{"id":"https://openalex.org/W1968405646","doi":"https://doi.org/10.1109/tpami.2015.2389830","title":"Regionlets for Generic Object Detection","display_name":"Regionlets for Generic Object Detection","publication_year":2015,"publication_date":"2015-01-09","ids":{"openalex":"https://openalex.org/W1968405646","doi":"https://doi.org/10.1109/tpami.2015.2389830","mag":"1968405646","pmid":"https://pubmed.ncbi.nlm.nih.gov/26353185"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2015.2389830","doi":"https://doi.org/10.1109/tpami.2015.2389830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2015.2389830","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,"listed_in":["ccf-a","cwts-core","dongbi-a","doyens","jufo-3","medline","norway-2","poland-200","russia-white-list-1"],"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"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"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/A5100357613","display_name":"Xiaoyu Wang","orcid":"https://orcid.org/0000-0002-6431-8822","observed_orcids":["https://orcid.org/0000-0002-6431-8822","https://orcid.org/0000-0003-3270-841X"]},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyu Wang","raw_affiliation_strings":["Department of Media Analytics, NEC Laboratories America, Cupertino, CA, 95014"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Media Analytics, NEC Laboratories America, Cupertino, CA, 95014","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019934281","display_name":"Ming Yang","orcid":"https://orcid.org/0000-0003-1691-6817","observed_orcids":["https://orcid.org/0000-0003-1691-6817","https://orcid.org/0000-0002-5544-9126"]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Yang","raw_affiliation_strings":["AI Research, Facebook Inc., Menlo Park, CA, 94025"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AI Research, Facebook Inc., Menlo Park, CA, 94025","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103861122","display_name":"Shenghuo Zhu B","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenghuo Zhu","raw_affiliation_strings":["Alibaba Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112103372","display_name":"Yuanqing Lin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanqing Lin","raw_affiliation_strings":["Department of Media Analytics, NEC Laboratories America, Cupertino, CA, 95014"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Media Analytics, NEC Laboratories America, Cupertino, CA, 95014","institution_ids":["https://openalex.org/I4210107353"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":12.2538,"has_fulltext":false,"cited_by_count":107,"citation_normalized_percentile":{"value":0.98991176,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"37","issue":"10","first_page":"2071","last_page":"2084"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5189999938011169,"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":0.5189999938011169,"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":0.45829999446868896,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.009399999864399433,"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/generic-object-detection","display_name":"generic object detection","score":0.9129999876022339},{"id":"https://openalex.org/keywords/feature-aggregation","display_name":"feature aggregation","score":0.6029999852180481},{"id":"https://openalex.org/keywords/viewpoint-variation","display_name":"viewpoint variation","score":0.5979999899864197},{"id":"https://openalex.org/keywords/deformation-tolerance","display_name":"deformation tolerance","score":0.7369999885559082},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.7929999828338623}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7551950812339783},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7462800741195679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7434161901473999},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7109118103981018},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6932379007339478},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.6259785890579224},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6177403330802917},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6034088730812073},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5299832820892334},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.52509605884552},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5055527687072754},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4991910457611084},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.47184303402900696},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17581894993782043},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tpami.2015.2389830","doi":"https://doi.org/10.1109/tpami.2015.2389830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2015.2389830","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,"listed_in":["ccf-a","cwts-core","dongbi-a","doyens","jufo-3","medline","norway-2","poland-200","russia-white-list-1"],"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:26353185","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26353185","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,"listed_in":[],"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":"Journal Article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.708.3167","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.708.3167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.xiaoyumu.com/s/PDF/Regionlets.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.708.996","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.708.996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.xiaoyumu.com/s/PDF/Regionlets-pami.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1487583988","https://openalex.org/W1528789833","https://openalex.org/W1545641654","https://openalex.org/W1864464506","https://openalex.org/W1950935069","https://openalex.org/W1964005749","https://openalex.org/W1976931317","https://openalex.org/W1989192414","https://openalex.org/W2010181071","https://openalex.org/W2017691720","https://openalex.org/W2023014843","https://openalex.org/W2024665880","https://openalex.org/W2032210760","https://openalex.org/W2036989445","https://openalex.org/W2037511607","https://openalex.org/W2044313232","https://openalex.org/W2066624635","https://openalex.org/W2068730032","https://openalex.org/W2091068168","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2109258005","https://openalex.org/W2112796928","https://openalex.org/W2120419212","https://openalex.org/W2128715914","https://openalex.org/W2129052208","https://openalex.org/W2129305389","https://openalex.org/W2130350952","https://openalex.org/W2136724559","https://openalex.org/W2141357020","https://openalex.org/W2142037471","https://openalex.org/W2143729633","https://openalex.org/W2147511372","https://openalex.org/W2149077040","https://openalex.org/W2151103935","https://openalex.org/W2155541015","https://openalex.org/W2161198271","https://openalex.org/W2161969291","https://openalex.org/W2162915993","https://openalex.org/W2163605009","https://openalex.org/W2168231600","https://openalex.org/W2534457893","https://openalex.org/W2548197316","https://openalex.org/W2963542991","https://openalex.org/W3097096317","https://openalex.org/W4244104295","https://openalex.org/W4244952642"],"related_works":["https://openalex.org/W2611642521","https://openalex.org/W2993683660","https://openalex.org/W2963150001","https://openalex.org/W2166127773","https://openalex.org/W2065850317","https://openalex.org/W2103897297","https://openalex.org/W2129818882","https://openalex.org/W2517052777","https://openalex.org/W1983128835","https://openalex.org/W1941385496"],"abstract_inverted_index":{"Generic":[0],"object":[1,19,30,45,144,198],"detection":[2,87,163,176],"is":[3,77],"confronted":[4],"by":[5,13,216],"dealing":[6],"with":[7,21,47,105,166,212],"different":[8],"degrees":[9],"of":[10,56,63,67,70,180],"variations,":[11],"caused":[12],"viewpoints":[14],"or":[15],"deformations":[16],"in":[17,37,102,228],"distinct":[18],"classes,":[20],"tractable":[22],"computations.":[23],"This":[24],"demands":[25],"for":[26,142,196],"descriptive":[27,111],"and":[28,95,189],"flexible":[29,134],"representations":[31],"which":[32,52,64],"can":[33],"be":[34,110,133],"efficiently":[35,208],"evaluated":[36],"many":[38],"locations.":[39],"We":[40,200],"propose":[41],"to":[42,85,109,112,132,135,207],"model":[43],"an":[44,90],"class":[46,145],"a":[48,68,78,86,124,149,167,175],"cascaded":[49],"boosting":[50,150],"classifier":[51],"integrates":[53],"various":[54],"types":[55],"features":[57,120,211],"from":[58],"competing":[59],"local":[60],"regions,":[61],"each":[62,143],"may":[65],"consist":[66],"group":[69,129],"subregions,":[71],"named":[72],"as":[73,131],"regionlets.":[74],"A":[75],"regionlet":[76,154,210,222],"base":[79],"feature":[80,126],"extraction":[81],"region":[82],"defined":[83],"proportionally":[84],"window":[88],"at":[89],"arbitrary":[91],"resolution":[92],"(i.e.,":[93],"size":[94],"aspect":[96],"ratio).":[97],"These":[98],"regionlets":[99,141],"are":[100,121,146],"organized":[101],"small":[103],"groups":[104],"stable":[106],"relative":[107],"positions":[108],"delineate":[113],"fine-grained":[114],"spatial":[115],"layouts":[116],"inside":[117],"objects.":[118],"Their":[119],"aggregated":[122],"into":[123],"one-dimensional":[125],"within":[127],"one":[128],"so":[130],"tolerate":[136],"deformations.":[137],"The":[138],"most":[139],"discriminative":[140],"selected":[147],"through":[148],"learning":[151],"procedure.":[152],"Our":[153,221],"approach":[155],"achieves":[156,174],"very":[157],"competitive":[158],"performance":[159],"on":[160,183,192],"popular":[161],"multi-class":[162],"benchmark":[164],"datasets":[165],"single":[168],"method,":[169],"without":[170],"any":[171],"context.":[172],"It":[173],"mean":[177],"average":[178],"precision":[179],"41.7":[181],"percent":[182,191],"the":[184,193,213,229],"PASCAL":[185],"VOC":[186,194],"2007":[187],"dataset,":[188],"39.7":[190],"2010":[195],"20":[197],"categories.":[199],"further":[201],"develop":[202],"support":[203],"pixel":[204],"integral":[205],"images":[206],"augment":[209],"responses":[214],"learned":[215],"deep":[217],"convolutional":[218],"neural":[219],"networks.":[220],"based":[223],"method":[224],"won":[225],"second":[226],"place":[227],"ImageNet":[230],"Large":[231],"Scale":[232],"Visual":[233],"Object":[234],"Recognition":[235],"Challenge":[236],"(ILSVRC":[237],"2013).":[238]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":17},{"year":2017,"cited_by_count":18},{"year":2016,"cited_by_count":11},{"year":2015,"cited_by_count":5}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
