{"id":"https://openalex.org/W4415536764","doi":"https://doi.org/10.1145/3746027.3755050","title":"Adaptive Graph Attention-Guided Parallel Sampling and Embedded Selection for Multi-Model Fitting","display_name":"Adaptive Graph Attention-Guided Parallel Sampling and Embedded Selection for Multi-Model Fitting","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536764","doi":"https://doi.org/10.1145/3746027.3755050"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ro.ecu.edu.au/ecuworks2022-2026/7416","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108496927","display_name":"Wenyu Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyu Yin","raw_affiliation_strings":["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, the Ministry of Education of China, and the Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0009-0001-1252-0893","affiliations":[{"raw_affiliation_string":"Key Laboratory of Multimedia Trusted Perception and Efficient Computing, the Ministry of Education of China, and the Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068187231","display_name":"Shuyuan Lin","orcid":"https://orcid.org/0000-0003-3247-4625"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyuan Lin","raw_affiliation_strings":["College of Cyber Security, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3247-4625","affiliations":[{"raw_affiliation_string":"College of Cyber Security, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031492664","display_name":"David Suter","orcid":"https://orcid.org/0000-0001-6306-3023"},"institutions":[{"id":"https://openalex.org/I12079687","display_name":"Edith Cowan University","ror":"https://ror.org/05jhnwe22","country_code":"AU","type":"education","lineage":["https://openalex.org/I12079687"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"David Suter","raw_affiliation_strings":["School of Science, Edith Cowan University, Joondalup, Australia"],"raw_orcid":"https://orcid.org/0000-0001-6306-3023","affiliations":[{"raw_affiliation_string":"School of Science, Edith Cowan University, Joondalup, Australia","institution_ids":["https://openalex.org/I12079687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, the Ministry of Education of China, and the Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-6913-9786","affiliations":[{"raw_affiliation_string":"Key Laboratory of Multimedia Trusted Perception and Efficient Computing, the Ministry of Education of China, and the Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"10896","last_page":"10904"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T10057","display_name":"Face and Expression Recognition","score":0.996399998664856,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.996399998664856,"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/outlier","display_name":"Outlier","score":0.6096000075340271},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5616999864578247},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5497999787330627},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5044999718666077},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4350000023841858},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.39340001344680786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7427999973297119},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6096000075340271},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5616999864578247},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5497999787330627},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4350000023841858},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4090000092983246},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3928999900817871},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.35010001063346863},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28439998626708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27079999446868896},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3746027.3755050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:ro.ecu.edu.au:ecuworks2022-2026-8416","is_oa":true,"landing_page_url":"https://ro.ecu.edu.au/ecuworks2022-2026/7416","pdf_url":null,"source":{"id":"https://openalex.org/S2765015692","display_name":"Australasian Journal of Paramedicine","issn_l":"2202-7270","issn":["2202-7270"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research outputs 2022 to 2026","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:ro.ecu.edu.au:ecuworks2022-2026-8416","is_oa":true,"landing_page_url":"https://ro.ecu.edu.au/ecuworks2022-2026/7416","pdf_url":null,"source":{"id":"https://openalex.org/S2765015692","display_name":"Australasian Journal of Paramedicine","issn_l":"2202-7270","issn":["2202-7270"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research outputs 2022 to 2026","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2024276620","https://openalex.org/W2035889148","https://openalex.org/W2085261163","https://openalex.org/W2222512263","https://openalex.org/W3039782136","https://openalex.org/W3087268568","https://openalex.org/W3161431093","https://openalex.org/W4210892030","https://openalex.org/W4306832769","https://openalex.org/W4382283842","https://openalex.org/W4385342469","https://openalex.org/W4387197150","https://openalex.org/W4391128950"],"related_works":[],"abstract_inverted_index":{"Multi-model":[0],"fitting":[1,58,107],"is":[2,202],"a":[3,29,51,80,116,131,159,185,193],"fundamental":[4],"challenge":[5],"in":[6,42],"computer":[7],"vision,":[8],"where":[9],"real-world":[10],"data":[11,87,175],"often":[12],"contains":[13],"severe":[14],"gross":[15],"outliers":[16],"and":[17,34,65,72,100,150,174,192],"pseudo-outliers.":[18],"Existing":[19],"methods":[20],"rely":[21],"on":[22],"inefficient":[23],"sequential":[24],"hypothesize-and-verify":[25],"frameworks":[26],"that":[27,37,61,85,121,163,180],"require":[28],"predefined":[30],"number":[31],"of":[32,171,189],"models":[33,86],"inlier":[35],"thresholds-parameters":[36],"are":[38],"difficult":[39],"to":[40,103,125],"determine":[41],"practical":[43],"scenes.":[44],"To":[45,153],"overcome":[46],"these":[47],"limitations,":[48],"we":[49,78,114,157],"propose":[50,115],"novel":[52],"Adaptive":[53],"Graph":[54],"Attention-guided":[55],"parallel":[56,69,117],"multi-model":[57,106],"method":[59,183],"(AGASAC)":[60],"jointly":[62],"learns":[63],"local":[64],"global":[66],"features,":[67],"performs":[68],"hypothesis":[70,148],"sampling,":[71],"executes":[73],"confidence-embedded":[74],"model":[75,172],"selection.":[76],"Specifically,":[77],"design":[79],"dual-confidence":[81],"graph":[82,92],"attention":[83,93],"module":[84,96,120,136,162],"relationships":[88],"using":[89],"an":[90],"adaptive":[91],"network.":[94],"This":[95],"computes":[97],"minimal-set":[98,123],"confidence":[99,102,124,166],"quality":[101,165],"guide":[104],"the":[105,168,181],"process,":[108],"eliminating":[109],"manual":[110],"parameter":[111],"tuning.":[112],"Additionally,":[113],"discriminative":[118],"sampling":[119],"leverages":[122],"concurrently":[126],"sample":[127],"hypotheses.":[128],"By":[129],"enforcing":[130],"quantized":[132],"consensus":[133],"constraint,":[134],"this":[135],"maximizes":[137],"inter-model":[138],"variance":[139],"while":[140],"minimizing":[141],"intra-model":[142],"discrepancy.":[143],"It":[144],"enables":[145],"computationally":[146],"efficient":[147],"generation":[149],"pseudo-outlier":[151],"suppression.":[152],"obtain":[154],"high-quality":[155],"models,":[156],"present":[158],"quality-embedded":[160],"selection":[161,173],"integrates":[164],"into":[167],"joint":[169],"optimization":[170],"clustering.":[176],"Extensive":[177],"experiments":[178],"show":[179],"proposed":[182],"achieves":[184],"lower":[186],"transfer":[187],"error":[188],"0.39":[190],"pixels":[191],"36.92%":[194],"runtime":[195],"reduction,":[196],"surpassing":[197],"state-of-the-art":[198],"methods.":[199],"The":[200],"code":[201],"available":[203],"at":[204],"https://github.com/YWY-Vivian/AGASAC.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}