{"id":"https://openalex.org/W4224903180","doi":"https://doi.org/10.1145/3512527.3531369","title":"Efficient Linear Attention for Fast and Accurate Keypoint Matching","display_name":"Efficient Linear Attention for Fast and Accurate Keypoint Matching","publication_year":2022,"publication_date":"2022-06-23","ids":{"openalex":"https://openalex.org/W4224903180","doi":"https://doi.org/10.1145/3512527.3531369"},"language":"en","primary_location":{"id":"doi:10.1145/3512527.3531369","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512527.3531369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.07731","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054073711","display_name":"Suwichaya Suwanwimolkul","orcid":"https://orcid.org/0000-0001-7369-9711"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Suwichaya Suwanwimolkul","raw_affiliation_strings":["KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061487218","display_name":"Satoshi Komorita","orcid":"https://orcid.org/0000-0003-1526-5514"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Komorita","raw_affiliation_strings":["KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210164495"],"apc_list":null,"apc_paid":null,"fwci":0.9244,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83303965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"330","last_page":"341"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9876000285148621,"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":0.6404197812080383},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5845986604690552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47419196367263794},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33024418354034424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22046110033988953},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18283095955848694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6404197812080383},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5845986604690552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47419196367263794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33024418354034424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22046110033988953},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18283095955848694}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3512527.3531369","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3512527.3531369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2204.07731","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.07731","pdf_url":"https://arxiv.org/pdf/2204.07731","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.07731","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.07731","pdf_url":"https://arxiv.org/pdf/2204.07731","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W1744214816","https://openalex.org/W2003447360","https://openalex.org/W2013603106","https://openalex.org/W2085261163","https://openalex.org/W2126080861","https://openalex.org/W2138562085","https://openalex.org/W2151103935","https://openalex.org/W2166820607","https://openalex.org/W2256099243","https://openalex.org/W2533007775","https://openalex.org/W2562686169","https://openalex.org/W2597507805","https://openalex.org/W2612690371","https://openalex.org/W2620629206","https://openalex.org/W2747550417","https://openalex.org/W2793477525","https://openalex.org/W2799132636","https://openalex.org/W2963143232","https://openalex.org/W2963760790","https://openalex.org/W3034275286","https://openalex.org/W3047057232","https://openalex.org/W3099546855","https://openalex.org/W3104213423","https://openalex.org/W3166285241","https://openalex.org/W3175295430","https://openalex.org/W3191460670","https://openalex.org/W3193951565"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Recently":[0],"Transformers":[1,18],"have":[2],"provided":[3],"state-of-the-art":[4],"performance":[5,105],"in":[6,94],"sparse":[7,66],"matching,":[8],"crucial":[9],"to":[10,22],"realize":[11],"high-performance":[12],"3D":[13],"vision":[14],"applications.":[15],"Yet,":[16],"these":[17],"lack":[19],"efficiency":[20],"due":[21],"the":[23,42,60,71,75,96,112],"quadratic":[24],"computational":[25,44],"complexity":[26],"of":[27,78],"their":[28],"attention":[29,40],"mechanism.":[30],"To":[31,68],"solve":[32],"this":[33],"problem,":[34],"we":[35,47,73],"employ":[36],"an":[37],"efficient":[38],"linear":[39,43],"for":[41],"complexity.":[45],"Then,":[46],"propose":[48,74],"a":[49],"new":[50],"attentional":[51],"aggregation":[52],"that":[53],"achieves":[54,103],"high":[55],"accuracy":[56],"by":[57],"aggregating":[58],"both":[59],"global":[61],"and":[62,81,87,118],"local":[63],"information":[64],"from":[65,99],"keypoints.":[67],"further":[69],"improve":[70],"efficiency,":[72],"joint":[76],"learning":[77,84],"feature":[79],"matching":[80,89,95],"description.":[82],"Our":[83,101],"enables":[85],"simpler":[86],"faster":[88],"than":[90],"Sinkhorn,":[91],"often":[92],"used":[93],"learned":[97],"descriptors":[98],"Transformers.":[100],"method":[102],"competitive":[104],"with":[106],"only":[107],"0.84M":[108],"learnable":[109],"parameters":[110],"against":[111],"bigger":[113],"SOTAs,":[114],"SuperGlue":[115],"(12M":[116],"parameters)":[117],"SGMNet":[119],"(30M":[120],"parameters),":[121],"on":[122],"three":[123],"benchmarks,":[124],"HPatch,":[125],"ETH,":[126],"Aachen":[127],"Day-Night.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
