{"id":"https://openalex.org/W3215955967","doi":"https://doi.org/10.1109/tpami.2021.3130302","title":"Optical Flow in the Dark","display_name":"Optical Flow in the Dark","publication_year":2021,"publication_date":"2021-11-24","ids":{"openalex":"https://openalex.org/W3215955967","doi":"https://doi.org/10.1109/tpami.2021.3130302","mag":"3215955967","pmid":"https://pubmed.ncbi.nlm.nih.gov/34818188"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2021.3130302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3130302","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,"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"},"type":"article","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":null,"display_name":"Mingfang Zhang","orcid":"https://orcid.org/0000-0003-1792-6654"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingfang Zhang","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-1792-6654","affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yinqiang Zheng","orcid":"https://orcid.org/0000-0001-7434-5069"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yinqiang Zheng","raw_affiliation_strings":["Next Generation Artificial Intelligence Research Center, the University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-7434-5069","affiliations":[{"raw_affiliation_string":"Next Generation Artificial Intelligence Research Center, the University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":null,"display_name":"Feng Lu","orcid":"https://orcid.org/0000-0001-9064-7964"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Lu","raw_affiliation_strings":["State Key Laboratory of VR system and technology, SCSE, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9064-7964","affiliations":[{"raw_affiliation_string":"State Key Laboratory of VR system and technology, SCSE, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"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":0.3658,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.57731697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"44","issue":"12","first_page":"9464","last_page":"9476"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9769999980926514,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9769999980926514,"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/T11019","display_name":"Image Enhancement Techniques","score":0.002899999963119626,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.001500000013038516,"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/optical-flow","display_name":"Optical flow","score":0.9258999824523926},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.6025999784469604},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5667999982833862},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4659999907016754},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4528999924659729},{"id":"https://openalex.org/keywords/optical-imaging","display_name":"Optical imaging","score":0.4311999976634979},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.37790000438690186},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.36469998955726624},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34619998931884766}],"concepts":[{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.9258999824523926},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258999943733215},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.6025999784469604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5364999771118164},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C92630104","wikidata":"https://www.wikidata.org/wiki/Q4115103","display_name":"Optical imaging","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C148204187","wikidata":"https://www.wikidata.org/wiki/Q176541","display_name":"Optical computing","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C151857401","wikidata":"https://www.wikidata.org/wiki/Q640621","display_name":"Optical engineering","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C128717455","wikidata":"https://www.wikidata.org/wiki/Q2024185","display_name":"3D optical data storage","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2858999967575073},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C194232370","wikidata":"https://www.wikidata.org/wiki/Q162","display_name":"Optical fiber","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2986007928","wikidata":"https://www.wikidata.org/wiki/Q478798","display_name":"Optical image","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.2574000060558319},{"id":"https://openalex.org/C45613198","wikidata":"https://www.wikidata.org/wiki/Q1134091","display_name":"Optical filter","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2021.3130302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2021.3130302","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,"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:34818188","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34818188","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,"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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3570431576","display_name":null,"funder_award_id":"61972012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1513100184","https://openalex.org/W1921093919","https://openalex.org/W2041625018","https://openalex.org/W2044810215","https://openalex.org/W2115579991","https://openalex.org/W2136035751","https://openalex.org/W2254039850","https://openalex.org/W2548527721","https://openalex.org/W2560474170","https://openalex.org/W2799192307","https://openalex.org/W2799265886","https://openalex.org/W2894683476","https://openalex.org/W2952323569","https://openalex.org/W2962767526","https://openalex.org/W2963073614","https://openalex.org/W2963200935","https://openalex.org/W2963782415","https://openalex.org/W2964156315","https://openalex.org/W2979762954","https://openalex.org/W2986422266","https://openalex.org/W2996488097","https://openalex.org/W2997694100","https://openalex.org/W3034239311","https://openalex.org/W3035155487","https://openalex.org/W3035160371","https://openalex.org/W3108679343","https://openalex.org/W3109908659","https://openalex.org/W3110002552","https://openalex.org/W4242059867","https://openalex.org/W6755037456","https://openalex.org/W6761854729"],"related_works":[],"abstract_inverted_index":{"Optical":[0],"flow":[1,16,42,66,86,94,109,143,162,182,197],"estimation":[2],"in":[3,88,216],"low-light":[4,19,73,107,119,138],"conditions":[5],"is":[6,211],"a":[7,59,79,83,100,105,170],"challenging":[8],"task":[9],"for":[10,164,184],"existing":[11,114],"methods":[12],"and":[13,71,204],"current":[14],"optical":[15,41,65,85,93,108,142,161,181,196],"datasets":[17,177],"lack":[18],"samples.":[20],"Even":[21],"if":[22],"the":[23,53,123,131,150,195,200,205],"dark":[24],"images":[25,126],"are":[26],"enhanced":[27],"before":[28],"estimation,":[29],"which":[30],"could":[31],"achieve":[32],"great":[33],"visual":[34],"perception,":[35],"it":[36],"still":[37],"leads":[38],"to":[39,57,63,81,103,158,178],"suboptimal":[40],"results":[43],"because":[44],"information":[45],"like":[46],"motion":[47],"consistency":[48],"may":[49],"be":[50],"broken":[51],"during":[52],"enhancement.":[54],"We":[55],"propose":[56],"apply":[58,99,169],"novel":[60],"training":[61,147,172],"policy":[62,173],"learn":[64],"directly":[67],"from":[68,122],"new":[69,84],"synthetic":[70,106],"real":[72],"images.":[74],"Specifically,":[75],"first,":[76],"we":[77,98,127,134,153,168],"design":[78],"method":[80,191,210],"collect":[82],"dataset":[87],"multiple":[89,217],"exposures":[90],"with":[91,149,174,213],"shared":[92],"pseudo":[95],"labels.":[96,144],"Then":[97],"two-step":[101],"process":[102],"create":[104,154],"dataset,":[110],"based":[111],"on":[112],"an":[113],"bright":[115],"one,":[116],"by":[117],"simulating":[118],"raw":[120,125,139],"features":[121],"multi-exposure":[124],"collected.":[128],"To":[129],"extend":[130],"data":[132],"diversity,":[133],"also":[135],"include":[136],"published":[137],"videos":[140],"without":[141],"In":[145],"our":[146,175,190,209],"pipeline,":[148],"three":[151],"datasets,":[152],"two":[155],"teacher-student":[156],"pairs":[157],"progressively":[159],"obtain":[160],"labels":[163],"all":[165],"data.":[166],"Finally,":[167],"mix-up":[171],"diversified":[176],"produce":[179],"low-light-robust":[180],"models":[183],"release.":[185],"The":[186],"experiments":[187],"show":[188],"that":[189],"can":[192],"relatively":[193],"maintain":[194],"accuracy":[198],"as":[199],"image":[201],"exposure":[202],"descends":[203],"generalization":[206],"ability":[207],"of":[208],"tested":[212],"different":[214],"cameras":[215],"practical":[218],"scenes.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2021-12-06T00:00:00"}