{"id":"https://openalex.org/W2947410788","doi":"https://doi.org/10.1109/tpami.2019.2921960","title":"Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes","display_name":"Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes","publication_year":2019,"publication_date":"2019-06-10","ids":{"openalex":"https://openalex.org/W2947410788","doi":"https://doi.org/10.1109/tpami.2019.2921960","mag":"2947410788","pmid":"https://pubmed.ncbi.nlm.nih.gov/31180842"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2019.2921960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2921960","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1905.11539","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078603640","display_name":"Mandar Dixit","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mandar Dixit","raw_affiliation_strings":["Microsoft, Redmond, WA, USA","Microsoft, Redmond WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft, Redmond WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101455771","display_name":"Yunsheng Li","orcid":"https://orcid.org/0000-0002-3868-7472"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunsheng Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","Department of Electrical and Computer Engineering, University of California\u2013San Diego, La Jolla, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3868-7472","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California\u2013San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043325212","display_name":"Nuno Vasconcelos","orcid":"https://orcid.org/0000-0002-9024-4302"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nuno Vasconcelos","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","Department of Electrical and Computer Engineering, University of California\u2013San Diego, La Jolla, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-9024-4302","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California\u2013San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2276,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5475175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"42","issue":"12","first_page":"3102","last_page":"3118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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":0.9988999962806702,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7261389493942261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6781672835350037},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6727922558784485},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6110936999320984},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5899858474731445},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5804321765899658},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5331361293792725},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5294948220252991},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5236254334449768},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5011773109436035},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.48386943340301514},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41653797030448914},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4101131558418274},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30731379985809326}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7261389493942261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6781672835350037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6727922558784485},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6110936999320984},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5899858474731445},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5804321765899658},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5331361293792725},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5294948220252991},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5236254334449768},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5011773109436035},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.48386943340301514},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41653797030448914},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4101131558418274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30731379985809326},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tpami.2019.2921960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2921960","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:31180842","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31180842","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 pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:arXiv.org:1905.11539","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.11539","pdf_url":"https://arxiv.org/pdf/1905.11539","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},{"id":"mag:2947410788","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1905.11539.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"doi:10.48550/arxiv.1905.11539","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.11539","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1905.11539","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.11539","pdf_url":"https://arxiv.org/pdf/1905.11539","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2516014180","display_name":null,"funder_award_id":"IIS-1637941","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4278282407","display_name":null,"funder_award_id":"IIS-1208522","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":91,"referenced_works":["https://openalex.org/W99353449","https://openalex.org/W190008395","https://openalex.org/W300523764","https://openalex.org/W819977924","https://openalex.org/W1514535095","https://openalex.org/W1524680991","https://openalex.org/W1536680647","https://openalex.org/W1606858007","https://openalex.org/W1625255723","https://openalex.org/W1662191912","https://openalex.org/W1836465849","https://openalex.org/W1849277567","https://openalex.org/W1880262756","https://openalex.org/W1909952827","https://openalex.org/W1924160326","https://openalex.org/W1933349210","https://openalex.org/W1933900011","https://openalex.org/W1966385142","https://openalex.org/W2012592962","https://openalex.org/W2017814585","https://openalex.org/W2024197741","https://openalex.org/W2027668556","https://openalex.org/W2031342017","https://openalex.org/W2040999325","https://openalex.org/W2049633694","https://openalex.org/W2058537326","https://openalex.org/W2060994933","https://openalex.org/W2064851185","https://openalex.org/W2079153429","https://openalex.org/W2079238516","https://openalex.org/W2095242101","https://openalex.org/W2097018403","https://openalex.org/W2097117768","https://openalex.org/W2100247253","https://openalex.org/W2100771357","https://openalex.org/W2102605133","https://openalex.org/W2104657103","https://openalex.org/W2105516263","https://openalex.org/W2107034620","https://openalex.org/W2108598243","https://openalex.org/W2109255472","https://openalex.org/W2110628941","https://openalex.org/W2120750550","https://openalex.org/W2122102310","https://openalex.org/W2122528955","https://openalex.org/W2125838338","https://openalex.org/W2128532956","https://openalex.org/W2129326773","https://openalex.org/W2130046005","https://openalex.org/W2134270519","https://openalex.org/W2134670479","https://openalex.org/W2138203286","https://openalex.org/W2146022472","https://openalex.org/W2147238549","https://openalex.org/W2152161678","https://openalex.org/W2162915993","https://openalex.org/W2163605009","https://openalex.org/W2166473218","https://openalex.org/W2167057485","https://openalex.org/W2169177311","https://openalex.org/W2171061940","https://openalex.org/W2179352600","https://openalex.org/W2181967766","https://openalex.org/W2191616647","https://openalex.org/W2194775991","https://openalex.org/W2261271299","https://openalex.org/W2289772031","https://openalex.org/W2395325225","https://openalex.org/W2414492585","https://openalex.org/W2462459612","https://openalex.org/W2613718673","https://openalex.org/W2780773082","https://openalex.org/W2950179405","https://openalex.org/W2951478746","https://openalex.org/W2962835968","https://openalex.org/W2963066927","https://openalex.org/W3143107425","https://openalex.org/W6607826182","https://openalex.org/W6620707391","https://openalex.org/W6630875275","https://openalex.org/W6636494156","https://openalex.org/W6637034065","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6639619044","https://openalex.org/W6674642818","https://openalex.org/W6679431263","https://openalex.org/W6679792166","https://openalex.org/W6684116732","https://openalex.org/W6684191040","https://openalex.org/W6684482212"],"related_works":["https://openalex.org/W2556382245","https://openalex.org/W2606166366","https://openalex.org/W3146000784","https://openalex.org/W1996884424","https://openalex.org/W2990715442","https://openalex.org/W2119853387","https://openalex.org/W2973523586","https://openalex.org/W822362947","https://openalex.org/W2133663278","https://openalex.org/W2596844964","https://openalex.org/W1955371424","https://openalex.org/W3093001114","https://openalex.org/W3176823266","https://openalex.org/W2306680624","https://openalex.org/W2782865505","https://openalex.org/W2933024809","https://openalex.org/W2766768743","https://openalex.org/W2513750347","https://openalex.org/W2000021714","https://openalex.org/W2589135627"],"abstract_inverted_index":{"The":[0,51,156,190,294,307],"transfer":[1,142,258],"of":[2,14,44,53,71,78,96,119,143,152,158,176,186,192,200,208,214,221,228,270,296],"a":[3,57,212,219,271,279,334],"neural":[4],"network":[5,226],"(CNN)":[6],"trained":[7,274],"to":[8,11,31,84,147,197,241,268,301,313,318],"recognize":[9],"objects":[10],"the":[12,32,37,41,54,69,76,79,113,117,141,148,153,172,177,198,201,204,209,229,236,254,262],"task":[13],"scene":[15,28,38,263,276,281,292,321,330],"classification":[16,264,322,331],"is":[17,23,61,66,122,238,289],"considered.":[18],"A":[19,64,225],"Bag-of-Semantics":[20],"(BoS)":[21],"representation":[22],"first":[24],"induced,":[25],"by":[26,40,88,167,333],"feeding":[27],"image":[29,39],"patches":[30],"object":[33,248,298],"CNN,":[34],"and":[35,75,107,180,250,326],"representing":[36],"ensuing":[42],"bag":[43],"posterior":[45],"class":[46],"probability":[47,154],"vectors":[48],"(semantic":[49],"posteriors).":[50],"encoding":[52,199],"BoS":[55,202],"with":[56,246],"Fisher":[58,215,231],"vector":[59,213],"(FV)":[60],"then":[62,123],"studied.":[63],"link":[65],"established":[67],"between":[68],"FV":[70],"any":[72,99],"probabilistic":[73,184],"model":[74,100],"Q-function":[77],"expectation-maximization":[80],"(EM)":[81],"algorithm":[82,105],"used":[83],"estimate":[85],"its":[86],"parameters":[87],"maximum":[89],"likelihood.":[90],"This":[91,284],"enables":[92],"1)":[93,168],"immediate":[94],"derivation":[95],"FVs":[97,160,170],"for":[98,101,116,140,275,291],"which":[102],"an":[103],"EM":[104,114],"exists,":[106],"2)":[108,181],"leveraging":[109],"efficient":[110],"implementations":[111],"from":[112,132,218],"literature":[115],"computation":[118],"FVs.":[120],"It":[121],"shown":[124,312],"that":[125,162,253,286],"standard":[126],"FVs,":[127],"such":[128],"as":[129,235],"those":[130,269],"derived":[131,217],"Gaussian":[133],"or":[134],"even":[135],"Dirichlet":[136],"mixtures,":[137],"are":[138,266,310],"unsuccessful":[139],"semantic":[144,187],"posteriors,":[145],"due":[146],"highly":[149],"non-linear":[150],"nature":[151],"simplex.":[155],"analysis":[157,288],"these":[159,193],"shows":[161],"significant":[163],"benefits":[164],"can":[165],"ensue":[166],"designing":[169],"in":[171,203],"natural":[173,205],"parameter":[174,206],"space":[175,207],"multinomial":[178],"distribution,":[179],"adopting":[182],"sophisticated":[183],"models":[185],"feature":[188],"covariance.":[189],"combination":[191],"two":[194,308],"insights":[195],"leads":[196],"multinomial,":[210],"using":[211,278],"scores":[216],"mixture":[220],"factor":[222],"analyzers":[223],"(MFA).":[224],"implementation":[227],"MFA":[230],"Score":[232],"(MFA-FS),":[233],"denoted":[234],"MFAFSNet,":[237],"finally":[239],"proposed":[240],"enable":[242],"end-to-end":[243],"training.":[244],"Experiments":[245],"various":[247],"CNNs":[249],"datasets":[251],"show":[252],"approach":[255],"has":[256],"state-of-the-art":[257],"performance.":[259],"Somewhat":[260],"surprisingly,":[261],"results":[265],"superior":[267],"CNN":[272],"explicitly":[273],"classification,":[277],"large":[280,320],"dataset":[282],"(Places).":[283],"suggests":[285],"holistic":[287],"insufficient":[290],"classification.":[293],"modeling":[295],"local":[297],"semantics":[299],"appears":[300],"be":[302,314],"at":[303],"least":[304],"equally":[305],"important.":[306],"approaches":[309,332],"also":[311],"strongly":[315],"complementary,":[316],"leading":[317],"very":[319],"gains":[323],"when":[324],"combined,":[325],"outperforming":[327],"all":[328],"previous":[329],"sizable":[335],"margin.":[336]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
