{"id":"https://openalex.org/W4386075831","doi":"https://doi.org/10.1109/cvpr52729.2023.02185","title":"Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models","display_name":"Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4386075831","doi":"https://doi.org/10.1109/cvpr52729.2023.02185"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52729.2023.02185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.02185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF 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/A5082306340","display_name":"Paul Micaelli","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Paul Micaelli","raw_affiliation_strings":["University of Edinburgh"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038984764","display_name":"Arash Vahdat","orcid":"https://orcid.org/0009-0005-9476-1306"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arash Vahdat","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108868459","display_name":"Hongxu Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongxu Yin","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056503617","display_name":"Jan Kautz","orcid":"https://orcid.org/0000-0002-8830-429X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jan Kautz","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066945976","display_name":"Pavlo Molchanov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavlo Molchanov","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5082306340"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":1.9384,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88320519,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"22814","last_page":"22825"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":1.0,"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/T11448","display_name":"Face recognition and analysis","score":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9745000004768372,"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/T10828","display_name":"Biometric Identification and Security","score":0.9552000164985657,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/landmark","display_name":"Landmark","score":0.9892156720161438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7463697791099548},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6694897413253784},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6668161749839783},{"id":"https://openalex.org/keywords/flicker","display_name":"Flicker","score":0.6603747606277466},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6269471645355225},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5612795948982239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5280184149742126},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5035802721977234},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4309680759906769},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13816294074058533}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9892156720161438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7463697791099548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6694897413253784},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6668161749839783},{"id":"https://openalex.org/C19743564","wikidata":"https://www.wikidata.org/wiki/Q25378119","display_name":"Flicker","level":2,"score":0.6603747606277466},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6269471645355225},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5612795948982239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5280184149742126},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5035802721977234},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4309680759906769},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13816294074058533},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52729.2023.02185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52729.2023.02185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":121,"referenced_works":["https://openalex.org/W1535041666","https://openalex.org/W1593822055","https://openalex.org/W1769974409","https://openalex.org/W1915668717","https://openalex.org/W1935685005","https://openalex.org/W1976948919","https://openalex.org/W1977821862","https://openalex.org/W1977822596","https://openalex.org/W2027222744","https://openalex.org/W2038281434","https://openalex.org/W2040100614","https://openalex.org/W2051434435","https://openalex.org/W2058961190","https://openalex.org/W2109606373","https://openalex.org/W2121684305","https://openalex.org/W2136000821","https://openalex.org/W2138406903","https://openalex.org/W2142780847","https://openalex.org/W2146991130","https://openalex.org/W2152826865","https://openalex.org/W2157285372","https://openalex.org/W2217426128","https://openalex.org/W2234876530","https://openalex.org/W2251810906","https://openalex.org/W2307770531","https://openalex.org/W2341528187","https://openalex.org/W2474575620","https://openalex.org/W2546611663","https://openalex.org/W2548264631","https://openalex.org/W2567154071","https://openalex.org/W2617539464","https://openalex.org/W2617750261","https://openalex.org/W2736728583","https://openalex.org/W2739748921","https://openalex.org/W2751681791","https://openalex.org/W2767915528","https://openalex.org/W2770121394","https://openalex.org/W2799041689","https://openalex.org/W2799930024","https://openalex.org/W2897558462","https://openalex.org/W2910603373","https://openalex.org/W2916798096","https://openalex.org/W2945570796","https://openalex.org/W2963566548","https://openalex.org/W2963755523","https://openalex.org/W2963789946","https://openalex.org/W2970900903","https://openalex.org/W2982772166","https://openalex.org/W2985243484","https://openalex.org/W3024582231","https://openalex.org/W3034241236","https://openalex.org/W3034384783","https://openalex.org/W3034634365","https://openalex.org/W3041404693","https://openalex.org/W3082900443","https://openalex.org/W3090968963","https://openalex.org/W3092693034","https://openalex.org/W3093565423","https://openalex.org/W3101998545","https://openalex.org/W3109923889","https://openalex.org/W3117088788","https://openalex.org/W3119543788","https://openalex.org/W3121594244","https://openalex.org/W3122663073","https://openalex.org/W3126161079","https://openalex.org/W3138697945","https://openalex.org/W3142648707","https://openalex.org/W3164941295","https://openalex.org/W3176131920","https://openalex.org/W3181047098","https://openalex.org/W3202069916","https://openalex.org/W3202782837","https://openalex.org/W3206810688","https://openalex.org/W3210339053","https://openalex.org/W3211726802","https://openalex.org/W3215641581","https://openalex.org/W4230159453","https://openalex.org/W4285784163","https://openalex.org/W4287757201","https://openalex.org/W4295676892","https://openalex.org/W4301496368","https://openalex.org/W4312800713","https://openalex.org/W4313130906","https://openalex.org/W4313161555","https://openalex.org/W4320013936","https://openalex.org/W6632149132","https://openalex.org/W6640455264","https://openalex.org/W6648088351","https://openalex.org/W6697925102","https://openalex.org/W6720549550","https://openalex.org/W6734122017","https://openalex.org/W6738027021","https://openalex.org/W6741832134","https://openalex.org/W6743586428","https://openalex.org/W6748331328","https://openalex.org/W6750455488","https://openalex.org/W6750843368","https://openalex.org/W6752307458","https://openalex.org/W6755288431","https://openalex.org/W6760979436","https://openalex.org/W6767563556","https://openalex.org/W6776162430","https://openalex.org/W6777262188","https://openalex.org/W6779581272","https://openalex.org/W6779806301","https://openalex.org/W6780178746","https://openalex.org/W6781203905","https://openalex.org/W6782344419","https://openalex.org/W6783862537","https://openalex.org/W6787184021","https://openalex.org/W6788125050","https://openalex.org/W6789572788","https://openalex.org/W6792069078","https://openalex.org/W6792793321","https://openalex.org/W6796055584","https://openalex.org/W6802010497","https://openalex.org/W6803081272","https://openalex.org/W6803278395","https://openalex.org/W6803525030","https://openalex.org/W6804017529","https://openalex.org/W6805692064"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W4243161226","https://openalex.org/W2950647290","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W1968481813","https://openalex.org/W2392886708"],"abstract_inverted_index":{"Cascaded":[0],"computation,":[1],"whereby":[2,120],"predictions":[3],"are":[4,82],"recurrently":[5],"refined":[6],"over":[7],"several":[8],"stages,":[9],"has":[10],"been":[11],"a":[12,65,112,121,137,172,181,223],"persistent":[13],"theme":[14],"throughout":[15],"the":[16,28,52,72,103,192,206,217],"development":[17],"of":[18,42,69,74,105,198],"landmark":[19,56,86,165,184,189],"detection":[20,87],"models.":[21],"In":[22,90],"this":[23,40,91],"work,":[24],"we":[25,78,140,168],"show":[26,79],"that":[27,80],"recently":[29],"proposed":[30],"Deep":[31],"Equilibrium":[32],"Model":[33],"(DEQ)":[34],"can":[35,109,123],"be":[36],"naturally":[37],"adapted":[38],"to":[39,96,102,111,150,216],"form":[41],"computation.":[43],"Our":[44],"Landmark":[45],"DEQ":[46],"(LDEQ)":[47],"achieves":[48],"state-of-the-art":[49],"performance":[50],"on":[51,98,118],"challenging":[53],"WFLW":[54],"facial":[55,183],"dataset,":[57],"reaching":[58],"3.92":[59],"NME":[60,207],"with":[61,203],"fewer":[62],"parameters":[63],"and":[64,179,208,212],"training":[66,154],"memory":[67],"cost":[68],"O(1)":[70],"in":[71,88,163],"number":[73],"recurrent":[75],"modules.":[76],"Furthermore,":[77],"DEQs":[81,135],"particularly":[83],"suited":[84],"for":[85],"videos.":[89,107],"setting,":[92],"it":[93],"is":[94],"typical":[95],"train":[97],"still":[99],"images":[100],"due":[101],"lack":[104],"labelled":[106],"This":[108,156],"lead":[110],"\u201cflickering\u201d":[113],"effect":[114],"at":[115,143,153],"inference":[116,144],"time":[117],"video,":[119],"model":[122,221],"rapidly":[124],"oscillate":[125],"between":[126],"different":[127],"plausible":[128],"solutions":[129],"across":[130],"consecutive":[131],"frames.":[132],"By":[133],"rephrasing":[134],"as":[136],"constrained":[138],"optimization,":[139],"emulate":[141],"recurrence":[142],"time,":[145],"despite":[146],"not":[147],"having":[148],"access":[149],"temporal":[151],"data":[152],"time.":[155],"Recurrence":[157,159],"without":[158],"(RwR)":[160],"paradigm":[161],"helps":[162],"reducing":[164],"flicker,":[166],"which":[167],"demonstrate":[169],"by":[170,210],"introducing":[171],"new":[173,182],"metric,":[174],"normalized":[175],"mean":[176],"flicker":[177],"(NMF),":[178],"contributing":[180],"video":[185],"dataset":[186],"(WFLW-V)":[187],"targeting":[188],"uncertainty.":[190],"On":[191],"WFLW-V":[193],"hard":[194],"subset":[195],"made":[196],"up":[197],"500":[199],"videos,":[200],"our":[201],"LDEQ":[202],"RwR":[204],"improves":[205],"NMF":[209],"10":[211],"13%":[213],"respectively,":[214],"compared":[215],"strongest":[218],"previously":[219],"published":[220],"using":[222],"hand-tuned":[224],"conventional":[225],"filter.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}