{"id":"https://openalex.org/W4320024241","doi":"https://doi.org/10.1109/bigdata55660.2022.10020343","title":"MS<sup>2</sup>A: A Multi-Scale Spatial Aggregation Framework for Visual Place Recognition","display_name":"MS<sup>2</sup>A: A Multi-Scale Spatial Aggregation Framework for Visual Place Recognition","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024241","doi":"https://doi.org/10.1109/bigdata55660.2022.10020343"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020343","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020343","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5088533461","display_name":"Cui Ge","orcid":"https://orcid.org/0000-0002-6182-6856"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ge Cui","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100757829","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8598-0831"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100397026","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0003-1991-119X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088533461"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19877371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4173","last_page":"4180"},"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9980000257492065,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9977999925613403,"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/semantics","display_name":"Semantics (computer science)","score":0.666069746017456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6416524052619934},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5615054965019226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5523756146430969},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49636465311050415},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46927228569984436},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42390233278274536},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4169039726257324},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4127490520477295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40555840730667114},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3997443616390228},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38695642352104187},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32906800508499146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2249090075492859},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.15555250644683838},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.11371216177940369},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07049894332885742}],"concepts":[{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.666069746017456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6416524052619934},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5615054965019226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5523756146430969},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49636465311050415},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46927228569984436},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42390233278274536},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4169039726257324},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4127490520477295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40555840730667114},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3997443616390228},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38695642352104187},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32906800508499146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2249090075492859},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.15555250644683838},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.11371216177940369},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07049894332885742},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020343","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020343","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1980867644","https://openalex.org/W2012592962","https://openalex.org/W2013270301","https://openalex.org/W2046166954","https://openalex.org/W2073761981","https://openalex.org/W2099253838","https://openalex.org/W2105516263","https://openalex.org/W2109255472","https://openalex.org/W2117228865","https://openalex.org/W2119605622","https://openalex.org/W2141362318","https://openalex.org/W2143238378","https://openalex.org/W2162915993","https://openalex.org/W2163704618","https://openalex.org/W2740418457","https://openalex.org/W2885052112","https://openalex.org/W2940791172","https://openalex.org/W2951019013","https://openalex.org/W2963507712","https://openalex.org/W2963708168","https://openalex.org/W2963713713","https://openalex.org/W2984478347","https://openalex.org/W2990519439","https://openalex.org/W3001152281","https://openalex.org/W3008897023","https://openalex.org/W3090189425","https://openalex.org/W3090895685","https://openalex.org/W3095367607","https://openalex.org/W3110076758","https://openalex.org/W3136246337","https://openalex.org/W3173736705","https://openalex.org/W3179808469","https://openalex.org/W3204429916","https://openalex.org/W3206622090","https://openalex.org/W3207528728","https://openalex.org/W4242177601","https://openalex.org/W4287664603","https://openalex.org/W4312597490","https://openalex.org/W6675402254","https://openalex.org/W6774308738","https://openalex.org/W6783184797"],"related_works":["https://openalex.org/W1846541313","https://openalex.org/W2015538044","https://openalex.org/W3214915308","https://openalex.org/W2052253960","https://openalex.org/W2480412556","https://openalex.org/W2382607599","https://openalex.org/W2016461833","https://openalex.org/W3097853387","https://openalex.org/W1568866260","https://openalex.org/W3197541072"],"abstract_inverted_index":{"Visual":[0],"Place":[1],"Recognition":[2],"(VPR)":[3],"is":[4,72],"the":[5,9,18,33,40,50,76,123,137,151,163],"task":[6],"of":[7,11,78,126,136,139,153,165],"estimating":[8],"location":[10,77],"a":[12,29,69,99],"query":[13],"image":[14,128],"by":[15,44,55],"searching":[16],"for":[17,149],"most":[19],"similar":[20,83],"reference":[21],"image.":[22],"The":[23,36],"ever-changing":[24],"appearance":[25],"and":[26,65,109,131,141,156],"viewpoint":[27],"pose":[28],"significant":[30],"challenge":[31],"to":[32,74,114,160],"VPR":[34],"task.":[35],"mainstream":[37],"methods":[38],"estimate":[39],"distance":[41],"between":[42],"images":[43,79],"comparing":[45],"their":[46],"compact":[47],"representations.":[48,116],"However,":[49],"representations":[51],"are":[52,81],"usually":[53],"generated":[54],"aggregating":[56],"all":[57],"deep":[58],"local":[59,90],"features,":[60],"which":[61,106,121],"lose":[62],"detailed":[63,108],"semantics":[64,91],"spatial":[66,110],"relationships.":[67],"As":[68],"result,":[70],"it":[71],"hard":[73],"distinguish":[75],"that":[80,176],"highly":[82],"in":[84,89],"global":[85],"semantics,":[86,120],"but":[87],"differ":[88],"or":[92],"viewpoints.":[93],"To":[94],"this":[95],"end,":[96],"we":[97,145],"proposed":[98],"novel":[100],"Multi-Scale":[101],"Spatial":[102],"Aggregation":[103],"(MS<sup>2</sup>A)":[104],"framework,":[105],"takes":[107],"information":[111],"into":[112],"account":[113],"generate":[115],"MS<sup>2</sup>A":[117,166,177],"fuses":[118],"multi-scale":[119,154],"calculates":[122],"feature":[124],"map":[125],"an":[127,147,157],"only":[129],"once":[130],"requires":[132],"no":[133],"fine-tuning,":[134],"regardless":[135],"number":[138],"scales":[140],"regions.":[142],"In":[143],"addition,":[144],"introduced":[146],"approach":[148],"quantifying":[150],"choice":[152],"parameters":[155],"efficient":[158],"method":[159],"further":[161],"reduce":[162],"dimension":[164],"representation.":[167],"Experimental":[168],"results":[169],"on":[170],"three":[171],"large-scale":[172],"benchmark":[173],"datasets":[174],"demonstrate":[175],"can":[178],"achieve":[179],"state-of-the-art":[180],"performance.":[181]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}