{"id":"https://openalex.org/W2295546024","doi":null,"title":"Transfer Learning for Video Recognition with Scarce Training Data.","display_name":"Transfer Learning for Video Recognition with Scarce Training Data.","publication_year":2014,"publication_date":"2014-09-15","ids":{"openalex":"https://openalex.org/W2295546024","mag":"2295546024"},"language":"en","primary_location":{"id":"mag:2295546024","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1409.4127v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1409.4127v2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103041770","display_name":"Yu-Chuan Su","orcid":"https://orcid.org/0000-0002-2711-6738","observed_orcids":["https://orcid.org/0000-0002-2711-6738"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu-Chuan Su","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2711-6738","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012397831","display_name":"Tzu-Hsuan Chiu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tzu-Hsuan Chiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068739397","display_name":"Chun-Yen Yeh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chun-Yen Yeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023612991","display_name":"Hsin\u2010Fu Huang","orcid":"https://orcid.org/0000-0001-8346-1571","observed_orcids":["https://orcid.org/0000-0001-8346-1571"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsin-Fu Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043898632","display_name":"Winston H. Hsu","orcid":"https://orcid.org/0000-0002-3330-0638","observed_orcids":["https://orcid.org/0000-0002-3330-0638"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Winston H. Hsu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3330-0638","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8687000274658203,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8687000274658203,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.08730000257492065,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.030799999833106995,"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/transfer-learning","display_name":"transfer learning","score":0.8690000176429749},{"id":"https://openalex.org/keywords/video-recognition","display_name":"video recognition","score":0.9919999837875366},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.8339999914169312},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.8709999918937683},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.9869999885559082},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.703000009059906},{"id":"https://openalex.org/keywords/meta-parameters","display_name":"meta-parameters","score":0.5910000205039978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.846490740776062},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8173227310180664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7365103960037231},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6487523913383484},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5590316653251648},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.496629536151886},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47881633043289185},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4699789583683014},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.45855918526649475},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.44512060284614563},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.43784886598587036},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.42038625478744507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4097119867801666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.408206045627594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4065013825893402},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3350450396537781},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18523165583610535},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:2295546024","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1409.4127v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.740.5724","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.740.5724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1409.4127.pdf","raw_type":"text"}],"best_oa_location":{"id":"mag:2295546024","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1409.4127v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W104184427","https://openalex.org/W237546731","https://openalex.org/W1965555842","https://openalex.org/W1975325338","https://openalex.org/W1993882792","https://openalex.org/W2017814585","https://openalex.org/W2024868105","https://openalex.org/W2062903088","https://openalex.org/W2097998348","https://openalex.org/W2102605133","https://openalex.org/W2108333036","https://openalex.org/W2112796928","https://openalex.org/W2120480077","https://openalex.org/W2130325614","https://openalex.org/W2131846894","https://openalex.org/W2133257461","https://openalex.org/W2136922672","https://openalex.org/W2138857742","https://openalex.org/W2139671364","https://openalex.org/W2145607950","https://openalex.org/W2155541015","https://openalex.org/W2168231600","https://openalex.org/W2308045930","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W1489833900","https://openalex.org/W2552900565","https://openalex.org/W2742318416","https://openalex.org/W2912003633","https://openalex.org/W3023992498","https://openalex.org/W4377691903","https://openalex.org/W2741541399","https://openalex.org/W2916145674","https://openalex.org/W3147354052","https://openalex.org/W2514167171"],"abstract_inverted_index":{"Abstract\u2014Unconstrained":[0],"video":[1,26,34,58,121,146],"recognition":[2,68,157,221],"and":[3,44,65,155,230,250,262],"Deep":[4],"Convo-lution":[5],"Network":[6],"(DCN)":[7],"are":[8,40,96,138],"two":[9],"active":[10],"topics":[11],"in":[12,114,237],"computer":[13,89,239],"vision":[14,90,240],"recently.":[15],"In":[16],"this":[17],"work,":[18],"we":[19,102,171],"apply":[20],"DCNs":[21,236],"as":[22],"frame-based":[23,175],"recognizers":[24],"for":[25,120,132,270,273],"recognition.":[27,122],"Our":[28,242],"preliminary":[29],"studies,":[30],"however,":[31],"show":[32,160],"that":[33,161],"corpora":[35],"with":[36,177],"complete":[37],"ground":[38],"truth":[39],"usually":[41],"not":[42,278],"large":[43],"diverse":[45],"enough":[46],"to":[47,108,110,127,169,218,267],"learn":[48,128,173],"a":[49,85,174,268],"robust":[50],"model.":[51],"The":[52,74,123,212],"networks":[53,151],"trained":[54,142],"directly":[55],"on":[56,70,84,144,280],"the":[57,71,79,100,112,115,145,149,182,188,202,233,246,251,256,259,281],"data":[59,95,206,227],"set":[60],"suffer":[61],"from":[62,106,167],"significant":[63],"overfitting":[64],"have":[66,152],"poor":[67],"rate":[69],"test":[72],"set.":[73],"same":[75,213],"lack-of-training-sample":[76],"problem":[77,261],"limits":[78],"usage":[80],"of":[81,88,164,235,253,258],"deep":[82],"models":[83,141],"wide":[86],"range":[87],"problems":[91],"where":[92,223],"obtaining":[93],"training":[94,226],"difficult.":[97],"To":[98],"overcome":[99],"problem,":[101],"perform":[103],"transfer":[104,165],"learning":[105,166,190],"images":[107],"videos":[109],"utilize":[111],"knowledge":[113],"weakly":[116,186],"labeled":[117],"image":[118,124,168,183,205],"corpus":[119,125,184],"help":[126],"important":[129],"visual":[130,220],"patterns":[131,137],"natural":[133],"images,":[134],"while":[135],"these":[136],"ignored":[139],"by":[140,162,209],"only":[143,178,193,224],"corpus.":[147],"Therefore,":[148],"resultant":[150],"better":[153,156],"generalizability":[154],"rate.":[158],"We":[159],"means":[163],"video,":[170],"can":[172],"recognizer":[176],"4k":[179,194],"videos.":[180],"Because":[181],"is":[185,198,228],"labeled,":[187],"entire":[189],"process":[191],"requires":[192],"annotated":[195],"instances,":[196],"which":[197,276],"far":[199],"less":[200],"than":[201],"million":[203],"scale":[204],"sets":[207],"required":[208],"previous":[210],"works.":[211],"approach":[214],"may":[215],"be":[216],"applied":[217],"other":[219],"tasks":[222],"scarce":[225],"available,":[229],"it":[231],"improves":[232],"applicability":[234],"various":[238],"problems.":[241],"experiments":[243],"also":[244],"reveal":[245],"correlation":[247],"between":[248],"meta-parameters":[249],"performance":[252],"DCNs,":[254],"given":[255],"properties":[257],"target":[260],"data.":[263],"These":[264],"results":[265],"lead":[266],"heuristic":[269],"meta-parameter":[271,284],"selection":[272],"future":[274],"researches,":[275],"does":[277],"rely":[279],"time":[282],"consuming":[283],"search.":[285]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2016-06-24T00:00:00"}
