{"id":"https://openalex.org/W4386596936","doi":"https://doi.org/10.1109/icip49359.2023.10222848","title":"Frequency Enhancement Network for Efficient Compressed Video Action Recognition","display_name":"Frequency Enhancement Network for Efficient Compressed Video Action Recognition","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386596936","doi":"https://doi.org/10.1109/icip49359.2023.10222848"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222848","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5089582342","display_name":"Yue Ming","orcid":"https://orcid.org/0000-0001-7105-4207"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Ming","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335131","display_name":"Xiong Lu","orcid":"https://orcid.org/0000-0001-6367-430X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Xiong","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112108150","display_name":"Xia Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098582","display_name":"ZTE (China)","ror":"https://ror.org/00rjhhq63","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Jia","raw_affiliation_strings":["ZTE Corpration,Nanjing,PR China,210008"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZTE Corpration,Nanjing,PR China,210008","institution_ids":["https://openalex.org/I4210098582"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101092469","display_name":"Qingfang Zheng","orcid":"https://orcid.org/0009-0006-7568-9318"},"institutions":[{"id":"https://openalex.org/I4210098582","display_name":"ZTE (China)","ror":"https://ror.org/00rjhhq63","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210098582"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingfang Zheng","raw_affiliation_strings":["ZTE Corpration,Nanjing,PR China,210008"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZTE Corpration,Nanjing,PR China,210008","institution_ids":["https://openalex.org/I4210098582"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009406615","display_name":"Jiangwan Zhou","orcid":"https://orcid.org/0009-0005-7015-2557"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangwan Zhou","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101488263","display_name":"Fan Feng","orcid":"https://orcid.org/0000-0002-0936-5255"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Feng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014620591","display_name":"Nannan Hu","orcid":"https://orcid.org/0000-0002-4080-7891"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nannan Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering,Beijing,PR China,100876","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"825","last_page":"829"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8031370639801025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5899166464805603},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5796088576316833},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5756597518920898},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5664129853248596},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5626987218856812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5097576975822449},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4839065968990326},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4838665723800659},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.45062142610549927},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4487876892089844},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4068663716316223},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34719228744506836},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11554712057113647},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10793697834014893},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09222611784934998}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8031370639801025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5899166464805603},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5796088576316833},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5756597518920898},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5664129853248596},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5626987218856812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5097576975822449},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4839065968990326},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4838665723800659},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.45062142610549927},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4487876892089844},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4068663716316223},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34719228744506836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11554712057113647},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10793697834014893},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09222611784934998},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222848","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5699999928474426}],"awards":[{"id":"https://openalex.org/G5457963859","display_name":null,"funder_award_id":"62076030","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G817815678","display_name":null,"funder_award_id":"L201023","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8515333292","display_name":null,"funder_award_id":"Grant No. 62076030","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320313887","display_name":"ZTE Corporation","ror":"https://ror.org/00rjhhq63"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W2126579184","https://openalex.org/W2194775991","https://openalex.org/W2507009361","https://openalex.org/W2619947201","https://openalex.org/W2961193895","https://openalex.org/W2963370182","https://openalex.org/W2963524571","https://openalex.org/W2963901365","https://openalex.org/W2990152177","https://openalex.org/W2990503944","https://openalex.org/W3034771037","https://openalex.org/W3035303837","https://openalex.org/W3036559109","https://openalex.org/W3092651603","https://openalex.org/W3107289539","https://openalex.org/W3114936184","https://openalex.org/W3175528717","https://openalex.org/W3187343781","https://openalex.org/W3211119616","https://openalex.org/W4312356418","https://openalex.org/W4312560592","https://openalex.org/W4312757006","https://openalex.org/W6803603938","https://openalex.org/W6880932415"],"related_works":["https://openalex.org/W2168109476","https://openalex.org/W2486460843","https://openalex.org/W1968121071","https://openalex.org/W2020254986","https://openalex.org/W2061647633","https://openalex.org/W2038374617","https://openalex.org/W2074225891","https://openalex.org/W1992540108","https://openalex.org/W2977517636","https://openalex.org/W2810129309"],"abstract_inverted_index":{"The":[0],"existing":[1],"frequency-based":[2],"action":[3,41,121],"recognition":[4],"methods":[5,140],"achieve":[6],"impressive":[7],"performance":[8],"in":[9,81],"improving":[10],"efficiency.":[11,143],"However,":[12],"they":[13],"ignore":[14],"the":[15,59,63,68,76,96,102,115],"low-frequency":[16,89,117],"texture":[17,90],"and":[18,50,67,91,128],"edge":[19,92],"clues,":[20,93],"leading":[21],"to":[22,87],"accuracy":[23,136],"degradation.":[24],"To":[25],"address":[26],"this":[27],"problem,":[28],"we":[29],"propose":[30],"a":[31,44,51],"novel":[32],"frequency":[33,52,71,83,108],"enhancement":[34],"(FE)":[35],"block":[36,104],"for":[37,120],"efficient":[38],"compressed":[39],"video":[40],"recognition,":[42],"including":[43],"temporal-channel":[45],"two-heads":[46],"attention":[47],"(TCTHA)":[48],"module":[49,61,78],"overlapping":[53],"group":[54],"convolution":[55],"(FOGC)":[56],"module.":[57],"First,":[58],"TCTHA":[60],"emphasizes":[62],"inter-frame":[64],"temporal":[65],"context":[66],"inner-frame":[69],"informative":[70],"semantics":[72,119],"by":[73],"attention.":[74],"Then,":[75],"FOGC":[77],"groups":[79],"channels":[80],"different":[82],"bands":[84],"with":[85,107,138,141],"overlap,":[86],"extract":[88],"while":[94],"maintaining":[95],"interaction":[97],"of":[98],"groups.":[99],"We":[100],"integrate":[101],"FE":[103],"into":[105],"2D-CNNs":[106],"I-frame":[109],"input,":[110],"termed":[111],"FENet,":[112],"focusing":[113],"on":[114,124],"pivotal":[116],"spatio-temporal":[118],"recognition.":[122],"Experiments":[123],"HMDB-51,":[125],"UCF-101,":[126],"Kinetics-400,":[127],"Kinetics-700":[129],"verify":[130],"that":[131],"our":[132],"FENet":[133],"achieves":[134],"comparable":[135],"compared":[137],"RGB-based":[139],"high":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}