{"id":"https://openalex.org/W4304080619","doi":"https://doi.org/10.1145/3503161.3548326","title":"DTR: An Information Bottleneck Based Regularization Framework for Video Action Recognition","display_name":"DTR: An Information Bottleneck Based Regularization Framework for Video Action Recognition","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304080619","doi":"https://doi.org/10.1145/3503161.3548326"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3548326","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548326","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","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/A5100842311","display_name":"Jiawei Fan","orcid":"https://orcid.org/0009-0008-7959-2606"},"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":"Jiawei Fan","raw_affiliation_strings":["MEGVII Technology &amp; Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology &amp; Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026758178","display_name":"Yu Zhao","orcid":"https://orcid.org/0000-0001-7531-0730"},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhao","raw_affiliation_strings":["MEGVII Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104093622","display_name":"Xie Yu","orcid":null},"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":"Xie Yu","raw_affiliation_strings":["MEGVII Technology &amp; Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology &amp; Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100637358","display_name":"Lihua Ma","orcid":"https://orcid.org/0000-0003-2215-0459"},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihua Ma","raw_affiliation_strings":["MEGVII Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100682140","display_name":"Junqi Liu","orcid":"https://orcid.org/0000-0003-1654-6174"},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junqi Liu","raw_affiliation_strings":["MEGVII Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075646933","display_name":"Fangqiu Yi","orcid":null},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangqiu Yi","raw_affiliation_strings":["MEGVII Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726805"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101982776","display_name":"Boxun Li","orcid":"https://orcid.org/0000-0002-6370-1723"},"institutions":[{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boxun Li","raw_affiliation_strings":["MEGVII Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MEGVII Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.177,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.508483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3877","last_page":"3885"},"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.996399998664856,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9797999858856201,"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/information-bottleneck-method","display_name":"Information bottleneck method","score":0.9506509304046631},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7654274702072144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7500406503677368},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7344293594360352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5704855918884277},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5275294780731201},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49220457673072815},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4545894265174866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4294244647026062},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.418878436088562},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.1321345865726471}],"concepts":[{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.9506509304046631},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7654274702072144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500406503677368},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7344293594360352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5704855918884277},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5275294780731201},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49220457673072815},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4545894265174866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4294244647026062},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.418878436088562},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.1321345865726471},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"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/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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3548326","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3548326","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":27,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2096516049","https://openalex.org/W2126579184","https://openalex.org/W2331143823","https://openalex.org/W2507009361","https://openalex.org/W2625366777","https://openalex.org/W2895243423","https://openalex.org/W2921861056","https://openalex.org/W2963177640","https://openalex.org/W2963524571","https://openalex.org/W2978607478","https://openalex.org/W2981385151","https://openalex.org/W2989613460","https://openalex.org/W2990152177","https://openalex.org/W2992308087","https://openalex.org/W3026092005","https://openalex.org/W3035180180","https://openalex.org/W3035303837","https://openalex.org/W3035523846","https://openalex.org/W3094193403","https://openalex.org/W3113713694","https://openalex.org/W3119547333","https://openalex.org/W3175530672","https://openalex.org/W3181598125","https://openalex.org/W3217059328","https://openalex.org/W6600281463","https://openalex.org/W6777179611"],"related_works":["https://openalex.org/W4401325445","https://openalex.org/W2622284819","https://openalex.org/W1504394672","https://openalex.org/W3089381707","https://openalex.org/W4285254085","https://openalex.org/W3034190530","https://openalex.org/W2741297526","https://openalex.org/W4295728955","https://openalex.org/W3129794609","https://openalex.org/W2949033103"],"abstract_inverted_index":{"An":[0],"optimal":[1,114],"representation":[2,30,68],"should":[3],"contain":[4],"the":[5,74,77,99],"maximum":[6],"task-relevant":[7,109],"information":[8,110],"and":[9,65,94,140,148],"minimum":[10],"task-irrelevant":[11,87],"information,":[12,88],"as":[13,135,137],"revealed":[14],"from":[15],"Information":[16,59],"Bottleneck":[17,60],"Principle.":[18],"In":[19,42],"video":[20,67],"action":[21],"recognition,":[22],"CNN":[23],"based":[24,50,57],"approaches":[25,37],"have":[26],"obtained":[27],"better":[28],"spatio-temporal":[29],"by":[31,111],"modeling":[32,134],"temporal":[33,133],"context.":[34],"However,":[35],"these":[36],"still":[38],"suffer":[39],"low":[40],"generalization.":[41],"this":[43],"paper,":[44],"we":[45,80,102],"propose":[46],"a":[47],"moderate":[48],"optimization":[49],"approach":[51],"called":[52],"Dual-view":[53,82],"Temporal":[54,104],"Regularization":[55,83,105],"(DTR)":[56],"on":[58,145,160],"Principle":[61],"for":[62],"an":[63,113],"effective":[64,154],"generalized":[66],"without":[69],"sacrificing":[70],"any":[71],"efficiency":[72],"of":[73],"model.":[75],"On":[76,98],"one":[78],"hand,":[79,101],"design":[81,103],"(DR)":[84],"to":[85,107,132],"constrain":[86],"which":[89,118,167],"can":[90],"effectively":[91],"compress":[92],"background":[93],"irrelevant":[95],"motion":[96,122],"information.":[97,123],"other":[100],"(TR)":[106],"maintain":[108],"finding":[112],"difference":[115],"between":[116],"frames,":[117],"benefits":[119],"extracting":[120],"sufficient":[121],"The":[124],"experimental":[125],"results":[126],"demonstrate:":[127],"(1)":[128],"DTR":[129,152,168],"is":[130,153],"orthogonal":[131],"well":[136],"data":[138],"augmentation,":[139],"it":[141],"achieves":[142],"general":[143],"improvement":[144],"both":[146],"model-based":[147],"data-based":[149],"approaches;":[150],"(2)":[151],"among":[155],"7":[156],"different":[157],"datasets,":[158],"especially":[159],"motion-centric":[161],"datasets":[162],"i.e.":[163],"SSv1/":[164],"SSv2,":[165],"in":[166,173],"gets":[169],"6%/3.8%":[170],"absolute":[171],"gains":[172],"top-1":[174],"accuracy.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
