{"id":"https://openalex.org/W4385767492","doi":"https://doi.org/10.24963/ijcai.2023/79","title":"Improve Video Representation with Temporal Adversarial Augmentation","display_name":"Improve Video Representation with Temporal Adversarial Augmentation","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385767492","doi":"https://doi.org/10.24963/ijcai.2023/79"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/79","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/79","pdf_url":"https://www.ijcai.org/proceedings/2023/0079.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0079.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073311852","display_name":"Jinhao Duan","orcid":"https://orcid.org/0000-0001-7045-9376"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinhao Duan","raw_affiliation_strings":["Drexel University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Drexel University","institution_ids":["https://openalex.org/I72816309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114069812","display_name":"Quanfu Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Quanfu Fan","raw_affiliation_strings":["Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406787","display_name":"Hao Cheng","orcid":"https://orcid.org/0000-0002-3254-4796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Cheng","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069008709","display_name":"Xiaoshuang Shi","orcid":"https://orcid.org/0000-0003-4934-0850"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshuang Shi","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102775611","display_name":"Kaidi Xu","orcid":"https://orcid.org/0000-0003-4437-0671"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaidi Xu","raw_affiliation_strings":["Drexel University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Drexel University","institution_ids":["https://openalex.org/I72816309"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114069812"],"corresponding_institution_ids":["https://openalex.org/I4210089985"],"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":null,"issue":null,"first_page":"708","last_page":"716"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9955999851226807,"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":0.9955999851226807,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9815999865531921,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9800999760627747,"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/interpretability","display_name":"Interpretability","score":0.8360738754272461},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7975902557373047},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7859541177749634},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6809467673301697},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5992513298988342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.596779465675354},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5283133387565613},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4972861111164093},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.454425573348999},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.43678462505340576},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4286864697933197},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4252821207046509}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8360738754272461},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975902557373047},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7859541177749634},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6809467673301697},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5992513298988342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.596779465675354},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5283133387565613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4972861111164093},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.454425573348999},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.43678462505340576},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4286864697933197},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4252821207046509},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.24963/ijcai.2023/79","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/79","pdf_url":"https://www.ijcai.org/proceedings/2023/0079.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-130422","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-130422","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/79","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/79","pdf_url":"https://www.ijcai.org/proceedings/2023/0079.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385767492.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1945616565","https://openalex.org/W2295107390","https://openalex.org/W2507009361","https://openalex.org/W2625366777","https://openalex.org/W2765793020","https://openalex.org/W2809943552","https://openalex.org/W2883429621","https://openalex.org/W2887603965","https://openalex.org/W2887906331","https://openalex.org/W2895243423","https://openalex.org/W2962858109","https://openalex.org/W2963091558","https://openalex.org/W2963524571","https://openalex.org/W2964116600","https://openalex.org/W2970621010","https://openalex.org/W2972986629","https://openalex.org/W2990152177","https://openalex.org/W3035258980","https://openalex.org/W3035413240","https://openalex.org/W3035743198","https://openalex.org/W3094628896","https://openalex.org/W3097573595","https://openalex.org/W3117054555","https://openalex.org/W3118859451","https://openalex.org/W3126330900","https://openalex.org/W3175958943","https://openalex.org/W3177958285","https://openalex.org/W3186608063","https://openalex.org/W3187541268","https://openalex.org/W3199877015","https://openalex.org/W3213911756","https://openalex.org/W4214612132","https://openalex.org/W4288404646","https://openalex.org/W4295803779","https://openalex.org/W4312312750","https://openalex.org/W4313396410","https://openalex.org/W4323706279"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Recent":[0],"works":[1],"reveal":[2],"that":[3,33,66,155],"adversarial":[4,39],"augmentation":[5,31],"benefits":[6],"the":[7,47,76,86,95,159,181],"generalization":[8],"of":[9,50,78,88,162],"neural":[10,51,79],"networks":[11,52],"(NNs)":[12],"if":[13],"used":[14],"in":[15],"an":[16],"appropriate":[17],"manner.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22,110],"introduce":[23],"Temporal":[24,112],"Adversarial":[25,114],"Augmentation":[26],"(TA),":[27],"a":[28,60,124,176],"novel":[29],"video":[30,56,120],"technique":[32],"utilizes":[34],"temporal":[35,71,90,100],"attention.":[36],"Unlike":[37],"conventional":[38],"augmentation,":[40],"TA":[41,67],"is":[42,123,188],"specifically":[43],"designed":[44],"to":[45,55,97,104],"shift":[46],"attention":[48],"distributions":[49],"with":[53,82,134,165],"respect":[54],"clips":[57],"by":[58],"maximizing":[59],"temporal-related":[61,146],"loss":[62],"function.":[63],"We":[64,131],"demonstrate":[65,154],"will":[68],"obtain":[69],"diverse":[70],"views,":[72],"which":[73],"significantly":[74],"affect":[75],"focus":[77],"networks.":[80],"Training":[81],"these":[83,163],"examples":[84],"remedies":[85],"flaw":[87],"unbalanced":[89],"information":[91],"perception":[92],"and":[93,127,141,150],"enhances":[94],"ability":[96],"defend":[98],"against":[99],"shifts,":[101],"ultimately":[102],"leading":[103],"better":[105],"generalization.":[106],"To":[107],"leverage":[108],"TA,":[109],"propose":[111],"Video":[113],"Fine-tuning":[115],"(TAF)":[116],"framework":[117],"for":[118],"improving":[119],"representations.":[121],"TAF":[122,133,156,178],"model-agnostic,":[125],"generic,":[126],"interpretability-friendly":[128],"training":[129],"strategy.":[130],"evaluate":[132],"four":[135],"powerful":[136],"models":[137,164],"(TSM,":[138],"GST,":[139],"TAM,":[140],"TPN)":[142],"over":[143],"three":[144],"challenging":[145],"benchmarks":[147],"(Something-something":[148],"V1&V2":[149],"diving48).":[151],"Experimental":[152],"results":[153],"effectively":[157],"improves":[158,180],"test":[160],"accuracy":[161],"notable":[166],"margins":[167],"without":[168],"introducing":[169],"additional":[170],"parameters":[171],"or":[172],"computational":[173],"costs.":[174],"As":[175],"byproduct,":[177],"also":[179],"robustness":[182],"under":[183],"out-of-distribution":[184],"(OOD)":[185],"settings.":[186],"Code":[187],"available":[189],"at":[190],"https://github.com/jinhaoduan/TAF.":[191]},"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"}