{"id":"https://openalex.org/W1978511849","doi":"https://doi.org/10.1109/cvpr.2011.5995470","title":"Joint segmentation and classification of human actions in video","display_name":"Joint segmentation and classification of human actions in video","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W1978511849","doi":"https://doi.org/10.1109/cvpr.2011.5995470","mag":"1978511849"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2011.5995470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","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/A5038957718","display_name":"Minh Hoai","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Minh Hoai","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie Mellon University Pittsburgh, PA 15213"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie Mellon University Pittsburgh, PA 15213","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103239171","display_name":"Zhenzhong Lan","orcid":"https://orcid.org/0000-0003-4763-6148"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhen-Zhong Lan","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie Mellon University Pittsburgh, PA 15213"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie Mellon University Pittsburgh, PA 15213","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108127869","display_name":"Fernando De la Torre","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fernando De la Torre","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie Mellon University Pittsburgh, PA 15213"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie Mellon University Pittsburgh, PA 15213","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038957718"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":19.6192,"has_fulltext":false,"cited_by_count":237,"citation_normalized_percentile":{"value":0.99558781,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3265","last_page":"3272"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9979000091552734,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8103402853012085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7707021832466125},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7228788733482361},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6927391886711121},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5338377356529236},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.518754243850708},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4669540822505951},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4338376522064209},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4333900511264801},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4258703291416168},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.41961729526519775},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.4180223345756531},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40415236353874207},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.2794896364212036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8103402853012085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7707021832466125},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7228788733482361},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6927391886711121},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5338377356529236},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.518754243850708},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4669540822505951},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4338376522064209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4333900511264801},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4258703291416168},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.41961729526519775},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.4180223345756531},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40415236353874207},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2794896364212036}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2011.5995470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.206.5280","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.5280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.andrew.cmu.edu/user/minhhoan/papers/SegReg_CVPR11.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.715.8571","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.715.8571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www3.cs.stonybrook.edu/%7Eminhhoai/papers/SegReg_CVPR11.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1498368596","https://openalex.org/W1593040460","https://openalex.org/W1839676122","https://openalex.org/W2036502167","https://openalex.org/W2045893916","https://openalex.org/W2048182576","https://openalex.org/W2097180427","https://openalex.org/W2112343299","https://openalex.org/W2112724113","https://openalex.org/W2127893047","https://openalex.org/W2133441419","https://openalex.org/W2137981002","https://openalex.org/W2139686264","https://openalex.org/W2139688922","https://openalex.org/W2142194269","https://openalex.org/W2142261432","https://openalex.org/W2142561902","https://openalex.org/W2143023146","https://openalex.org/W2146634731","https://openalex.org/W2155268664","https://openalex.org/W2157791002","https://openalex.org/W2158169396","https://openalex.org/W2168347767","https://openalex.org/W2172207578","https://openalex.org/W2533739470","https://openalex.org/W2535977253","https://openalex.org/W2539912446","https://openalex.org/W3004533406","https://openalex.org/W6630009274","https://openalex.org/W6635689683","https://openalex.org/W6639141311","https://openalex.org/W6659456710","https://openalex.org/W6677031804","https://openalex.org/W6680007087","https://openalex.org/W6680627905","https://openalex.org/W6680805354","https://openalex.org/W6682953061","https://openalex.org/W6685130317","https://openalex.org/W6728799417"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2981954115","https://openalex.org/W2182357018"],"abstract_inverted_index":{"Automatic":[0],"video":[1,20,68],"segmentation":[2,21,30,69],"and":[3,22,70,132],"action":[4,23,41,71],"recognition":[5,24,42],"has":[6,98],"been":[7,99],"a":[8,35,54,88,111],"long-standing":[9],"problem":[10],"in":[11,16,102],"computer":[12],"vision.":[13],"Much":[14],"work":[15],"the":[17,39,59,62,93,116,119,136],"literature":[18],"treats":[19],"as":[25],"two":[26],"independent":[27],"problems;":[28],"while":[29],"is":[31,43,85,107,121],"often":[32],"done":[33,122],"without":[34],"temporal":[36,90],"model":[37,96],"of":[38,61,79,92,138],"activity,":[40],"usually":[44],"performed":[45,108],"on":[46,77,87,129],"pre-segmented":[47],"clips.":[48],"In":[49],"this":[50],"paper":[51],"we":[52],"propose":[53],"novel":[55],"method":[56,84],"that":[57,97],"avoids":[58],"limitations":[60],"above":[63],"approaches":[64,75],"by":[65],"jointly":[66],"performing":[67],"recognition.":[72,104],"Unlike":[73],"standard":[74],"based":[76,86],"extensions":[78],"dynamic":[80,125],"Bayesian":[81],"networks,":[82],"our":[83,139],"discriminative":[89],"extension":[91],"spatial":[94],"bag-of-words":[95],"very":[100],"popular":[101],"object":[103],"The":[105],"classification":[106],"robustly":[109],"within":[110],"multi-class":[112],"SVM":[113],"framework":[114],"whereas":[115],"inference":[117],"over":[118],"segments":[120],"efficiently":[123],"with":[124],"programming.":[126],"Experimental":[127],"results":[128],"honeybee,":[130],"Weizmann,":[131],"Hollywood":[133],"datasets":[134],"illustrate":[135],"benefits":[137],"approach":[140],"compared":[141],"to":[142],"state-of-the-art":[143],"methods.":[144]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":18},{"year":2017,"cited_by_count":27},{"year":2016,"cited_by_count":35},{"year":2015,"cited_by_count":47},{"year":2014,"cited_by_count":32},{"year":2013,"cited_by_count":21},{"year":2012,"cited_by_count":22}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}