{"id":"https://openalex.org/W2517185326","doi":"https://doi.org/10.1109/icme.2016.7552910","title":"Learning deep classifiers with deep features","display_name":"Learning deep classifiers with deep features","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2517185326","doi":"https://doi.org/10.1109/icme.2016.7552910","mag":"2517185326"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2016.7552910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","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/A5007285444","display_name":"Jie Lei","orcid":"https://orcid.org/0000-0003-0851-6565"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Lei","raw_affiliation_strings":["Zhejiang University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103079800","display_name":"Xinhui Song","orcid":"https://orcid.org/0000-0002-0082-9244"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhui Song","raw_affiliation_strings":["Zhejiang University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100318678","display_name":"Li Sun","orcid":"https://orcid.org/0000-0002-2957-4214"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Sun","raw_affiliation_strings":["Zhejiang University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026532752","display_name":"Mingli Song","orcid":"https://orcid.org/0000-0003-2621-6048"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingli Song","raw_affiliation_strings":["Zhejiang University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114488748","display_name":"Na Li","orcid":"https://orcid.org/0000-0003-4538-1663"},"institutions":[{"id":"https://openalex.org/I105126617","display_name":"Zhejiang International Studies University","ror":"https://ror.org/01vwvvq12","country_code":"CN","type":"education","lineage":["https://openalex.org/I105126617"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Li","raw_affiliation_strings":["Zhejiang International Studies University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang International Studies University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I105126617"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100373367","display_name":"Chun Chen","orcid":"https://orcid.org/0000-0002-6198-7481"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun Chen","raw_affiliation_strings":["Zhejiang University, Hangzhou, P.R. China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, P.R. China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5007285444"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.167,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.56839579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9983999729156494,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.998199999332428,"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/computer-science","display_name":"Computer science","score":0.8150938749313354},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.7514317631721497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7394245862960815},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6882904171943665},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.6584992408752441},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6111401915550232},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6063741445541382},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5598567128181458},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5365272760391235},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5153443217277527},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5144439339637756},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45600757002830505},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.45217233896255493},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.43357670307159424},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4246911108493805},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41331204771995544},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39474403858184814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8150938749313354},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7514317631721497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7394245862960815},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6882904171943665},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.6584992408752441},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6111401915550232},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6063741445541382},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5598567128181458},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5365272760391235},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5153443217277527},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5144439339637756},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45600757002830505},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.45217233896255493},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.43357670307159424},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4246911108493805},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41331204771995544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39474403858184814},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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":1,"locations":[{"id":"doi:10.1109/icme.2016.7552910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1948751323","https://openalex.org/W2024922353","https://openalex.org/W2030377904","https://openalex.org/W2091759811","https://openalex.org/W2110765924","https://openalex.org/W2152411181","https://openalex.org/W2163605009","https://openalex.org/W2183697171","https://openalex.org/W2402669826","https://openalex.org/W4285719527","https://openalex.org/W6600609147","https://openalex.org/W6639204139","https://openalex.org/W6658103998","https://openalex.org/W6684191040","https://openalex.org/W6686159848"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W4256502920","https://openalex.org/W103652678","https://openalex.org/W4226090359","https://openalex.org/W2999756192","https://openalex.org/W2059697060","https://openalex.org/W4382701072","https://openalex.org/W2491314273"],"abstract_inverted_index":{"Visual":[0],"separability":[1],"between":[2],"different":[3,18,85],"objects":[4,25,102],"in":[5,26,42,54,110,116,132,167,178],"various":[6],"image":[7],"classification":[8,55,176],"tasks":[9,180],"is":[10],"highly":[11],"uneven.":[12],"As":[13],"a":[14,95,129,146],"consequence,":[15],"humans":[16],"need":[17],"levels":[19,90],"of":[20,60,91],"detailed":[21],"descriptions":[22],"to":[23,68,75,100,154],"separate":[24],"multi-granularity":[27,78],"similarities.":[28],"Meanwhile,":[29],"deep":[30,52,96,114,122],"networks,":[31],"such":[32],"as":[33,64,88,124],"convolutional":[34],"neural":[35],"networks":[36,53,123],"(C-NNs)":[37],"have":[38],"demonstrated":[39],"great":[40,104],"ability":[41],"multilevel":[43],"representations":[44],"for":[45,127,143],"an":[46],"object.":[47],"Unfortunately,":[48],"existing":[49],"methods":[50],"with":[51,107],"typically":[56],"use":[57],"the":[58,61,65,77,125,133,136,156,161,175],"output":[59],"last":[62],"layer":[63],"only":[66],"feature":[67],"train":[69],"flat":[70,182],"N-way":[71],"classifiers,":[72],"which":[73],"fail":[74],"fit":[76],"character.":[79],"In":[80],"this":[81],"paper,":[82],"by":[83,159],"regarding":[84],"CNN":[86],"layers":[87,118,140],"multiple":[89,117,150],"abstraction,":[92],"we":[93],"propose":[94],"decision":[97],"tree":[98],"(DDT)":[99],"distinguish":[101],"sharing":[103],"appearance":[105],"similarities":[106],"utilizing":[108],"features":[109,115,137],"all":[111],"layers.":[112],"First,":[113],"are":[119,141,152],"extracted":[120],"from":[121,138],"input":[126],"building":[128],"DDT.":[130],"Next,":[131],"training":[134],"phase,":[135],"earlier":[139],"selected":[142],"splitting":[144],"on":[145],"deeper":[147],"node.":[148],"Finally,":[149],"DDTs":[151],"bagged":[153],"make":[155],"final":[157],"prediction":[158],"taking":[160],"majority":[162],"vote.":[163],"The":[164],"experimental":[165],"results":[166],"two":[168],"datasets":[169],"show":[170],"DDT":[171],"can":[172],"greatly":[173],"improve":[174],"accuracy":[177],"multi-grained":[179],"than":[181],"models.":[183]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
