{"id":"https://openalex.org/W1903820182","doi":"https://doi.org/10.1109/cvpr.2015.7298597","title":"Expanding object detector's Horizon: Incremental learning framework for object detection in videos","display_name":"Expanding object detector's Horizon: Incremental learning framework for object detection in videos","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1903820182","doi":"https://doi.org/10.1109/cvpr.2015.7298597","mag":"1903820182"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5101831247","display_name":"Alina Kuznetsova","orcid":"https://orcid.org/0000-0002-9648-5457"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]},{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE","US"],"is_corresponding":true,"raw_author_name":"Alina Kuznetsova","raw_affiliation_strings":["Disney Research Pittsburgh","[Leibniz University Hannover, Germany]"],"affiliations":[{"raw_affiliation_string":"Disney Research Pittsburgh","institution_ids":["https://openalex.org/I4210142140"]},{"raw_affiliation_string":"[Leibniz University Hannover, Germany]","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070302452","display_name":"Sung Ju Hwang","orcid":"https://orcid.org/0000-0002-9675-2324"},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung Ju Hwang","raw_affiliation_strings":["UNIST","UNIST (South Korea)"],"affiliations":[{"raw_affiliation_string":"UNIST","institution_ids":["https://openalex.org/I48566637"]},{"raw_affiliation_string":"UNIST (South Korea)","institution_ids":["https://openalex.org/I48566637"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040412734","display_name":"Bodo Rosenhahn","orcid":"https://orcid.org/0000-0003-3861-1424"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bodo Rosenhahn","raw_affiliation_strings":["Leibniz Universitat Hannover, Hannover, Niedersachsen, DE","[Leibniz University Hannover, Germany]"],"affiliations":[{"raw_affiliation_string":"Leibniz Universitat Hannover, Hannover, Niedersachsen, DE","institution_ids":[]},{"raw_affiliation_string":"[Leibniz University Hannover, Germany]","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053011888","display_name":"Leonid Sigal","orcid":"https://orcid.org/0000-0002-3942-2804"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leonid Sigal","raw_affiliation_strings":["Disney Research Pittsburgh","Disney Research, Pittsburgh, USA"],"affiliations":[{"raw_affiliation_string":"Disney Research Pittsburgh","institution_ids":["https://openalex.org/I4210142140"]},{"raw_affiliation_string":"Disney Research, Pittsburgh, USA","institution_ids":["https://openalex.org/I4210142140"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101831247"],"corresponding_institution_ids":["https://openalex.org/I114112103","https://openalex.org/I4210142140"],"apc_list":null,"apc_paid":null,"fwci":11.1655,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.98311579,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9973999857902527,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9916999936103821,"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":0.8155781626701355},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7563380002975464},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7150640487670898},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6730197668075562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575667858123779},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6427730321884155},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.620073139667511},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6034259796142578},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5667989253997803},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5640604496002197},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5040375590324402},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45093971490859985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33682000637054443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08861550688743591}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8155781626701355},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7563380002975464},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7150640487670898},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6730197668075562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575667858123779},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6427730321884155},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.620073139667511},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6034259796142578},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5667989253997803},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5640604496002197},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5040375590324402},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45093971490859985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33682000637054443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08861550688743591},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarworks.unist.ac.kr:201301/46659","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/7298597","pdf_url":null,"source":{"id":"https://openalex.org/S4306401118","display_name":"Scholarworks@UNIST (Ulsan National Institute of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I48566637","host_organization_name":"Ulsan National Institute of Science and Technology","host_organization_lineage":["https://openalex.org/I48566637"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"CONFERENCE"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W21006490","https://openalex.org/W43954826","https://openalex.org/W1512387364","https://openalex.org/W1535804263","https://openalex.org/W1589326101","https://openalex.org/W1722318740","https://openalex.org/W1822439997","https://openalex.org/W1852255964","https://openalex.org/W1964763677","https://openalex.org/W1973054923","https://openalex.org/W1988348003","https://openalex.org/W1992605376","https://openalex.org/W1994129692","https://openalex.org/W2005295545","https://openalex.org/W2015563892","https://openalex.org/W2031342017","https://openalex.org/W2031489346","https://openalex.org/W2088049833","https://openalex.org/W2090923791","https://openalex.org/W2104068492","https://openalex.org/W2108598243","https://openalex.org/W2114623790","https://openalex.org/W2116496396","https://openalex.org/W2118877769","https://openalex.org/W2120501001","https://openalex.org/W2121010533","https://openalex.org/W2128053425","https://openalex.org/W2132984949","https://openalex.org/W2133434696","https://openalex.org/W2143629212","https://openalex.org/W2146753383","https://openalex.org/W2149466042","https://openalex.org/W2155144632","https://openalex.org/W2155893237","https://openalex.org/W2156689847","https://openalex.org/W2165605600","https://openalex.org/W2168356304","https://openalex.org/W2168930216","https://openalex.org/W3040777582","https://openalex.org/W4239072543","https://openalex.org/W4294351762","https://openalex.org/W6600827882","https://openalex.org/W6601825177","https://openalex.org/W6630579473","https://openalex.org/W6635629998","https://openalex.org/W6637542466","https://openalex.org/W6638635240","https://openalex.org/W6639105354","https://openalex.org/W6648765433","https://openalex.org/W6676297131","https://openalex.org/W6677548441","https://openalex.org/W6677850982","https://openalex.org/W6679390333","https://openalex.org/W6679805309","https://openalex.org/W6681637710","https://openalex.org/W6681897824","https://openalex.org/W6684982718","https://openalex.org/W6780413204"],"related_works":["https://openalex.org/W2095705906","https://openalex.org/W2975200075","https://openalex.org/W2922421953","https://openalex.org/W1971759388","https://openalex.org/W2025800131","https://openalex.org/W2129974284","https://openalex.org/W2007544051","https://openalex.org/W2035456249","https://openalex.org/W2021186063","https://openalex.org/W2004370856"],"abstract_inverted_index":{"Over":[0],"the":[1,16,29,61,85,168,176,179,191],"last":[2],"several":[3,119],"years":[4],"it":[5],"has":[6,193],"been":[7,44],"shown":[8],"that":[9,26,68,138,141],"image-based":[10],"object":[11,114,143,159,204],"detectors":[12],"are":[13,52,171],"sensitive":[14],"to":[15,22,24,46,54,60,146,151,173,187,209],"training":[17,31],"data":[18,70],"and":[19,87,95,111,133,163,221],"often":[20],"fail":[21],"generalize":[23],"examples":[25],"fall":[27],"outside":[28],"original":[30,86],"sample":[32],"domain":[33,39,101],"(e.g.,":[34,63],"videos).":[35],"A":[36],"number":[37],"of":[38,121,129,178,198,215],"adaptation":[40],"(DA)":[41],"techniques":[42],"have":[43],"proposed":[45],"address":[47],"this":[48,99,104],"problem.":[49],"DA":[50],"approaches":[51],"designed":[53],"adapt":[55],"a":[56,108],"fixed":[57],"complexity":[58,94,177],"model":[59,93,127,192],"new":[62,109,185],"video)":[64],"domain.":[65],"We":[66,97,195],"posit":[67],"unlabeled":[69],"should":[71],"not":[72],"only":[73],"allow":[74],"adaptation,":[75],"but":[76],"also":[77],"improve":[78],"(or":[79],"at":[80],"least":[81],"maintain)":[82],"performance":[83,197],"on":[84,118,207],"other":[88],"domains":[89,190],"by":[90,183,201],"dynamically":[91,174],"adjusting":[92],"parameters.":[96],"call":[98],"notion":[100],"expansion.":[102],"To":[103],"end,":[105],"we":[106,170],"develop":[107],"scalable":[110],"accurate":[112],"incremental":[113],"detection":[115,126],"algorithm,":[116],"based":[117],"extensions":[120],"large-margin":[122],"embedding":[123,131,139],"(LME).":[124],"Our":[125],"consists":[128],"an":[130,203],"space":[132],"multiple":[134],"class":[135],"prototypes":[136,148,186],"in":[137,161,212],"space,":[140],"represent":[142],"classes;":[144],"distance":[145],"those":[147],"allows":[149],"us":[150],"reason":[152],"about":[153],"multi-class":[154],"detection.":[155],"By":[156],"incrementally":[157],"detecting":[158],"instances":[160],"video":[162],"adding":[164],"confident":[165],"detections":[166],"into":[167],"model,":[169],"able":[172],"adjust":[175],"detector":[180,205],"over":[181],"time":[182],"instantiating":[184],"span":[188],"all":[189],"seen.":[194],"test":[196],"our":[199],"approach":[200],"expanding":[202],"trained":[206],"ImageNet":[208],"detect":[210],"objects":[211],"egocentric":[213],"videos":[214,223],"Activity":[216],"Daily":[217],"Living":[218],"(ADL)":[219],"dataset":[220],"challenging":[222],"from":[224],"YouTube":[225],"Objects":[226],"(YTO)":[227],"dataset.":[228]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":12}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
