{"id":"https://openalex.org/W4390874215","doi":"https://doi.org/10.1109/iccv51070.2023.00575","title":"CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations","display_name":"CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4390874215","doi":"https://doi.org/10.1109/iccv51070.2023.00575"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51070.2023.00575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51070.2023.00575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF International Conference on Computer Vision (ICCV)","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/A5059888118","display_name":"Qiming Xia","orcid":"https://orcid.org/0000-0002-6440-5694"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiming Xia","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024719468","display_name":"Jinhao Deng","orcid":"https://orcid.org/0000-0003-1271-5532"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhao Deng","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047386824","display_name":"Chenglu Wen","orcid":"https://orcid.org/0000-0002-6189-1236"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglu Wen","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037881802","display_name":"Hai Wu","orcid":"https://orcid.org/0000-0003-4530-1184"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai Wu","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037156705","display_name":"Shaoshuai Shi","orcid":"https://orcid.org/0000-0001-8984-9809"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaoshuai Shi","raw_affiliation_strings":["Max-Planck Institute"],"affiliations":[{"raw_affiliation_string":"Max-Planck Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353783","display_name":"Xin Li","orcid":"https://orcid.org/0000-0002-0144-9489"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Li","raw_affiliation_strings":["Texas A&M University"],"affiliations":[{"raw_affiliation_string":"Texas A&M University","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100416961","display_name":"Cheng Wang","orcid":"https://orcid.org/0000-0001-6075-796X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Wang","raw_affiliation_strings":["Xiamen University"],"affiliations":[{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5059888118"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":2.7057,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92296171,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6231","last_page":"6240"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9958000183105469,"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/computer-science","display_name":"Computer science","score":0.8368068933486938},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7345824241638184},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6867767572402954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6284546256065369},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5764884352684021},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5756655931472778},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5270476937294006},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.500615119934082},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4776388704776764},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45442771911621094},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.42764320969581604},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42724478244781494},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4044564664363861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3565199673175812},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.19245249032974243}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8368068933486938},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7345824241638184},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6867767572402954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6284546256065369},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5764884352684021},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5756655931472778},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5270476937294006},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.500615119934082},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4776388704776764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45442771911621094},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.42764320969581604},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42724478244781494},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4044564664363861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3565199673175812},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.19245249032974243},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv51070.2023.00575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51070.2023.00575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2138621090","https://openalex.org/W2150066425","https://openalex.org/W2798991696","https://openalex.org/W2949708697","https://openalex.org/W2963727135","https://openalex.org/W2963988212","https://openalex.org/W2968296999","https://openalex.org/W2981949127","https://openalex.org/W2982363097","https://openalex.org/W3008105217","https://openalex.org/W3034314779","https://openalex.org/W3034602892","https://openalex.org/W3035057392","https://openalex.org/W3035172746","https://openalex.org/W3035346742","https://openalex.org/W3035524453","https://openalex.org/W3035574168","https://openalex.org/W3092761163","https://openalex.org/W3108262825","https://openalex.org/W3108381027","https://openalex.org/W3113028524","https://openalex.org/W3118341329","https://openalex.org/W3128661784","https://openalex.org/W3134233478","https://openalex.org/W3136027703","https://openalex.org/W3166089996","https://openalex.org/W3167095230","https://openalex.org/W3168822201","https://openalex.org/W3175854195","https://openalex.org/W3178218920","https://openalex.org/W3185102306","https://openalex.org/W3201719054","https://openalex.org/W3205005447","https://openalex.org/W3217335336","https://openalex.org/W4200632008","https://openalex.org/W4226085620","https://openalex.org/W4285262093","https://openalex.org/W4292793891","https://openalex.org/W4293704629","https://openalex.org/W4293811845","https://openalex.org/W4312284259","https://openalex.org/W4312437143","https://openalex.org/W4312602791","https://openalex.org/W4312707458","https://openalex.org/W4313149358","https://openalex.org/W6785786191","https://openalex.org/W6791426119"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Recently,":[0],"3D":[1],"object":[2],"detection":[3],"with":[4,95,100,129],"sparse":[5],"annotations":[6,143],"has":[7],"received":[8],"great":[9],"attention.":[10],"However,":[11],"current":[12],"detectors":[13,94],"usually":[14],"perform":[15],"poorly":[16],"under":[17,111],"very":[18],"limited":[19,43,96,112],"annotations.":[20,97],"To":[21,36],"address":[22],"this":[23],"problem,":[24],"we":[25,45,58,134],"propose":[26,59,135],"a":[27,47,60,74,136],"novel":[28],"Contrastive":[29],"Instance":[30],"feature":[31,55,69,87],"mining":[32,63,70],"method,":[33],"named":[34],"CoIn.":[35],"better":[37],"identify":[38],"indistinguishable":[39],"features":[40],"learned":[41],"through":[42],"supervision,":[44],"design":[46],"Multi-Class":[48],"contrastive":[49,76],"learning":[50,77],"module":[51,71,78],"(MCcont)":[52],"to":[53,89,148,152],"enhance":[54],"discrimination.":[56],"Meanwhile,":[57],"feature-level":[61],"pseudo-label":[62],"framework":[64],"consisting":[65],"of":[66,93,120],"an":[67,130],"instance":[68],"(InF-Mining)":[72],"and":[73,106,125],"Labeled-to-Pseudo":[75],"(LPcont).":[79],"These":[80],"two":[81],"modules":[82],"exploit":[83],"latent":[84],"instances":[85],"in":[86,144],"space":[88],"supervise":[90],"the":[91,118,145,153],"training":[92,132],"Extensive":[98],"experiments":[99],"KITTI":[101,146],"dataset,":[102,105],"Waymo":[103],"open":[104],"nuScenes":[107],"dataset":[108,147],"show":[109],"that":[110],"annotations,":[113],"our":[114],"method":[115],"greatly":[116],"improves":[117],"performance":[119,150],"baseline":[121],"detectors:":[122],"CenterPoint,":[123],"Voxel-RCNN,":[124],"CasA.":[126],"Combining":[127],"CoIn":[128],"iterative":[131],"strategy,":[133],"CoIn++":[137],"pipeline,":[138],"which":[139],"requires":[140],"only":[141],"2%":[142],"achieve":[149],"comparable":[151],"fully":[154],"supervised":[155],"methods.":[156],"The":[157],"code":[158],"is":[159],"available":[160],"at":[161],"https://github.com/xmuqimingxia/CoIn.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}