{"id":"https://openalex.org/W4388190970","doi":"https://doi.org/10.1145/3581783.3613774","title":"A Figure Skating Jumping Dataset for Replay-Guided Action Quality Assessment","display_name":"A Figure Skating Jumping Dataset for Replay-Guided Action Quality Assessment","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388190970","doi":"https://doi.org/10.1145/3581783.3613774"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3613774","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3613774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5012607880","display_name":"Yanchao Liu","orcid":"https://orcid.org/0000-0002-6165-7060"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yanchao Liu","raw_affiliation_strings":["Waseda University, Kitakyushu, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6165-7060","affiliations":[{"raw_affiliation_string":"Waseda University, Kitakyushu, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007867868","display_name":"Xina Cheng","orcid":"https://orcid.org/0000-0001-7319-1635"},"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":"Xina Cheng","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-7319-1635","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103206427","display_name":"Takeshi Ikenaga","orcid":"https://orcid.org/0000-0001-8338-8175"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Ikenaga","raw_affiliation_strings":["Waseda University, Kitakyushu, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8338-8175","affiliations":[{"raw_affiliation_string":"Waseda University, Kitakyushu, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5719,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85770499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2437","last_page":"2445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9810000061988831,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9710999727249146,"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":0.8134564757347107},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6738723516464233},{"id":"https://openalex.org/keywords/rubric","display_name":"Rubric","score":0.5531408786773682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5052450299263},{"id":"https://openalex.org/keywords/zoom","display_name":"Zoom","score":0.4644849896430969},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.43190449476242065},{"id":"https://openalex.org/keywords/quality-assessment","display_name":"Quality assessment","score":0.4280223250389099},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4027630090713501},{"id":"https://openalex.org/keywords/evaluation-methods","display_name":"Evaluation methods","score":0.17101305723190308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8134564757347107},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6738723516464233},{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.5531408786773682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5052450299263},{"id":"https://openalex.org/C124913957","wikidata":"https://www.wikidata.org/wiki/Q1232548","display_name":"Zoom","level":3,"score":0.4644849896430969},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.43190449476242065},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.4280223250389099},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4027630090713501},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.17101305723190308},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C15336307","wikidata":"https://www.wikidata.org/wiki/Q1766051","display_name":"Lens (geology)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3613774","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3613774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G474819574","display_name":null,"funder_award_id":"KAKENHI (21K11816)","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2260521078","https://openalex.org/W2552035314","https://openalex.org/W2602920898","https://openalex.org/W2796214461","https://openalex.org/W2905550006","https://openalex.org/W2939039198","https://openalex.org/W2953867939","https://openalex.org/W2963139324","https://openalex.org/W2963524571","https://openalex.org/W2963867213","https://openalex.org/W3034623254","https://openalex.org/W3034658206","https://openalex.org/W3035050855","https://openalex.org/W3035130921","https://openalex.org/W3035303837","https://openalex.org/W3035413240","https://openalex.org/W3035714233","https://openalex.org/W3104432418","https://openalex.org/W3120254722","https://openalex.org/W3175528717","https://openalex.org/W3177433885","https://openalex.org/W3178701062","https://openalex.org/W3193654597","https://openalex.org/W3196971551","https://openalex.org/W3204116406","https://openalex.org/W3207409354","https://openalex.org/W4221142137","https://openalex.org/W4296181607","https://openalex.org/W4304080460","https://openalex.org/W4312419390","https://openalex.org/W4312658081","https://openalex.org/W4362710663"],"related_works":["https://openalex.org/W1987813225","https://openalex.org/W4390610055","https://openalex.org/W4379053243","https://openalex.org/W2029032313","https://openalex.org/W4315563560","https://openalex.org/W2553559590","https://openalex.org/W2772061936","https://openalex.org/W2143990275","https://openalex.org/W2992816059","https://openalex.org/W2159603254"],"abstract_inverted_index":{"In":[0,138],"competitive":[1],"sports,":[2],"judges":[3],"often":[4],"scrutinize":[5],"replay":[6,72,111],"videos":[7],"from":[8,30],"multiple":[9],"views":[10,134],"to":[11,125,178,190],"adjudicate":[12],"uncertain":[13],"or":[14,34,146],"contentious":[15],"actions,":[16],"and":[17,38,47,74,93,103,109,135],"ultimately":[18],"ascertain":[19],"the":[20,45,70,100,107,118,123,129,142,163,176,186,209,215],"definitive":[21],"score.":[22],"Most":[23],"existing":[24,216],"action":[25,84,131],"quality":[26,85],"assessment":[27],"methods":[28],"regress":[29],"a":[31,35,57,80,89,94,167],"single":[32],"video":[33,73,164],"pairwise":[36,101],"exemplar":[37],"input":[39,102,108],"videos,":[40],"which":[41],"are":[42,151,158],"limited":[43],"by":[44,69,88,112],"viewpoint":[46],"zoom":[48,136],"scale":[49],"of":[50,120,128,149,162,206],"videos.":[51],"To":[52],"end":[53],"this,":[54],"we":[55,105],"construct":[56],"Replay":[58],"Figure":[59],"Skating":[60],"Jumping":[61],"dataset":[62],"(RFSJ),":[63],"containing":[64],"additional":[65],"view":[66],"information":[67],"provided":[68],"post-match":[71],"fine-grained":[75],"annotations.":[76],"We":[77],"also":[78],"propose":[79],"Replay-Guided":[81],"approach":[82],"for":[83],"assessment,":[86],"learned":[87],"Triple-Stream":[90],"Contrastive":[91],"Transformer":[92],"Temporal":[95],"Concentration":[96],"Module.":[97],"Specifically,":[98],"besides":[99],"exemplar,":[104],"contrast":[106],"its":[110],"an":[113,192],"extra":[114],"contrastive":[115,187],"module.":[116],"Then":[117],"consistency":[119],"scores":[121],"guides":[122],"model":[124,177],"learn":[126],"features":[127],"same":[130],"under":[132],"different":[133],"scales.":[137],"addition,":[139],"based":[140],"on":[141,180,208],"fact":[143],"that":[144,199],"errors":[145],"highlight":[147],"moments":[148,157],"athletes":[150],"crucial":[152],"factors":[153],"affecting":[154],"scoring,":[155],"these":[156,181],"concentrated":[159],"in":[160],"parts":[161],"rather":[165],"than":[166],"uniform":[168],"distribution.":[169],"The":[170],"proposed":[171,210],"temporal":[172],"concentration":[173],"module":[174,189],"encourages":[175],"concentrate":[179],"features,":[182],"then":[183],"cooperates":[184],"with":[185],"regression":[188],"obtain":[191],"effective":[193],"scoring":[194],"mechanism.":[195],"Extensive":[196],"experiments":[197],"demonstrate":[198],"our":[200],"method":[201],"achieves":[202],"Spearman's":[203],"Rank":[204],"Correlation":[205],"0.9346":[207],"RFSJ":[211],"dataset,":[212],"improving":[213],"over":[214],"state-of-the-art":[217],"methods.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
