{"id":"https://openalex.org/W3136965813","doi":"https://doi.org/10.1109/tmm.2021.3066775","title":"Spatiotemporal Saliency Representation Learning for Video Action Recognition","display_name":"Spatiotemporal Saliency Representation Learning for Video Action Recognition","publication_year":2021,"publication_date":"2021-03-18","ids":{"openalex":"https://openalex.org/W3136965813","doi":"https://doi.org/10.1109/tmm.2021.3066775","mag":"3136965813"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2021.3066775","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2021.3066775","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5030098733","display_name":"Yongqiang Kong","orcid":"https://orcid.org/0000-0001-6793-2492"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongqiang Kong","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6793-2492","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398953","display_name":"Yunhong Wang","orcid":"https://orcid.org/0000-0001-8001-2703"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhong Wang","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8001-2703","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012121355","display_name":"Annan Li","orcid":"https://orcid.org/0000-0003-3497-5052"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Annan Li","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3497-5052","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030098733"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":2.0389,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.88558548,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"24","issue":null,"first_page":"1515","last_page":"1528"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9959999918937683,"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":0.8888664245605469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7374733090400696},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6127836108207703},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5914183855056763},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5668588876724243},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5106108784675598},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4818804860115051},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4620475172996521},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45106106996536255},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.426461786031723},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4229733347892761},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.41961869597435},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4180053770542145},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38635390996932983},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2971944212913513},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.11002081632614136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8888664245605469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7374733090400696},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6127836108207703},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5914183855056763},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5668588876724243},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5106108784675598},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4818804860115051},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4620475172996521},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45106106996536255},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.426461786031723},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4229733347892761},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.41961869597435},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4180053770542145},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38635390996932983},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2971944212913513},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.11002081632614136},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2021.3066775","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2021.3066775","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7665144123","display_name":null,"funder_award_id":"U20B2069","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":83,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1522734439","https://openalex.org/W1677182931","https://openalex.org/W1850164289","https://openalex.org/W1867429401","https://openalex.org/W1903029394","https://openalex.org/W1983364832","https://openalex.org/W2016053056","https://openalex.org/W2055611119","https://openalex.org/W2058256495","https://openalex.org/W2065778157","https://openalex.org/W2071555787","https://openalex.org/W2083174318","https://openalex.org/W2105101328","https://openalex.org/W2110019070","https://openalex.org/W2124017786","https://openalex.org/W2126579184","https://openalex.org/W2146634731","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2235034809","https://openalex.org/W2342662179","https://openalex.org/W2462996230","https://openalex.org/W2470139095","https://openalex.org/W2501148868","https://openalex.org/W2507009361","https://openalex.org/W2519750370","https://openalex.org/W2522485430","https://openalex.org/W2559833261","https://openalex.org/W2583194072","https://openalex.org/W2585592883","https://openalex.org/W2612135493","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2751445731","https://openalex.org/W2757028014","https://openalex.org/W2765103010","https://openalex.org/W2770804203","https://openalex.org/W2798472916","https://openalex.org/W2799108379","https://openalex.org/W2800632947","https://openalex.org/W2894404285","https://openalex.org/W2895340898","https://openalex.org/W2904232353","https://openalex.org/W2913950831","https://openalex.org/W2921653116","https://openalex.org/W2938260698","https://openalex.org/W2947084868","https://openalex.org/W2948510860","https://openalex.org/W2955060956","https://openalex.org/W2955084925","https://openalex.org/W2957408986","https://openalex.org/W2962680827","https://openalex.org/W2962711930","https://openalex.org/W2963091558","https://openalex.org/W2963155035","https://openalex.org/W2963246338","https://openalex.org/W2963315828","https://openalex.org/W2963422510","https://openalex.org/W2963529609","https://openalex.org/W2963868681","https://openalex.org/W2965638232","https://openalex.org/W2968553732","https://openalex.org/W2969626490","https://openalex.org/W2969741484","https://openalex.org/W2973058802","https://openalex.org/W2981342784","https://openalex.org/W2986056979","https://openalex.org/W2990503944","https://openalex.org/W2997217064","https://openalex.org/W2997316506","https://openalex.org/W2999458807","https://openalex.org/W2999794487","https://openalex.org/W3049318984","https://openalex.org/W3088826108","https://openalex.org/W3092499653","https://openalex.org/W3106587394","https://openalex.org/W6600983433","https://openalex.org/W6628877408","https://openalex.org/W6682864246","https://openalex.org/W6729814214","https://openalex.org/W6770390784","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5,37],"achieved":[6],"great":[7,54],"success":[8],"in":[9,18],"human":[10,90],"action":[11,63,121,151],"recognition,":[12],"however":[13],"they":[14],"are":[15],"still":[16],"limited":[17],"understanding":[19],"complex":[20],"and":[21,31,91,156],"noisy":[22],"videos":[23],"owing":[24],"to":[25,40,96],"the":[26,46,69,98,132,150,159],"difficulties":[27],"of":[28,48,53,62,71,145,161],"exploiting":[29],"appearance":[30],"motion":[32],"information.":[33],"Most":[34],"existing":[35],"works":[36],"been":[38],"devoted":[39],"designing":[41],"CNN":[42],"architectures,":[43],"which":[44,85,113],"overlook":[45],"quality":[47,70],"network":[49,72,109],"inputs":[50,118],"that":[51,131],"is":[52,94,111],"importance.":[55],"This":[56],"paper":[57],"provides":[58],"an":[59],"alternative":[60],"solution":[61],"recognition":[64,152],"improvement":[65],"by":[66],"focusing":[67],"on":[68,115,126],"inputs.":[73],"A":[74],"multi-task":[75],"video":[76,100],"salient":[77],"object":[78],"detection":[79],"approach":[80],"with":[81],"object-of-interest":[82],"segmentation":[83],"scheme,":[84],"takes":[86],"into":[87],"account":[88],"both":[89],"action-relevant":[92],"cues,":[93],"proposed":[95,133],"immunize":[97],"input":[99],"from":[101,154],"background":[102],"clutter.":[103],"Further,":[104],"a":[105],"simple":[106],"spatiotemporal":[107],"residual":[108],"architecture":[110],"presented,":[112],"operates":[114],"multiple":[116],"high-quality":[117],"for":[119],"long-term":[120],"representation":[122],"learning.":[123],"Empirical":[124],"evaluations":[125],"various":[127],"challenging":[128],"datasets":[129],"demonstrate":[130],"framework":[134],"can":[135,147],"perform":[136],"competitively":[137],"against":[138],"state-of-the-art.":[139],"Besides":[140],"better":[141],"performance,":[142],"learning":[143],"representations":[144],"saliency":[146],"help":[148],"prevent":[149],"model":[153],"overfitting":[155],"speed":[157],"up":[158],"convergence":[160],"training.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
