{"id":"https://openalex.org/W4386076348","doi":"https://doi.org/10.1109/cvpr52729.2023.00475","title":"EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision","display_name":"EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4386076348","doi":"https://doi.org/10.1109/cvpr52729.2023.00475"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52729.2023.00475","doi":"https://doi.org/10.1109/cvpr52729.2023.00475","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.00475","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2303.15440","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jiahui Lei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiahui Lei","raw_affiliation_strings":["University of Pennsylvania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050560702","display_name":"Congyue Deng","orcid":"https://orcid.org/0000-0002-0177-218X","observed_orcids":["https://orcid.org/0000-0002-0177-218X"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Congyue Deng","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049609650","display_name":"Karl Schmeckpeper","orcid":"https://orcid.org/0000-0003-4989-2022","observed_orcids":["https://orcid.org/0000-0003-4989-2022"]},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karl Schmeckpeper","raw_affiliation_strings":["University of Pennsylvania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111954013","display_name":"Leonidas J. Guibas","orcid":"https://orcid.org/0000-0002-8315-4886","observed_orcids":["https://orcid.org/0000-0002-8315-4886"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leonidas Guibas","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050660826","display_name":"Kostas Daniilidis","orcid":"https://orcid.org/0000-0003-0498-0758","observed_orcids":["https://orcid.org/0000-0003-0498-0758"]},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kostas Daniilidis","raw_affiliation_strings":["University of Pennsylvania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1263,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82494255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4902","last_page":"4912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.013299999758601189,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.000699999975040555,"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/3d-object-segmentation","display_name":"3D object segmentation","score":0.9570000171661377},{"id":"https://openalex.org/keywords/unsupervised-segmentation","display_name":"unsupervised segmentation","score":0.9869999885559082},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7979999780654907},{"id":"https://openalex.org/keywords/shape-priors","display_name":"shape priors","score":0.9309999942779541},{"id":"https://openalex.org/keywords/geometric-segmentation","display_name":"geometric segmentation","score":0.382999986410141}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8153275847434998},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.7678910493850708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825106739997864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6781290173530579},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6569463014602661},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5878098011016846},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5760273337364197},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5682162046432495},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4412424564361572},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.44090670347213745},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.429796040058136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4088886082172394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20673492550849915},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.10988926887512207},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.10800164937973022},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr52729.2023.00475","doi":"https://doi.org/10.1109/cvpr52729.2023.00475","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.00475","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2303.15440","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.15440","pdf_url":"https://arxiv.org/pdf/2303.15440","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2303.15440","doi":"https://doi.org/10.48550/arxiv.2303.15440","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.15440","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,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2303.15440","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.15440","pdf_url":"https://arxiv.org/pdf/2303.15440","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3508111004","display_name":null,"funder_award_id":"W911NF-17-2-0181","funder_id":"https://openalex.org/F4320308476","funder_display_name":"Association of Research Libraries"},{"id":"https://openalex.org/G381355255","display_name":null,"funder_award_id":"1934960","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G4720003262","display_name":null,"funder_award_id":"N00014-22","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G4785748979","display_name":null,"funder_award_id":"W911NF2010080","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G5469137442","display_name":null,"funder_award_id":"W911NF-17-2-0181","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G5494771050","display_name":"RI: Medium: Learning to Map and Navigate with Vision and Language","funder_award_id":"2212433","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6673954204","display_name":"Collaborative Research: Visual Tactile Neural Fields for Active Digital Twin Generation","funder_award_id":"2220868","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G8183607530","display_name":null,"funder_award_id":"N00014-22-1-2677","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8895310522","display_name":null,"funder_award_id":"W911NF-17-2-0181","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G981263030","display_name":"HDR TRIPODS: FINPenn: Center for the Foundations of Information Processing at the University of Pennsylvania","funder_award_id":"1934960","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308476","display_name":"Association of Research Libraries","ror":"https://ror.org/053mpbz30"},{"id":"https://openalex.org/F4320308507","display_name":"Stanford University","ror":"https://ror.org/00f54p054"},{"id":"https://openalex.org/F4320309370","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10"},{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386076348.pdf","grobid_xml":"https://content.openalex.org/works/W4386076348.grobid-xml"},"referenced_works_count":95,"referenced_works":["https://openalex.org/W1655654231","https://openalex.org/W1953319329","https://openalex.org/W1986442311","https://openalex.org/W2000018820","https://openalex.org/W2190691619","https://openalex.org/W2332774326","https://openalex.org/W2563685048","https://openalex.org/W2594519801","https://openalex.org/W2769312834","https://openalex.org/W2785397276","https://openalex.org/W2788775653","https://openalex.org/W2900364424","https://openalex.org/W2904332125","https://openalex.org/W2904383797","https://openalex.org/W2909750748","https://openalex.org/W2925338335","https://openalex.org/W2948410735","https://openalex.org/W2952515999","https://openalex.org/W2962759414","https://openalex.org/W2962849139","https://openalex.org/W2963088756","https://openalex.org/W2964339842","https://openalex.org/W3009443497","https://openalex.org/W3012728190","https://openalex.org/W3034550906","https://openalex.org/W3034949383","https://openalex.org/W3035624836","https://openalex.org/W3036234074","https://openalex.org/W3036737467","https://openalex.org/W3094480455","https://openalex.org/W3103242287","https://openalex.org/W3116959466","https://openalex.org/W3117476483","https://openalex.org/W3122401711","https://openalex.org/W3158545781","https://openalex.org/W3163187545","https://openalex.org/W3166573884","https://openalex.org/W3170500059","https://openalex.org/W3171974924","https://openalex.org/W3173631825","https://openalex.org/W3174960126","https://openalex.org/W3176751833","https://openalex.org/W3177426614","https://openalex.org/W3177826989","https://openalex.org/W3187051590","https://openalex.org/W3187637097","https://openalex.org/W3201922548","https://openalex.org/W3203426856","https://openalex.org/W3204034406","https://openalex.org/W3214000333","https://openalex.org/W4225557479","https://openalex.org/W4280559326","https://openalex.org/W4281877847","https://openalex.org/W4283067636","https://openalex.org/W4283075128","https://openalex.org/W4285102336","https://openalex.org/W4297805934","https://openalex.org/W4297846303","https://openalex.org/W4312246270","https://openalex.org/W4312251507","https://openalex.org/W4312259096","https://openalex.org/W4312274934","https://openalex.org/W4312638666","https://openalex.org/W4312700461","https://openalex.org/W4312799694","https://openalex.org/W4312903773","https://openalex.org/W4313148744","https://openalex.org/W4313166301","https://openalex.org/W4313172691","https://openalex.org/W4313427342","https://openalex.org/W4319300695","https://openalex.org/W4323057694","https://openalex.org/W4328029535","https://openalex.org/W4383097449","https://openalex.org/W4383108224","https://openalex.org/W4383108699","https://openalex.org/W4383109180","https://openalex.org/W4394671432","https://openalex.org/W6747912417","https://openalex.org/W6749031613","https://openalex.org/W6763229141","https://openalex.org/W6779789776","https://openalex.org/W6784313998","https://openalex.org/W6784339035","https://openalex.org/W6799787682","https://openalex.org/W6802538487","https://openalex.org/W6810534297","https://openalex.org/W6839117414","https://openalex.org/W6839214563","https://openalex.org/W6840994586","https://openalex.org/W6843343107","https://openalex.org/W6846033935","https://openalex.org/W6846431998","https://openalex.org/W6846456916","https://openalex.org/W7133232860"],"related_works":["https://openalex.org/W7161137932","https://openalex.org/W4302011424","https://openalex.org/W7133227267","https://openalex.org/W4416548527","https://openalex.org/W7133232860","https://openalex.org/W4399411440","https://openalex.org/W4387559713","https://openalex.org/W2770302340","https://openalex.org/W4415709289","https://openalex.org/W4309870671"],"abstract_inverted_index":{"We":[0,28,39,85],"introduce":[1,49],"Equivariant":[2],"Neural":[3],"Field":[4],"Expectation":[5],"Maximization":[6],"(EFEM),":[7],"a":[8,70,87],"simple,":[9],"effective,":[10],"and":[11,92,99,108,121,135],"robust":[12,122],"geometric":[13],"algorithm":[14,73],"that":[15,45,74,94,116],"can":[16,75],"segment":[17],"objects":[18],"in":[19,44,64,102],"3D":[20],"scenes":[21,101,126],"without":[22],"annotations":[23],"or":[24],"training":[25],"on":[26],"scenes.":[27],"achieve":[29],"such":[30],"unsupervised":[31],"segmentation":[32,78],"by":[33,61],"exploiting":[34],"single":[35],"object":[36,65,97],"shape":[37,51,83],"priors.":[38],"make":[40],"two":[41],"novel":[42,71,88,100],"steps":[43],"direction.":[46],"First,":[47],"we":[48,68],"equivariant":[50,82],"representations":[52],"to":[53,56,104],"this":[54],"problem":[55],"eliminate":[57],"the":[58,62,81,106,128],"complexity":[59],"induced":[60],"variation":[63],"configuration.":[66],"Second,":[67],"propose":[69],"EM":[72],"iteratively":[76],"refine":[77],"masks":[79],"using":[80],"prior.":[84],"collect":[86],"real":[89],"dataset":[90],"Chairs":[91],"Mugs":[93],"contains":[95],"various":[96],"configurations":[98],"order":[103],"verify":[105],"effectiveness":[107],"robustness":[109],"of":[110],"our":[111,117],"method.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"method":[118],"achieves":[119],"consistent":[120],"performance":[123],"across":[124],"different":[125],"where":[127],"(weakly)":[129],"supervised":[130],"methods":[131],"may":[132],"fail.":[133],"Code":[134],"data":[136],"available":[137],"at":[138],"https://www.cis.upenn.edu/~leijh/projects/EFEM":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-08-23T00:00:00"}