{"id":"https://openalex.org/W3028265921","doi":"https://doi.org/10.1109/wacvw50321.2020.9096948","title":"Adaptive Feature Aggregation for Video Object Detection","display_name":"Adaptive Feature Aggregation for Video Object Detection","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3028265921","doi":"https://doi.org/10.1109/wacvw50321.2020.9096948","mag":"3028265921"},"language":"en","primary_location":{"id":"doi:10.1109/wacvw50321.2020.9096948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacvw50321.2020.9096948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)","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/A5084965116","display_name":"Yijun Qian","orcid":"https://orcid.org/0009-0000-9440-9744"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yijun Qian","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056705600","display_name":"Lijun Yu","orcid":"https://orcid.org/0000-0003-0645-1657"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijun Yu","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045007670","display_name":"Wenhe Liu","orcid":"https://orcid.org/0000-0003-4679-2958"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhe Liu","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011488839","display_name":"Guoliang Kang","orcid":"https://orcid.org/0000-0003-1978-2025"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guoliang Kang","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103099928","display_name":"Alexander G. Hauptmann","orcid":"https://orcid.org/0000-0003-2123-0684"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander G. Hauptmann","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5084965116"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.8831,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76092507,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"143","last_page":"147"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9990000128746033,"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":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8279886245727539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6463969945907593},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6351521611213684},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6346054077148438},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6143217086791992},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6034903526306152},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5974234938621521},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.535738468170166},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5318517088890076},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4127749502658844},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35574889183044434},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3480459153652191}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8279886245727539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6463969945907593},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6351521611213684},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6346054077148438},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6143217086791992},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6034903526306152},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5974234938621521},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.535738468170166},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5318517088890076},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4127749502658844},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35574889183044434},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3480459153652191},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacvw50321.2020.9096948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacvw50321.2020.9096948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4099999964237213,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2142996775","https://openalex.org/W2194775991","https://openalex.org/W2407521645","https://openalex.org/W2603203130","https://openalex.org/W2613718673","https://openalex.org/W2777578098","https://openalex.org/W2793130599","https://openalex.org/W2903541953","https://openalex.org/W2950800384","https://openalex.org/W2963653352","https://openalex.org/W2964086649","https://openalex.org/W2964286567","https://openalex.org/W3007708315","https://openalex.org/W3013542938","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6714138976","https://openalex.org/W6746949076","https://openalex.org/W6774899143"],"related_works":["https://openalex.org/W2012531322","https://openalex.org/W2785900585","https://openalex.org/W2353730437","https://openalex.org/W2490303674","https://openalex.org/W2609066826","https://openalex.org/W2810752900","https://openalex.org/W3186538219","https://openalex.org/W2365677836","https://openalex.org/W2531295127","https://openalex.org/W4292830139"],"abstract_inverted_index":{"Object":[0],"detection,":[1],"as":[2],"a":[3,100],"fundamental":[4],"research":[5],"topic":[6],"of":[7,14,27,45,52,58,135,147],"computer":[8],"vision,":[9],"is":[10],"facing":[11],"the":[12,50,56,132],"challenges":[13],"video-related":[15],"tasks.":[16],"Objects":[17],"in":[18,138],"videos":[19,146],"tend":[20],"to":[21,38,88,142],"be":[22],"blurred,":[23],"occluded,":[24],"or":[25,71],"out":[26],"focus":[28],"more":[29],"frequently.":[30],"Existing":[31],"works":[32],"adopt":[33],"feature":[34,81],"aggregation":[35,82,105],"and":[36,55,102,114,119],"enhancement":[37],"design":[39],"video-based":[40,115],"object":[41,53,86],"detectors.":[42],"However,":[43],"most":[44],"them":[46],"do":[47],"not":[48,65],"consider":[49],"diversity":[51],"movements":[54],"quality":[57],"aggregated":[59],"context":[60],"features.":[61],"Thus,":[62],"they":[63],"can":[64],"generate":[66],"comparable":[67],"results":[68],"given":[69],"blurred":[70],"crowded":[72],"videos.":[73],"In":[74],"this":[75,148],"paper,":[76],"we":[77],"propose":[78],"an":[79,95],"adaptive":[80,96],"method":[83],"for":[84],"video":[85],"detection":[87,137],"deal":[89],"with":[90,99,111],"these":[91],"problems.":[92],"We":[93],"introduce":[94],"quality-similarity":[97],"weight,":[98],"sparse":[101],"dense":[103],"temporal":[104],"policy,":[106],"into":[107],"our":[108,122,129],"model.":[109],"Compared":[110],"both":[112],"image-based":[113],"baselines":[116],"on":[117],"Im-ageNet":[118],"VIRAT":[120,139],"datasets,":[121],"work":[123,149],"consistently":[124],"demonstrates":[125],"better":[126],"performance.":[127],"Especially,":[128],"model":[130],"improves":[131],"average":[133],"precision":[134],"person":[136],"from":[140],"85.93%":[141],"87.21%.":[143],"Several":[144],"demonstration":[145],"are":[150],"available":[151],"<sup":[152],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[153],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[154],".":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}