{"id":"https://openalex.org/W4402352892","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650303","title":"A Complementary Action Recognition Network based on Conv-Transformer","display_name":"A Complementary Action Recognition Network based on Conv-Transformer","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352892","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650303"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650303","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650303","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/A5019436850","display_name":"Linfu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linfu Liu","raw_affiliation_strings":["Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326349","display_name":"Lin Qi","orcid":"https://orcid.org/0000-0002-4558-6741"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Qi","raw_affiliation_strings":["Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101112071","display_name":"Yun Tie","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Tie","raw_affiliation_strings":["Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University,School of Electrical and Information Engineering,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003326044","display_name":"Chengwu Liang","orcid":"https://orcid.org/0000-0001-6291-5212"},"institutions":[{"id":"https://openalex.org/I4210113703","display_name":"Henan University of Urban Construction","ror":"https://ror.org/01x1skr92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113703"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengwu Liang","raw_affiliation_strings":["Henan University of Urban Construction,School of Electrical and Control Engineering,Pingdingshan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Henan University of Urban Construction,School of Electrical and Control Engineering,Pingdingshan,China","institution_ids":["https://openalex.org/I4210113703"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16800478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9994000196456909,"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":0.9988999962806702,"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/computer-science","display_name":"Computer science","score":0.639724612236023},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.595431923866272},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5694960951805115},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4620515704154968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32777947187423706},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1621018648147583},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14551937580108643},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.1287919580936432},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06901815533638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.639724612236023},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.595431923866272},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5694960951805115},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4620515704154968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32777947187423706},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1621018648147583},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14551937580108643},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.1287919580936432},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06901815533638},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650303","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650303","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":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1983364832","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2943568678","https://openalex.org/W2963091558","https://openalex.org/W2963155035","https://openalex.org/W2963315828","https://openalex.org/W2963524571","https://openalex.org/W2963820951","https://openalex.org/W2964350391","https://openalex.org/W2990503944","https://openalex.org/W2997747012","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3096833468","https://openalex.org/W3126721948","https://openalex.org/W3159663321","https://openalex.org/W3159778524","https://openalex.org/W3160694286","https://openalex.org/W3162457465","https://openalex.org/W3168663926","https://openalex.org/W3170874841","https://openalex.org/W3175544090","https://openalex.org/W3210279979","https://openalex.org/W4214614183","https://openalex.org/W4312340826","https://openalex.org/W4312349930","https://openalex.org/W4312442876","https://openalex.org/W4312560592","https://openalex.org/W4389666313","https://openalex.org/W6739901393","https://openalex.org/W6796931752"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W1576128429","https://openalex.org/W2269464716","https://openalex.org/W4283332100"],"abstract_inverted_index":{"As":[0],"one":[1],"of":[2,10,65,121,154,159,165,169,179,188,192],"the":[3,8,34,56,63,155,160,163,166,170,177,180,186,189,197],"important":[4,103],"research":[5],"directions":[6],"in":[7,19,29,68,200,224],"field":[9],"computer":[11],"vision,":[12],"action":[13,93,129,139,142,147],"recognition":[14,130],"has":[15],"extensive":[16,206],"application":[17],"value":[18],"today's":[20],"internet.":[21],"Since":[22],"traditional":[23],"convolutional":[24],"neural":[25],"networks":[26,131],"performed":[27],"well":[28],"processing":[30],"local":[31,77,110,156],"features,":[32],"but":[33,59,182],"ability":[35,74],"to":[36,75,101,132],"process":[37,76],"global":[38,53,107,167],"information":[39,64,78,111],"and":[40,70,72,109,126,141,162,204,213,215,218,227],"performance":[41,135],"on":[42,136,208],"large-scale":[43],"datasets":[44],"is":[45,79],"weaker.":[46],"The":[47],"transformer-based":[48],"model":[49,52,199],"can":[50],"efficiently":[51],"features":[54,104],"through":[55,185],"attention":[57],"mechanism,":[58],"it":[60],"cannot":[61],"capture":[62],"dynamic":[66],"changes":[67],"spatial":[69,193],"temporal,":[71],"its":[73],"relatively":[80],"weak.":[81],"To":[82],"do":[83],"this,":[84],"this":[85,114],"paper":[86,115],"proposed":[87,198],"a":[88,96,117],"Conv-Transformer":[89,98],"Residual":[90,99],"Network(CTRN)":[91],"for":[92,144],"recognition,":[94],"used":[95],"unique":[97],"Module(CTRM)":[100],"extract":[102],"that":[105],"combine":[106],"contextual":[108],"efficiently.":[112],"Furthermore":[113],"designed":[116],"loss":[118,125],"function":[119],"consisting":[120],"pixel":[122],"loss,":[123],"cross-entropy":[124],"CIOU_Loss,":[127],"allows":[128],"simultaneously":[133],"optimise":[134],"pixel-level":[137],"prediction,":[138],"classification":[140],"localisation":[143],"more":[145],"accurate":[146],"recognition.":[148],"This":[149],"network":[150],"makes":[151],"full":[152],"use":[153],"modeling":[157,168],"capabilities":[158],"cnn":[161],"advantages":[164],"Transformer,":[171],"which":[172],"not":[173],"only":[174],"effectively":[175],"reduces":[176],"complexity":[178],"model,":[181],"also":[183],"breaks":[184],"limitations":[187],"Transformer's":[190],"lack":[191],"sensing":[194],"bias.":[195],"Training":[196],"an":[201],"end-to-end":[202],"manner":[203],"conducted":[205],"experiments":[207],"two":[209],"typical":[210],"datasets,":[211],"Kinetics-400":[212],"Something":[214,216],"V2,":[217],"outperformed":[219],"some":[220],"representative":[221],"state-of-the-art":[222],"models":[223],"both":[225],"qualitative":[226],"quantitative":[228],"evaluations.":[229]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
