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.44248145818710327},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.436368465423584},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.42433255910873413},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39951053261756897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34746336936950684},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26070642471313477},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3548251","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548251","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1803059841","https://openalex.org/W2125416623","https://openalex.org/W2155871590","https://openalex.org/W2436453945","https://openalex.org/W2460657278","https://openalex.org/W2558535589","https://openalex.org/W2559655401","https://openalex.org/W2594519801","https://openalex.org/W2595697910","https://openalex.org/W2612690371","https://openalex.org/W2792472361","https://openalex.org/W2895472109","https://openalex.org/W2949258649","https://openalex.org/W2963281829","https://openalex.org/W2963517242","https://openalex.org/W2963599420","https://openalex.org/W2963735856","https://openalex.org/W2963760790","https://openalex.org/W2964345792","https://openalex.org/W2970027214","https://openalex.org/W2980088508","https://openalex.org/W2981787211","https://openalex.org/W2983850069","https://openalex.org/W2985775862","https://openalex.org/W2990230185","https://openalex.org/W2991216808","https://openalex.org/W3012494314","https://openalex.org/W3026879213","https://openalex.org/W3106441352","https://openalex.org/W3129840128","https://openalex.org/W3163679690","https://openalex.org/W3168377947","https://openalex.org/W3175682855","https://openalex.org/W3201726317","https://openalex.org/W4212774754","https://openalex.org/W4249142012"],"related_works":["https://openalex.org/W4240440807","https://openalex.org/W953566696","https://openalex.org/W2010220987","https://openalex.org/W2010927954","https://openalex.org/W2085535992","https://openalex.org/W2053951134","https://openalex.org/W4386123105","https://openalex.org/W2933405975","https://openalex.org/W3117345873","https://openalex.org/W2759763224"],"abstract_inverted_index":{"Recently,":[0],"few-shot":[1,37,84,235],"3D":[2,25,63,88,151,180,236],"point":[3,89,97,152,181,237],"cloud":[4,90,98,153,182,238],"semantic":[5,91,239],"segmentation":[6,121,240],"methods":[7,39],"have":[8],"been":[9],"introduced":[10],"to":[11,30,32,99,118,129,184],"mitigate":[12],"the":[13,96,120,138,146,160,172,192,201,204],"limitations":[14],"of":[15,59,122,140,174,194],"existing":[16,234],"fully":[17],"supervised":[18],"approaches,":[19],"i.e.,":[20],"heavy":[21],"dependence":[22],"on":[23,131,156,212],"labeled":[24,44,110,222],"data":[26,45,53],"and":[27,70,159,176,196,203],"poor":[28],"capacity":[29],"generalize":[31],"new":[33],"categories.":[34],"However,":[35],"those":[36],"learning":[38,85],"need":[40,128],"one":[41,107,219],"or":[42,108,220],"few":[43,109,133,221],"as":[46,104,116,225],"support":[47,117,135,148,175,195],"for":[48,87,137,170,207],"testing.":[49],"In":[50,93],"practice,":[51],"such":[52],"labeling":[54],"usually":[55],"requires":[56],"manual":[57],"annotation":[58],"large-scale":[60],"points":[61],"in":[62,76],"space,":[64],"which":[65],"can":[66],"be":[67,100],"very":[68],"difficult":[69],"laborious.":[71],"To":[72],"address":[73],"this":[74,77,94],"problem,":[75],"paper":[78],"we":[79,126,143,190],"introduce":[80,166],"a":[81,132,167],"novel":[82],"crossmodal":[83],"approach":[86],"segmentation.":[92,209],"approach,":[95],"segmented":[101],"is":[102],"taken":[103,115],"query":[105,205],"while":[106],"2D":[111,134,147,223],"RGB":[112],"images":[113,136,149,224],"are":[114],"guide":[119],"query.":[123],"This":[124],"way,":[125],"only":[127],"annotate":[130],"categories":[139],"interest.":[141],"Specifically,":[142],"first":[144],"convert":[145],"into":[150],"format":[154],"based":[155],"both":[157,178],"appearance":[158],"estimated":[161],"depth":[162],"information.":[163],"We":[164],"then":[165],"co-embedding":[168],"network":[169],"extracting":[171],"features":[173,206],"query,":[177],"from":[179],"format,":[183],"fill":[185],"their":[186],"domain":[187],"gap.":[188],"Finally,":[189],"compute":[191],"prototypes":[193,202],"employ":[197],"cosine":[198],"similarity":[199],"between":[200],"final":[208],"Experimental":[210],"results":[211,232],"two":[213],"widely-used":[214],"benchmarks":[215],"show":[216],"that,":[217],"with":[218],"support,":[226],"our":[227],"proposed":[228],"method":[229],"achieves":[230],"competitive":[231],"against":[233],"methods.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}