{"id":"https://openalex.org/W4411635470","doi":"https://doi.org/10.1145/3731715.3733468","title":"A Prior Representation-Guided Method for Low-Resolution Human Pose Estimation","display_name":"A Prior Representation-Guided Method for Low-Resolution Human Pose Estimation","publication_year":2025,"publication_date":"2025-06-25","ids":{"openalex":"https://openalex.org/W4411635470","doi":"https://doi.org/10.1145/3731715.3733468"},"language":"en","primary_location":{"id":"doi:10.1145/3731715.3733468","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","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":null,"display_name":"Mengting Jiang","orcid":"https://orcid.org/0009-0001-4894-8867"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengting Jiang","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113188032","display_name":"Xiaoqi An","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqi An","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yang Gao","orcid":"https://orcid.org/0009-0007-8873-0196"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Gao","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075204048","display_name":"Yalong Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yalong Xu","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401389","display_name":"Di Wang","orcid":"https://orcid.org/0000-0001-8027-4287"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110277601","display_name":"Lin Zhao","orcid":"https://orcid.org/0000-0002-8756-2027"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhao","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12567412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1988","last_page":"1992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6811363697052002},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6446647644042969},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6137733459472656},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5928176045417786},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5650964975357056},{"id":"https://openalex.org/keywords/low-resolution","display_name":"Low resolution","score":0.5402261018753052},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.4393930733203888},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.43437743186950684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39814773201942444},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.24237284064292908},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.11058112978935242},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07717975974082947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6811363697052002},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6446647644042969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137733459472656},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5928176045417786},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5650964975357056},{"id":"https://openalex.org/C3019883945","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Low resolution","level":3,"score":0.5402261018753052},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.4393930733203888},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.43437743186950684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39814773201942444},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.24237284064292908},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.11058112978935242},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07717975974082947},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3731715.3733468","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3731715.3733468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1326197445","display_name":null,"funder_award_id":"SQ2023YFE0102775","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2080873731","https://openalex.org/W2194775991","https://openalex.org/W2916798096","https://openalex.org/W2963402313","https://openalex.org/W2963598138","https://openalex.org/W2979581740","https://openalex.org/W3034742259","https://openalex.org/W3034750257","https://openalex.org/W3162686812","https://openalex.org/W3176892444","https://openalex.org/W3203925315","https://openalex.org/W3204595807","https://openalex.org/W4210851988","https://openalex.org/W4224992933","https://openalex.org/W4229890965","https://openalex.org/W4313127332","https://openalex.org/W4386065649","https://openalex.org/W4390874241","https://openalex.org/W4390874574","https://openalex.org/W4402753275","https://openalex.org/W6638523607"],"related_works":["https://openalex.org/W4253893311","https://openalex.org/W3089306886","https://openalex.org/W2113785214","https://openalex.org/W3201205132","https://openalex.org/W2798721181","https://openalex.org/W4387967917","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4382141741","https://openalex.org/W2951583186"],"abstract_inverted_index":{"Human":[0],"pose":[1,49,90],"estimation":[2,91],"has":[3],"achieved":[4],"significant":[5,136],"progress":[6],"on":[7,17,95,126],"high-resolution":[8],"(HR)":[9],"images,":[10,122],"but":[11],"it":[12],"experiences":[13],"severe":[14],"performance":[15],"degradation":[16],"low-resolution":[18,47],"(LR)":[19],"images.":[20,74,128],"One":[21],"key":[22],"reason":[23],"is":[24],"that":[25,81,132],"LR":[26,121],"images":[27],"lack":[28],"sufficient":[29],"appearance":[30],"details":[31],"and":[32,141],"fine-grained":[33],"spatial":[34],"information.":[35],"In":[36,101,144],"this":[37],"paper,":[38],"we":[39,61,76,105],"propose":[40,77],"a":[41,63,107,157],"prior":[42,64,70,85,118],"representation-guided":[43],"method":[44,134,147],"(PRG)":[45],"for":[46],"human":[48],"estimation.":[50],"Our":[51],"approach":[52],"consists":[53],"of":[54,159],"two":[55],"stages:":[56],"in":[57,93],"the":[58,83,89,102,116,124,153],"first":[59],"stage,":[60,104],"design":[62],"representation":[65,71,86,119],"extraction":[66],"network":[67,92],"to":[68,87,114,152],"obtain":[69],"from":[72,120],"HR":[73,127],"Then":[75],"dynamic":[78],"residual":[79],"blocks":[80],"utilize":[82],"extracted":[84],"guide":[88],"focusing":[94],"detailed":[96],"features":[97],"around":[98],"joint":[99],"areas.":[100],"second":[103],"use":[106],"compact":[108],"diffusion":[109],"model":[110],"with":[111],"fewer":[112],"iterations":[113],"generate":[115],"consistent":[117],"eliminating":[123],"reliance":[125],"Extensive":[129],"experiments":[130],"demonstrate":[131],"our":[133,146],"achieves":[135],"improvements":[137],"across":[138],"various":[139],"resolutions":[140],"backbone":[142],"networks.":[143],"particular,":[145],"improves":[148],"16.4":[149],"AP":[150],"compared":[151],"SimCC-Res50":[154],"baseline":[155],"at":[156],"resolution":[158],"32\u00d732.":[160]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
