{"id":"https://openalex.org/W3006559153","doi":null,"title":"Continual Universal Object Detection","display_name":"Continual Universal Object Detection","publication_year":2020,"publication_date":"2020-02-13","ids":{"openalex":"https://openalex.org/W3006559153","mag":"3006559153"},"language":"en","primary_location":{"id":"mag:3006559153","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.05347","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2002.05347","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026904363","display_name":"Xialei Liu","orcid":"https://orcid.org/0000-0001-8534-3026","observed_orcids":["https://orcid.org/0000-0001-8534-3026"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xialei Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100748251","display_name":"Hao Yang","orcid":"https://orcid.org/0000-0003-3590-4491","observed_orcids":["https://orcid.org/0000-0003-3590-4491"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Yang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3590-4491","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010126054","display_name":"Avinash Ravichandran","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Avinash Ravichandran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110699034","display_name":"Rahul Bhotika","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahul Bhotika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038328783","display_name":"Stefano Soatto","orcid":"https://orcid.org/0000-0003-2902-6362","observed_orcids":["https://orcid.org/0000-0003-2902-6362"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Soatto","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2902-6362","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9750999808311462,"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":0.9750999808311462,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.02449999935925007,"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":9.999999747378752e-05,"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/catastrophic-forgetting","display_name":"catastrophic forgetting","score":0.984000027179718},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.8569999933242798},{"id":"https://openalex.org/keywords/feature-distillation","display_name":"feature distillation","score":0.8220000267028809},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.6129999756813049},{"id":"https://openalex.org/keywords/top-down-attention","display_name":"top-down attention","score":0.8019999861717224},{"id":"https://openalex.org/keywords/bottom-up-attention","display_name":"bottom-up attention","score":0.9629999995231628}],"concepts":[{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.9029160737991333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.788614809513092},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7794210910797119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.680666983127594},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.601585328578949},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5943960547447205},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5497725605964661},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5299602150917053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5129429697990417},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5001885890960693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3214729130268097},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06958562135696411},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3006559153","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.05347","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3006559153","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2002.05347","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1821462560","https://openalex.org/W2150066425","https://openalex.org/W2561238782","https://openalex.org/W2618767506","https://openalex.org/W2737492962","https://openalex.org/W2747206364","https://openalex.org/W2786446225","https://openalex.org/W2788388592","https://openalex.org/W2791091755","https://openalex.org/W2899771611","https://openalex.org/W2939059980","https://openalex.org/W2963420686","https://openalex.org/W2963788399","https://openalex.org/W2963813679","https://openalex.org/W2964241181"],"related_works":["https://openalex.org/W3049372737","https://openalex.org/W6926389786","https://openalex.org/W4226436870","https://openalex.org/W4416903590","https://openalex.org/W3081700103","https://openalex.org/W4402754211","https://openalex.org/W3087060432","https://openalex.org/W4415182882","https://openalex.org/W4312250316","https://openalex.org/W4393160061"],"abstract_inverted_index":{"Object":[0],"detection":[1,158],"has":[2],"improved":[3],"significantly":[4],"in":[5,72,150],"recent":[6],"years":[7],"on":[8,26],"multiple":[9],"challenging":[10,120],"benchmarks.":[11],"However,":[12],"most":[13,119],"existing":[14],"detectors":[15,33],"are":[16,22],"still":[17],"domain-specific,":[18],"where":[19],"the":[20,68,82,105,118,143],"models":[21],"trained":[23],"and":[24,78,92,109],"tested":[25],"a":[27,51],"single":[28],"domain.":[29],"When":[30],"adapting":[31],"these":[32],"to":[34,95,103,112,129],"new":[35],"domains,":[36],"they":[37],"often":[38],"suffer":[39],"from":[40,59,81,132],"catastrophic":[41],"forgetting":[42,137],"of":[43,101,146],"previous":[44,133],"knowledge.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,65,84,122],"propose":[50,85,123],"continual":[52],"object":[53,69,157],"detector":[54,70],"that":[55],"can":[56],"learn":[57],"sequentially":[58],"different":[60,73,152,156],"domains":[61,77],"without":[62],"forgetting.":[63,97],"First,":[64],"explore":[66],"learning":[67],"continually":[71],"scenarios":[74,153],"across":[75,154],"various":[76],"categories.":[79],"Learning":[80],"analysis,":[83],"attentive":[86],"feature":[87,110],"distillation":[88,111],"leveraging":[89],"both":[90],"bottom-up":[91],"top-down":[93],"attentions":[94],"mitigate":[96],"It":[98],"takes":[99],"advantage":[100],"attention":[102],"ignore":[104],"noisy":[106],"background":[107],"information":[108],"provide":[113],"strong":[114],"supervision.":[115],"Finally,":[116],"for":[117,135],"scenarios,":[121],"an":[124],"adaptive":[125],"exemplar":[126],"sampling":[127],"method":[128,149],"leverage":[130],"exemplars":[131],"tasks":[134],"less":[136],"effectively.":[138],"The":[139],"experimental":[140],"results":[141],"show":[142],"excellent":[144],"performance":[145],"our":[147],"proposed":[148],"three":[151],"seven":[155],"datasets.":[159]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2020-02-24T00:00:00"}
