{"id":"https://openalex.org/W48707324","doi":"https://doi.org/10.48550/arxiv.1409.6911","title":"Do More Dropouts in Pool5 Feature Maps for Better Object Detection","display_name":"Do More Dropouts in Pool5 Feature Maps for Better Object Detection","publication_year":2014,"publication_date":"2014-09-24","ids":{"openalex":"https://openalex.org/W48707324","doi":"https://doi.org/10.48550/arxiv.1409.6911","mag":"48707324"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1409.6911","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1409.6911","pdf_url":"https://arxiv.org/pdf/1409.6911","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":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1409.6911","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066530136","display_name":"Zhiqiang Shen","orcid":"https://orcid.org/0000-0002-4560-5092","observed_orcids":["https://orcid.org/0000-0002-4560-5092"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shen, Zhiqiang","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003418019","display_name":"Xiangyang Xue","orcid":"https://orcid.org/0000-0002-4897-9209","observed_orcids":["https://orcid.org/0000-0002-4897-9209"]},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue, Xiangyang","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":11,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9937000274658203,"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":0.9937000274658203,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.00279999990016222,"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":0.0017999999690800905,"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/feature-selection","display_name":"feature selection","score":0.550000011920929},{"id":"https://openalex.org/keywords/maximum-entropy-principle","display_name":"maximum entropy principle","score":0.8970000147819519},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.7670000195503235},{"id":"https://openalex.org/keywords/feature-matching","display_name":"feature matching","score":0.34700000286102295},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.6869999766349792},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.8389999866485596},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.7710000276565552}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7346751689910889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7328818440437317},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6632999181747437},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6201944351196289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5863361954689026},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5524821877479553},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5360499024391174},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5124371647834778},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5067256093025208},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4974232017993927},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4921252131462097},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4658658504486084},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.45219382643699646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28789156675338745},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1409.6911","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1409.6911","pdf_url":"https://arxiv.org/pdf/1409.6911","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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1409.6911","doi":"https://doi.org/10.48550/arxiv.1409.6911","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1409.6911","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1409.6911","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1409.6911","pdf_url":"https://arxiv.org/pdf/1409.6911","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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W48707324.pdf","grobid_xml":"https://content.openalex.org/works/W48707324.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W613766092","https://openalex.org/W1565402342","https://openalex.org/W1849277567","https://openalex.org/W1964005749","https://openalex.org/W1972515067","https://openalex.org/W2010181071","https://openalex.org/W2030536784","https://openalex.org/W2031489346","https://openalex.org/W2045798786","https://openalex.org/W2088049833","https://openalex.org/W2089468765","https://openalex.org/W2096175520","https://openalex.org/W2100495367","https://openalex.org/W2102605133","https://openalex.org/W2104854090","https://openalex.org/W2108598243","https://openalex.org/W2110226160","https://openalex.org/W2112247540","https://openalex.org/W2112796928","https://openalex.org/W2130306094","https://openalex.org/W2137278143","https://openalex.org/W2147800946","https://openalex.org/W2156547346","https://openalex.org/W2161969291","https://openalex.org/W2166127773","https://openalex.org/W2168356304","https://openalex.org/W2179352600","https://openalex.org/W2952390042","https://openalex.org/W2963542991"],"related_works":["https://openalex.org/W7097255712","https://openalex.org/W2984032887","https://openalex.org/W1846926097","https://openalex.org/W2794463555","https://openalex.org/W2760011800","https://openalex.org/W4404207992","https://openalex.org/W4385988955","https://openalex.org/W2117287331","https://openalex.org/W2300154331","https://openalex.org/W4319299876"],"abstract_inverted_index":{"Deep":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(CNNs)":[4],"have":[5],"gained":[6],"great":[7],"success":[8],"in":[9,102,148],"image":[10,38,52,104],"classification":[11],"and":[12,39,47,142],"object":[13,131],"detection.":[14],"In":[15],"these":[16],"fields,":[17],"the":[18,40,50,66,86,99],"outputs":[19],"of":[20,23,118],"all":[21],"layers":[22],"CNNs":[24],"are":[25],"usually":[26],"considered":[27],"as":[28],"a":[29,70],"high":[30],"dimensional":[31],"feature":[32,45,82,111],"vector":[33,83,157],"extracted":[34],"from":[35,109],"an":[36,75],"input":[37,51],"correspondence":[41],"between":[42],"finer":[43],"level":[44],"vectors":[46,96],"concepts":[48,101],"that":[49],"contains":[53],"is":[54,124],"all-important.":[55],"However,":[56],"fewer":[57],"studies":[58],"focus":[59],"on":[60,133],"this":[61],"deserving":[62],"issue.":[63],"On":[64],"considering":[65],"correspondence,":[67],"we":[68],"propose":[69],"novel":[71],"approach":[72],"which":[73],"generates":[74],"edited":[76],"version":[77],"for":[78,129],"each":[79,103],"original":[80],"CNN":[81],"by":[84],"applying":[85],"maximum":[87],"entropy":[88],"principle":[89],"to":[90,98],"abandon":[91],"particular":[92],"vectors.":[93],"These":[94],"selected":[95],"correspond":[97],"unfriendly":[100],"category.":[105],"The":[106,126],"classifier":[107],"trained":[108],"merged":[110],"sets":[112],"can":[113],"significantly":[114],"improve":[115],"model":[116],"generalization":[117],"individual":[119],"categories":[120],"when":[121],"training":[122],"data":[123],"limited.":[125],"experimental":[127],"results":[128],"classification-based":[130],"detection":[132],"canonical":[134],"datasets":[135],"including":[136],"VOC":[137],"2007":[138],"(60.1%),":[139],"2010":[140],"(56.4%)":[141],"2012":[143],"(56.3%)":[144],"show":[145],"obvious":[146],"improvement":[147],"mean":[149],"average":[150],"precision":[151],"(mAP)":[152],"with":[153],"simple":[154],"linear":[155],"support":[156],"machines.":[158]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
