{"id":"https://openalex.org/W3159350859","doi":"https://doi.org/10.1109/iscas51556.2021.9401289","title":"SDAN: Stacked Diverse Attention Network for Video Action Recognition","display_name":"SDAN: Stacked Diverse Attention Network for Video Action Recognition","publication_year":2021,"publication_date":"2021-04-27","ids":{"openalex":"https://openalex.org/W3159350859","doi":"https://doi.org/10.1109/iscas51556.2021.9401289","mag":"3159350859"},"language":"en","primary_location":{"id":"doi:10.1109/iscas51556.2021.9401289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-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/A5100685782","display_name":"Xiaoguang Zhu","orcid":"https://orcid.org/0000-0001-9554-2133"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoguang Zhu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013938193","display_name":"Siran Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siran Huang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039925111","display_name":"Wenjing Fan","orcid":"https://orcid.org/0000-0002-0571-1966"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjing Fan","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040096430","display_name":"Yuhao Cheng","orcid":"https://orcid.org/0000-0002-3174-8973"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhao Cheng","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036319627","display_name":"Huaqing Shao","orcid":"https://orcid.org/0009-0009-5761-5114"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaqing Shao","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032986088","display_name":"Peilin Liu","orcid":"https://orcid.org/0000-0002-5321-2336"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peilin Liu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100685782"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46539216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"abs 1212 402","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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":1.0,"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8333131074905396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8331922292709351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7115990519523621},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.640944242477417},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5674816370010376},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5355111956596375},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5257737636566162},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5070216655731201},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4672994911670685},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.46429169178009033},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45818769931793213},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4446064531803131},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.44163796305656433},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4263710379600525},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4126940369606018}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8333131074905396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8331922292709351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7115990519523621},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.640944242477417},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5674816370010376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5355111956596375},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5257737636566162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5070216655731201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4672994911670685},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46429169178009033},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45818769931793213},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4446064531803131},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.44163796305656433},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4263710379600525},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4126940369606018},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas51556.2021.9401289","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401289","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1944615693","https://openalex.org/W2126579184","https://openalex.org/W2156303437","https://openalex.org/W2549139847","https://openalex.org/W2553594924","https://openalex.org/W2619947201","https://openalex.org/W2809562466","https://openalex.org/W2883534172","https://openalex.org/W2962934715","https://openalex.org/W2963155035","https://openalex.org/W2963457877","https://openalex.org/W2963524571","https://openalex.org/W2963587483","https://openalex.org/W2963616706","https://openalex.org/W2963645879","https://openalex.org/W2963820951","https://openalex.org/W2964545709","https://openalex.org/W2982049169","https://openalex.org/W2990503944","https://openalex.org/W3003443105","https://openalex.org/W3021108376","https://openalex.org/W3035413240","https://openalex.org/W4237044863","https://openalex.org/W6600983433","https://openalex.org/W6682864246","https://openalex.org/W6729814214","https://openalex.org/W6766120389","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W3119773509","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2182357018","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Recently,":[0],"deep":[1],"learning":[2],"methods":[3,27],"have":[4],"proved":[5],"exceptional":[6],"performance":[7],"in":[8,75],"video":[9],"action":[10,119],"recognition.":[11],"However,":[12],"it":[13],"remains":[14],"a":[15,39,48,63,71,76],"challenging":[16],"problem":[17],"to":[18,52,69],"extract":[19],"discriminative":[20],"features":[21],"from":[22],"videos":[23],"effectively.":[24],"Most":[25],"existing":[26],"mainly":[28],"focus":[29],"on":[30,143,152],"spatial-temporal":[31,60],"information":[32],"separately.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"propose":[38],"novel":[40],"Stacked":[41],"Diverse":[42],"Attention":[43,50,65,93,110],"Network":[44],"(SDAN).":[45],"It":[46],"uses":[47],"Multi-dimensional":[49,92],"Module":[51,66,94,111],"emphasize":[53],"informative":[54],"maps":[55],"along":[56,104],"the":[57,83,87,113,123,133,136],"channel":[58],"and":[59,99,126,139,146],"dimension.":[61],"Besides,":[62],"Supervised":[64,109],"is":[67],"designed":[68],"generate":[70],"weighted":[72],"feature":[73],"map":[74],"class-supervised":[77],"way.":[78],"Compared":[79],"with":[80],"previous":[81],"methods,":[82],"proposed":[84,137],"method":[85],"has":[86],"following":[88],"advantages:":[89],"(1)":[90],"The":[91,108],"can":[95],"exploit":[96],"effective":[97],"combinations":[98],"correlation":[100],"of":[101,135],"attention":[102],"mechanisms":[103],"different":[105],"dimensions.":[106],"(2)":[107],"improves":[112],"network":[114],"capability":[115],"supervised":[116],"directly":[117],"by":[118],"labels":[120],"which":[121],"reveal":[122],"class-related":[124],"object":[125],"limb":[127],"information.":[128],"Extensive":[129],"experimental":[130],"evaluation":[131],"demonstrates":[132],"effectiveness":[134],"approach":[138],"establishes":[140],"significant":[141],"results":[142],"Kinetics400,":[144],"UCF101":[145],"HMDB51":[147],"Datasets.":[148],"Codes":[149],"are":[150],"available":[151],"https://github.com/jeff62802217/SDAN-Pytorch.":[153]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}