{"id":"https://openalex.org/W4416252038","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227865","title":"Instance-Specific Learning for Skeleton-Based Action Recognition with Varying Data Quality","display_name":"Instance-Specific Learning for Skeleton-Based Action Recognition with Varying Data Quality","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252038","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227865"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5013970731","display_name":"Liang Zhu","orcid":"https://orcid.org/0000-0002-0921-7076"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Zhu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083200173","display_name":"Huaijun Qiu","orcid":"https://orcid.org/0009-0007-2201-6264"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaijun Qiu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063569052","display_name":"Zhuo Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Yan","raw_affiliation_strings":["Shenyang Aerospace University,School of Artificial Intelligence,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Artificial Intelligence,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101969200","display_name":"Cuiwei Liu","orcid":"https://orcid.org/0000-0003-4279-4841"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuiwei Liu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125904092"],"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":0.9778000116348267,"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":0.9778000116348267,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.005900000222027302,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/discriminative-model","display_name":"Discriminative model","score":0.7143999934196472},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6284000277519226},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.521399974822998},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.48019999265670776},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44359999895095825},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4320000112056732},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.39629998803138733},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.31779998540878296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7504000067710876},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7143999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7127000093460083},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6284000277519226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5964999794960022},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.521399974822998},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.31779998540878296},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322539","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1983705368","https://openalex.org/W2056898157","https://openalex.org/W2098339052","https://openalex.org/W2106996050","https://openalex.org/W2126579184","https://openalex.org/W2194775991","https://openalex.org/W2593146028","https://openalex.org/W2799409595","https://openalex.org/W2883780447","https://openalex.org/W2916798096","https://openalex.org/W2944006115","https://openalex.org/W2949805239","https://openalex.org/W2962820842","https://openalex.org/W2963076818","https://openalex.org/W2964134613","https://openalex.org/W2982083293","https://openalex.org/W2996249958","https://openalex.org/W3035050855","https://openalex.org/W3047772167","https://openalex.org/W3128140926","https://openalex.org/W3169535809","https://openalex.org/W3170431411","https://openalex.org/W3185273257","https://openalex.org/W3203634062","https://openalex.org/W4281249524","https://openalex.org/W4294643314","https://openalex.org/W4390871806","https://openalex.org/W4393170627","https://openalex.org/W4394004556","https://openalex.org/W4402727383","https://openalex.org/W4409077121"],"related_works":[],"abstract_inverted_index":{"Skeleton-based":[0],"action":[1,180],"recognition":[2,181],"technology":[3],"has":[4],"gained":[5],"significant":[6],"attention":[7],"and":[8,72,130,145,161,165],"made":[9],"great":[10],"progress":[11],"in":[12,125],"recent":[13],"years.":[14],"However,":[15],"the":[16,24,49,84,95,98,105,158,174,179],"performance":[17,182],"of":[18,26,60,86,107,154],"existing":[19],"methods":[20,149],"declines":[21],"significantly":[22,177],"when":[23,56],"quality":[25,156],"skeleton":[27,58,152],"data":[28,59,153],"extracted":[29],"by":[30,88],"pose":[31,147],"estimation":[32,148],"algorithms":[33],"varies.":[34],"To":[35],"address":[36],"this":[37,39],"issue,":[38],"study":[40],"proposes":[41],"an":[42],"instance-specific":[43],"learning":[44],"method":[45,119,176],"aimed":[46],"at":[47,109],"enhancing":[48],"model\u2019s":[50],"ability":[51],"to":[52,139,150],"learn":[53],"discriminative":[54],"features":[55],"handling":[57],"varying":[61,155],"quality.":[62],"We":[63,142],"introduce":[64],"a":[65,73,122],"Dynamic":[66],"Instance":[67,75],"Discriminability":[68],"Assessment":[69],"(DIDA)":[70],"mechanism":[71,81],"Staged":[74],"Weighting":[76],"(SIW)":[77],"strategy.":[78],"The":[79,101,170],"DIDA":[80],"dynamically":[82],"evaluates":[83],"discriminability":[85],"instances":[87,108],"combining":[89],"prior":[90],"knowledge":[91],"with":[92],"feedback":[93],"from":[94,157],"model":[96],"during":[97,128,136],"training":[99,111,129],"process.":[100],"SIW":[102],"strategy":[103],"adjusts":[104],"weights":[106],"different":[110],"stages":[112],"based":[113],"on":[114],"their":[115],"discriminability.":[116],"Notably,":[117],"our":[118],"requires":[120],"only":[121],"minimal":[123],"increase":[124],"computational":[126,134,185],"cost":[127],"incurs":[131],"no":[132],"additional":[133],"overhead":[135],"testing":[137],"compared":[138],"baseline":[140],"models.":[141],"utilized":[143],"Pifpaf":[144],"HR-Net":[146],"extract":[151],"NTU60,":[159],"NTU120,":[160],"HMDB51":[162],"video":[163],"datasets":[164],"conducted":[166],"extensive":[167],"experimental":[168],"validation.":[169],"results":[171],"indicate":[172],"that":[173],"proposed":[175],"enhances":[178],"while":[183],"maintaining":[184],"efficiency.":[186]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}