{"id":"https://openalex.org/W4402352124","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650081","title":"SAMPose: Multi-Person Pose Estimation based on Segment Anything Model","display_name":"SAMPose: Multi-Person Pose Estimation based on Segment Anything Model","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352124","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650081"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650081","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5014312058","display_name":"Jiechen Li","orcid":"https://orcid.org/0000-0002-9154-7161"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiechen Li","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067821474","display_name":"Ruoshan Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoshan Kong","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107934554","display_name":"Feng Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Liu","raw_affiliation_strings":["Wuhan University,School of Computer Science,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,School of Computer Science,Wuhan,China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987999796867371,"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":0.9921000003814697,"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/pose","display_name":"Pose","score":0.7530192136764526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6633120775222778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5781351327896118},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5021941661834717},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4831823408603668},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10688328742980957}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7530192136764526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6633120775222778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5781351327896118},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5021941661834717},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4831823408603668},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10688328742980957},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650081","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2080873731","https://openalex.org/W2113325037","https://openalex.org/W2175012183","https://openalex.org/W2194775991","https://openalex.org/W2307770531","https://openalex.org/W2382036597","https://openalex.org/W2559085405","https://openalex.org/W2565639579","https://openalex.org/W2916798096","https://openalex.org/W2952122856","https://openalex.org/W2952819818","https://openalex.org/W2962954622","https://openalex.org/W2963402313","https://openalex.org/W2963781481","https://openalex.org/W2964221239","https://openalex.org/W2964304707","https://openalex.org/W3109585842","https://openalex.org/W3110017807","https://openalex.org/W4226261765","https://openalex.org/W4293584584","https://openalex.org/W4311726887","https://openalex.org/W4313127332","https://openalex.org/W4324128075","https://openalex.org/W4327486298","https://openalex.org/W4366196888","https://openalex.org/W4366781106","https://openalex.org/W4386071543","https://openalex.org/W4386632015","https://openalex.org/W4390190334","https://openalex.org/W4390874575","https://openalex.org/W4391109864","https://openalex.org/W4402916242","https://openalex.org/W6730410022","https://openalex.org/W6850787431","https://openalex.org/W6851932778"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Recently,":[0],"there":[1],"have":[2],"been":[3],"numerous":[4],"advances":[5],"in":[6,117,146,178],"the":[7,31,35,42,61,67,73,86,102,115,118,131,136,143,147,150,161,169,174,179],"field":[8],"of":[9,34,63,88,130,142,198,230],"2D":[10],"human":[11,22,36],"pose":[12,89,97],"estimation.":[13,90],"We":[14],"observed":[15],"that":[16,100],"conventional":[17],"methods":[18],"generally":[19],"use":[20,101,111],"a":[21,79,95,155,165,187],"detector":[23],"or":[24],"rely":[25],"on":[26,160,190,202],"ground":[27],"truth":[28],"to":[29,81,106,113,138,213],"get":[30],"bounding":[32,43],"box":[33,44],"subject.":[37],"The":[38],"image":[39],"cropped":[40],"by":[41],"is":[45,125],"then":[46],"directly":[47],"used":[48,126],"as":[49,127,221],"input":[50,132],"for":[51,84],"training.":[52],"This":[53],"may":[54],"introduce":[55,93],"irrelevant":[56],"and":[57,120,200,204,217,224],"distracting":[58],"information,":[59],"affecting":[60],"accuracy":[62,87],"prediction.":[64],"Upon":[65],"recognizing":[66],"newly":[68],"proposed":[69],"large":[70],"vision":[71],"model,":[72],"Segment":[74,103],"Anything":[75,104],"Model,":[76],"we":[77,92,110,208],"devised":[78],"method":[80,99,212],"leverage":[82],"segmentation":[83,123],"improving":[85],"Consequently,":[91],"SAMPose,":[94],"multi-person":[96],"estimation":[98],"Model":[105],"improve":[107],"performance.":[108],"First,":[109],"SAM":[112],"segment":[114],"person":[116,144],"image,":[119,133],"then,":[121],"this":[122],"mask":[124],"an":[128],"enhancement":[129],"it":[134],"guide":[135],"network":[137],"be":[139],"more":[140],"aware":[141],"part":[145],"picture.":[148],"In":[149],"experiment,":[151],"our":[152,184,194,210],"SAMPose-m":[153,172],"achieves":[154,186],"62.3%":[156],"Average":[157],"Precision":[158],"(AP)":[159],"COCO-wholebody":[162,191],"benchmark,":[163],"representing":[164],"4.1%":[166],"improvement":[167,227],"over":[168],"baseline.":[170],"Our":[171],"beats":[173],"SOTA":[175],"model":[176,181,185,195],"DWPose":[177],"same":[180],"size.":[182],"Additionally,":[183],"75.4%":[188],"AP":[189],"benchmark.":[192],"Moreover,":[193],"exhibits":[196],"improvements":[197],"1.4%":[199],"1.9%":[201],"Crowdpose":[203],"MPII,":[205],"respectively.":[206],"Furthermore,":[207],"apply":[209],"SAM-based":[211],"various":[214],"other":[215],"famous":[216],"milestone":[218],"methods,":[219],"such":[220],"HRNet,":[222],"SimCC,":[223],"DeepPose,":[225],"demonstrating":[226],"across":[228],"all":[229],"them.":[231]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
