{"id":"https://openalex.org/W2613192286","doi":"https://doi.org/10.1109/tmm.2017.2762010","title":"Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation","display_name":"Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation","publication_year":2017,"publication_date":"2017-10-11","ids":{"openalex":"https://openalex.org/W2613192286","doi":"https://doi.org/10.1109/tmm.2017.2762010","mag":"2613192286"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2017.2762010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2017.2762010","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Multimedia","raw_type":"journal-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/A5082349270","display_name":"Guanghan Ning","orcid":"https://orcid.org/0000-0002-4356-7862"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guanghan Ning","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410702","display_name":"Zhi Zhang","orcid":"https://orcid.org/0000-0003-0249-1678"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi Zhang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101709194","display_name":"Zhiquan He","orcid":"https://orcid.org/0000-0003-2255-4293"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiquan He","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082349270"],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":null,"apc_paid":null,"fwci":7.4638,"has_fulltext":false,"cited_by_count":184,"citation_normalized_percentile":{"value":0.98231321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"20","issue":"5","first_page":"1246","last_page":"1259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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":0.9973000288009644,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.995199978351593,"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":0.8273786306381226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7329378724098206},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6426745057106018},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5421276092529297},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5374060273170471},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4956294298171997},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46117034554481506},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4574096202850342},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.45384281873703003},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4196147620677948},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4098120927810669},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3219561278820038},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18855515122413635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8273786306381226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7329378724098206},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6426745057106018},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5421276092529297},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5374060273170471},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4956294298171997},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46117034554481506},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4574096202850342},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.45384281873703003},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4196147620677948},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4098120927810669},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3219561278820038},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18855515122413635},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2017.2762010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2017.2762010","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":103,"referenced_works":["https://openalex.org/W23953656","https://openalex.org/W171061157","https://openalex.org/W602397586","https://openalex.org/W639708223","https://openalex.org/W753012316","https://openalex.org/W1546771929","https://openalex.org/W1594587862","https://openalex.org/W1606844999","https://openalex.org/W1686810756","https://openalex.org/W1690739335","https://openalex.org/W1821462560","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1936750108","https://openalex.org/W1948369226","https://openalex.org/W1993149133","https://openalex.org/W1998294030","https://openalex.org/W2013640163","https://openalex.org/W2021387930","https://openalex.org/W2022699039","https://openalex.org/W2023866509","https://openalex.org/W2030536784","https://openalex.org/W2045798786","https://openalex.org/W2046875449","https://openalex.org/W2057204268","https://openalex.org/W2065717806","https://openalex.org/W2074587583","https://openalex.org/W2080873731","https://openalex.org/W2089665312","https://openalex.org/W2097117768","https://openalex.org/W2097151019","https://openalex.org/W2113325037","https://openalex.org/W2116626343","https://openalex.org/W2117499988","https://openalex.org/W2123263858","https://openalex.org/W2128271252","https://openalex.org/W2134797427","https://openalex.org/W2135533529","https://openalex.org/W2136391815","https://openalex.org/W2137203603","https://openalex.org/W2141200610","https://openalex.org/W2142540304","https://openalex.org/W2143478373","https://openalex.org/W2143487029","https://openalex.org/W2150383810","https://openalex.org/W2155394491","https://openalex.org/W2155893237","https://openalex.org/W2161969291","https://openalex.org/W2168894214","https://openalex.org/W2174722029","https://openalex.org/W2175012183","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2294370754","https://openalex.org/W2295474072","https://openalex.org/W2307770531","https://openalex.org/W2330914857","https://openalex.org/W2342651600","https://openalex.org/W2345033116","https://openalex.org/W2345945060","https://openalex.org/W2375583958","https://openalex.org/W2382036597","https://openalex.org/W2395611524","https://openalex.org/W2464305746","https://openalex.org/W2518965973","https://openalex.org/W2563056398","https://openalex.org/W2567442802","https://openalex.org/W2612947062","https://openalex.org/W2613718673","https://openalex.org/W2949117887","https://openalex.org/W2952074561","https://openalex.org/W2963037989","https://openalex.org/W2963448913","https://openalex.org/W2963474899","https://openalex.org/W2963484342","https://openalex.org/W2964118293","https://openalex.org/W2964304707","https://openalex.org/W2964350391","https://openalex.org/W6620707391","https://openalex.org/W6622239757","https://openalex.org/W6632360823","https://openalex.org/W6635560335","https://openalex.org/W6637373629","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6638667902","https://openalex.org/W6640740087","https://openalex.org/W6677604277","https://openalex.org/W6679909955","https://openalex.org/W6680285999","https://openalex.org/W6682891246","https://openalex.org/W6684665197","https://openalex.org/W6685299716","https://openalex.org/W6694260854","https://openalex.org/W6697925102","https://openalex.org/W6701707986","https://openalex.org/W6707250398","https://openalex.org/W6709066369","https://openalex.org/W6709775842","https://openalex.org/W6726294871","https://openalex.org/W6737373811","https://openalex.org/W6764072534"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2988126442","https://openalex.org/W1974414866","https://openalex.org/W2063982682","https://openalex.org/W2338543196","https://openalex.org/W1544691147","https://openalex.org/W1499137908"],"abstract_inverted_index":{"Human":[0],"pose":[1,142,220],"estimation":[2,221],"using":[3,117,187,192],"deep":[4,55,109],"neural":[5,56,110,185],"networks":[6,111],"aims":[7],"to":[8,71,96,112,134,139,161],"map":[9],"input":[10],"images":[11,143],"with":[12,146,155,210],"large":[13],"variations":[14],"into":[15,107,144,183],"multiple":[16],"body":[17,33,167],"keypoints,":[18],"which":[19,58,158],"must":[20],"satisfy":[21],"a":[22,37,45,136,188],"set":[23],"of":[24,165,173,199],"geometric":[25,82],"constraints":[26,164],"and":[27,84,105,131,169,204],"interdependence":[28,85],"imposed":[29],"by":[30],"the":[31,54,67,108,127,163,171,184,200,205],"human":[32,75,89,141,166,219],"model.":[34],"This":[35],"is":[36,59,65,213],"very":[38,46],"challenging":[39],"nonlinear":[40],"manifold":[41],"learning":[42,209],"process":[43,116],"in":[44,88,207],"high":[47],"dimensional":[48],"feature":[49],"space.":[50],"We":[51,151,177],"believe":[52],"that":[53,120],"network,":[57],"inherently":[60],"an":[61,193],"algebraic":[62],"computation":[63],"system,":[64],"not":[66],"most":[68],"efficient":[69],"way":[70],"capture":[72],"highly":[73,80],"sophisticated":[74],"knowledge,":[76],"for":[77],"example":[78],"those":[79],"coupled":[81],"characteristics":[83],"between":[86],"keypoints":[87],"poses.":[90],"In":[91],"this":[92],"work,":[93],"we":[94,125],"propose":[95],"explore":[97],"how":[98],"external":[99,153,181],"knowledge":[100,154,211],"can":[101],"be":[102],"effectively":[103],"represented":[104],"injected":[106],"guide":[113],"its":[114],"training":[115],"learned":[118,191],"projections":[119],"impose":[121],"proper":[122],"prior.":[123],"Specifically,":[124],"use":[126],"stacked":[128],"hourglass":[129],"design":[130],"inception-resnet":[132,202],"module":[133,203],"construct":[135],"fractal":[137],"network":[138,175,186],"regress":[140],"heatmaps":[145],"no":[147],"explicit":[148],"graphical":[149],"modeling.":[150],"encode":[152],"visual":[156],"features,":[157],"are":[159],"able":[160],"characterize":[162],"models":[168],"evaluate":[170],"fitness":[172],"intermediate":[174],"output.":[176],"then":[178],"inject":[179],"these":[180],"features":[182],"projection":[189,212],"matrix":[190],"auxiliary":[194],"cost":[195],"function.":[196],"The":[197],"effectiveness":[198],"proposed":[201],"benefit":[206],"guided":[208],"evaluated":[214],"on":[215,228],"two":[216],"widely":[217],"used":[218],"benchmarks.":[222],"Our":[223],"approach":[224],"achieves":[225],"state-of-the-art":[226],"performance":[227],"both":[229],"datasets.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":26},{"year":2020,"cited_by_count":33},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}