{"id":"https://openalex.org/W2898553394","doi":"https://doi.org/10.1109/tip.2018.2877936","title":"Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks","display_name":"Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks","publication_year":2018,"publication_date":"2018-10-24","ids":{"openalex":"https://openalex.org/W2898553394","doi":"https://doi.org/10.1109/tip.2018.2877936","mag":"2898553394","pmid":"https://pubmed.ncbi.nlm.nih.gov/30369442"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2877936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2877936","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5032642601","display_name":"Wenbing Huang","orcid":"https://orcid.org/0000-0002-2566-4159"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbing Huang","raw_affiliation_strings":["Tencent AI Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-2566-4159","affiliations":[{"raw_affiliation_string":"Tencent AI Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102354778","display_name":"Lijie Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijie Fan","raw_affiliation_strings":["Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021790939","display_name":"Mehrtash Harandi","orcid":"https://orcid.org/0000-0002-6937-6300"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mehrtash Harandi","raw_affiliation_strings":["Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017116858","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0002-7331-6132"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Ma","raw_affiliation_strings":["Tencent AI Laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041101317","display_name":"Huaping Liu","orcid":"https://orcid.org/0000-0002-4042-6044"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaping Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100431792","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-3865-8145"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Tencent AI Laboratory, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040877128","display_name":"Chuang Gan","orcid":"https://orcid.org/0000-0003-4031-5886"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuang Gan","raw_affiliation_strings":["MIT-IBM Watson AI Lab, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0003-4031-5886","affiliations":[{"raw_affiliation_string":"MIT-IBM Watson AI Lab, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7561,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.93297085,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"28","issue":"4","first_page":"1773","last_page":"1782"},"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":0.9950000047683716,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9904999732971191,"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/backpropagation","display_name":"Backpropagation","score":0.9188355207443237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8131849765777588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6478484869003296},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5311692357063293},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48667699098587036},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4675311744213104},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4524853825569153},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4220193326473236}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.9188355207443237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8131849765777588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6478484869003296},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5311692357063293},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48667699098587036},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4675311744213104},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4524853825569153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4220193326473236}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2018.2877936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2877936","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:30369442","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30369442","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W787785461","https://openalex.org/W1211924006","https://openalex.org/W1522734439","https://openalex.org/W1599220855","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1867429401","https://openalex.org/W1923404803","https://openalex.org/W1944615693","https://openalex.org/W1947481528","https://openalex.org/W1983364832","https://openalex.org/W2016053056","https://openalex.org/W2068611653","https://openalex.org/W2105101328","https://openalex.org/W2106809158","https://openalex.org/W2106996050","https://openalex.org/W2108598243","https://openalex.org/W2126579184","https://openalex.org/W2142194269","https://openalex.org/W2155893237","https://openalex.org/W2156303437","https://openalex.org/W2180092181","https://openalex.org/W2336403884","https://openalex.org/W2342662179","https://openalex.org/W2462996230","https://openalex.org/W2472293097","https://openalex.org/W2507009361","https://openalex.org/W2553594924","https://openalex.org/W2736596806","https://openalex.org/W2746726611","https://openalex.org/W2949117887","https://openalex.org/W2963149042","https://openalex.org/W2963216700","https://openalex.org/W2963218601","https://openalex.org/W2963516811","https://openalex.org/W6627953756","https://openalex.org/W6638667902","https://openalex.org/W6639126518","https://openalex.org/W6640257725","https://openalex.org/W6682864246","https://openalex.org/W6724944384","https://openalex.org/W6729814214"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W2894173309","https://openalex.org/W4387932263","https://openalex.org/W2371065793","https://openalex.org/W2157746493","https://openalex.org/W1977222966"],"abstract_inverted_index":{"In":[0,66],"this":[1],"paper,":[2],"we":[3,61],"propose":[4],"the":[5,13,34,37,45,68,80,88,114,119,122,129,132,155,158],"novel":[6,104],"principal":[7],"backpropagation":[8,14,38,120],"networks":[9,21],"(PBNets)":[10],"to":[11,78,167],"revisit":[12],"algorithms":[15],"commonly":[16],"used":[17],"in":[18,50],"training":[19],"two-stream":[20],"for":[22,36,41,83],"video":[23,42,145],"action":[24,146],"recognition.":[25],"We":[26,110],"content":[27],"that":[28,99,112],"existing":[29],"approaches":[30],"always":[31],"take":[32],"all":[33],"frames/snippets":[35],"not":[39],"optimal":[40],"recognition":[43,147],"since":[44],"desired":[46],"actions":[47],"only":[48,91],"occur":[49],"a":[51,55,63,72,93],"short":[52],"period":[53],"within":[54],"video.":[56],"To":[57],"remedy":[58],"these":[59],"drawbacks,":[60],"design":[62],"watch-and-choose":[64],"mechanism.":[65],"particular,":[67],"watching":[69],"stage":[70],"exploits":[71],"dense":[73],"snippet-wise":[74],"temporal":[75],"pooling":[76],"strategy":[77],"discover":[79],"global":[81],"characteristic":[82],"each":[84],"input":[85],"video,":[86],"while":[87],"choosing":[89],"phase":[90],"backpropagates":[92],"small":[94],"number":[95],"of":[96,127,131,157],"representative":[97],"snippets":[98,134],"are":[100,140],"selected":[101,123],"with":[102,113],"two":[103,143],"strategies,":[105,117],"i.e.,":[106],"Max-rule":[107],"and":[108,150,165],"KL-rule.":[109],"prove":[111],"proposed":[115,138],"selection":[116],"performing":[118],"on":[121,142],"subset":[124],"is":[125],"capable":[126],"decreasing":[128],"loss":[130],"whole":[133],"as":[135],"well.":[136],"The":[137],"PBNets":[139],"evaluated":[141],"standard":[144],"benchmarks":[148],"UCF101":[149],"HMDB51,":[151],"where":[152],"it":[153],"surpasses":[154],"state":[156],"arts":[159],"consistently,":[160],"but":[161],"requiring":[162],"less":[163],"memory":[164],"computation":[166],"achieve":[168],"high":[169],"performance.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
