{"id":"https://openalex.org/W2994303237","doi":"https://doi.org/10.1109/mlsp.2019.8918924","title":"AH-CoLT: an AI-Human Co-Labeling Toolbox to Augment Efficient Groundtruth Generation","display_name":"AH-CoLT: an AI-Human Co-Labeling Toolbox to Augment Efficient Groundtruth Generation","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2994303237","doi":"https://doi.org/10.1109/mlsp.2019.8918924","mag":"2994303237"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2019.8918924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2019.8918924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5101931898","display_name":"Xiaofei Huang","orcid":"https://orcid.org/0000-0002-2429-0546"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaofei Huang","raw_affiliation_strings":["Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067250202","display_name":"Behnaz Rezaei","orcid":"https://orcid.org/0000-0002-5892-5001"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behnaz Rezaei","raw_affiliation_strings":["Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031787107","display_name":"Sarah Ostadabbas","orcid":"https://orcid.org/0000-0002-2216-9988"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarah Ostadabbas","raw_affiliation_strings":["Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101931898"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.4049,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66465402,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T10812","display_name":"Human Pose and Action Recognition","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/T11714","display_name":"Multimodal Machine Learning 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"}}],"keywords":[{"id":"https://openalex.org/keywords/toolbox","display_name":"Toolbox","score":0.8651546239852905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8472207188606262},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7026898860931396},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.693026602268219},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5634394288063049},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.542551577091217},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48846665024757385},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4817926287651062},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4521867632865906},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42917585372924805},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4018867611885071},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.22245573997497559}],"concepts":[{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.8651546239852905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8472207188606262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7026898860931396},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.693026602268219},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5634394288063049},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.542551577091217},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48846665024757385},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4817926287651062},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4521867632865906},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42917585372924805},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4018867611885071},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.22245573997497559},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2019.8918924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2019.8918924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)","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":21,"referenced_works":["https://openalex.org/W1739260168","https://openalex.org/W1980434677","https://openalex.org/W2110764733","https://openalex.org/W2149489787","https://openalex.org/W2307770531","https://openalex.org/W2483862638","https://openalex.org/W2546643512","https://openalex.org/W2573098616","https://openalex.org/W2742737904","https://openalex.org/W2767308488","https://openalex.org/W2795262365","https://openalex.org/W2796347433","https://openalex.org/W2799211965","https://openalex.org/W2898898181","https://openalex.org/W2919115771","https://openalex.org/W2963197583","https://openalex.org/W2963998561","https://openalex.org/W2964105113","https://openalex.org/W3101855769","https://openalex.org/W4293584584","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W4205140848","https://openalex.org/W2068663075","https://openalex.org/W2978678743","https://openalex.org/W2797837731","https://openalex.org/W4393677513","https://openalex.org/W4390832911","https://openalex.org/W829257147","https://openalex.org/W4385302116","https://openalex.org/W3081389670","https://openalex.org/W3000197790"],"abstract_inverted_index":{"Two":[0],"critical":[1],"aspects":[2],"in":[3,33,83,169],"building":[4],"successful":[5],"AI-based":[6],"visual":[7],"recognizers":[8,125],"are":[9,161],"having":[10,48],"access":[11],"to":[12,65,163,176],"enough":[13],"representative":[14],"labeled":[15,53],"data":[16,54],"and":[17,59,131,167],"creating":[18],"a":[19,49,127,170],"robust":[20],"learning":[21,35,86],"paradigm.":[22],"The":[23,154],"need":[24,89],"for":[25,43,57,78,110],"large":[26,50],"scale":[27],"training":[28],"set":[29,51],"is":[30,55],"clearly":[31],"understood":[32],"supervised":[34],"models":[36],"especially":[37],"with":[38],"deep":[39],"structure;":[40],"however,":[41],"even":[42],"semi-supervised":[44],"or":[45,74],"unsupervised":[46],"algorithms":[47],"of":[52,68,122,141],"crucial":[56],"quantitative":[58],"comparative":[60],"model":[61],"performance":[62,140],"evaluation.":[63],"Reaching":[64],"the":[66,79,84,120,139,142,177],"limits":[67],"manual":[69],"annotation":[70],"by":[71,118,182],"human":[72,151],"experts":[73],"via":[75],"crowd":[76,183],"sourcing,":[77],"next":[80],"big":[81],"breakthroughs":[82],"machine":[85],"domain":[87],"we":[88,137],"much":[90],"more":[91,165,171],"data/label":[92],"pairs.":[93],"To":[94],"address":[95],"this":[96,135],"need,":[97],"our":[98,159],"AI-Human":[99],"Co-Labeling":[100],"Toolbox":[101],"(AH-CoLT)":[102],"presents":[103],"an":[104],"efficient":[105],"semi-automatic":[106],"groundtruth":[107,116,148,178],"generation":[108],"framework":[109,145],"unlabeled":[111],"images/videos.":[112],"AH-CoLT":[113,144],"enables":[114],"accurate":[115,166],"labeling":[117],"incorporating":[119],"outcomes":[121],"state-of-the-art":[123],"AI":[124],"into":[126],"time-efficient":[128,172],"human-based":[129],"review":[130],"revise":[132],"process.":[133],"In":[134],"paper,":[136],"evaluated":[138],"proposed":[143],"on":[146,150],"generating":[147],"labels":[149,157,180],"pose":[152,156,179],"images.":[153],"resultant":[155],"using":[158],"toolbox":[160],"shown":[162],"be":[164],"achieved":[168],"way,":[173],"when":[174],"compared":[175],"generated":[181],"sourcing.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}