{"id":"https://openalex.org/W4293518656","doi":"https://doi.org/10.1109/icme52920.2022.9859973","title":"Change Detection Converter: Using Semantic Segmantation Models to Tackle Change Detection Task","display_name":"Change Detection Converter: Using Semantic Segmantation Models to Tackle Change Detection Task","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293518656","doi":"https://doi.org/10.1109/icme52920.2022.9859973"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859973","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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 Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100663435","display_name":"Ding Chen","orcid":"https://orcid.org/0000-0002-8647-2425"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ding Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077682130","display_name":"Bowei Ye","orcid":"https://orcid.org/0009-0000-7965-4987"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowei Ye","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079144156","display_name":"Zhicheng Zhao","orcid":"https://orcid.org/0000-0002-2761-7399"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Zhao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101962378","display_name":"Feihong Wang","orcid":"https://orcid.org/0009-0008-9806-2280"},"institutions":[{"id":"https://openalex.org/I4210100254","display_name":"Clinical Insights","ror":"https://ror.org/0168sna55","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210100254"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feihong Wang","raw_affiliation_strings":["Beijing Insights Value Technology CO., LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Insights Value Technology CO., LTD","institution_ids":["https://openalex.org/I4210100254"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100857013","display_name":"Weida Xu","orcid":"https://orcid.org/0009-0002-4975-456X"},"institutions":[{"id":"https://openalex.org/I4210100254","display_name":"Clinical Insights","ror":"https://ror.org/0168sna55","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210100254"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weida Xu","raw_affiliation_strings":["Beijing Insights Value Technology CO., LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Insights Value Technology CO., LTD","institution_ids":["https://openalex.org/I4210100254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100564548","display_name":"Wenjun Yin","orcid":"https://orcid.org/0000-0003-0200-3056"},"institutions":[{"id":"https://openalex.org/I4210100254","display_name":"Clinical Insights","ror":"https://ror.org/0168sna55","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210100254"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenjun Yin","raw_affiliation_strings":["Beijing Insights Value Technology CO., LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Insights Value Technology CO., LTD","institution_ids":["https://openalex.org/I4210100254"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.8845077157020569},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8358702659606934},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.6599217653274536},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6254492998123169},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6029758453369141},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5428166389465332},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49244222044944763},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.48568710684776306},{"id":"https://openalex.org/keywords/semantic-change","display_name":"Semantic change","score":0.4804475009441376},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.465005487203598},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4416375160217285},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42722660303115845},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4144079089164734}],"concepts":[{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.8845077157020569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8358702659606934},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.6599217653274536},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6254492998123169},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6029758453369141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5428166389465332},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49244222044944763},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.48568710684776306},{"id":"https://openalex.org/C36391188","wikidata":"https://www.wikidata.org/wiki/Q1939117","display_name":"Semantic change","level":2,"score":0.4804475009441376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.465005487203598},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4416375160217285},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42722660303115845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4144079089164734},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"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/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859973","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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 Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2285010073","display_name":null,"funder_award_id":"2020YFB2104604","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2560023338","https://openalex.org/W2761352265","https://openalex.org/W2891248708","https://openalex.org/W2908320224","https://openalex.org/W2913318911","https://openalex.org/W2948648905","https://openalex.org/W2964309882","https://openalex.org/W2983446232","https://openalex.org/W3009942016","https://openalex.org/W3027225766","https://openalex.org/W3033529678","https://openalex.org/W3036453075","https://openalex.org/W3094502228","https://openalex.org/W3130754787","https://openalex.org/W3138516171","https://openalex.org/W3163207600","https://openalex.org/W3170544306","https://openalex.org/W3177155650","https://openalex.org/W3179869055","https://openalex.org/W3186032668","https://openalex.org/W3194968429","https://openalex.org/W3211490618","https://openalex.org/W4287241952","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6748481559","https://openalex.org/W6779163297","https://openalex.org/W6784333009","https://openalex.org/W6790731625","https://openalex.org/W6792950394","https://openalex.org/W6799693294"],"related_works":["https://openalex.org/W2811390910","https://openalex.org/W2146076056","https://openalex.org/W2144059113","https://openalex.org/W3003836766","https://openalex.org/W1964120219","https://openalex.org/W2000165426","https://openalex.org/W2385132419","https://openalex.org/W2772780115","https://openalex.org/W2114557664","https://openalex.org/W2546942002"],"abstract_inverted_index":{"In":[0,44,77],"recent":[1],"years,":[2],"change":[3,51,74],"detection":[4,52,75],"(CD)":[5],"in":[6,96],"remote":[7],"sensing":[8],"images":[9],"has":[10,39],"achieved":[11],"a":[12,23,49,79],"huge":[13],"success":[14],"by":[15],"using":[16],"deep":[17],"learning,":[18],"and":[19,66,114,124],"it":[20],"is":[21,87],"essentially":[22],"subtask":[24],"of":[25,29,37],"semantic":[26,63],"segmentation.":[27],"Both":[28],"them":[30,38],"are":[31],"tightly":[32],"related.":[33],"However,":[34],"the":[35,61,92,107],"complementarity":[36],"not":[40],"been":[41],"fully":[42],"explored.":[43],"this":[45],"paper,":[46],"we":[47],"propose":[48],"new":[50,70,80],"converter":[53],"(CDC),":[54],"which":[55,89],"can":[56,90,125],"be":[57,67],"easily":[58],"inserted":[59],"into":[60,69],"existing":[62],"segmentation":[64],"networks":[65,71],"transformed":[68],"suitable":[72],"for":[73],"task.":[76],"addition,":[78],"task-specific":[81],"data":[82],"augmentation":[83],"method":[84,109],"named":[85],"Crossover":[86],"proposed,":[88],"enhance":[91],"feature":[93],"representation":[94],"ability":[95],"sequence-form":[97],"features":[98],"without":[99],"any":[100],"additional":[101],"cost.":[102],"Extensive":[103],"experiments":[104],"demonstrate":[105],"that":[106],"proposed":[108],"obtains":[110],"significantly":[111],"better":[112],"performance":[113,128],"more":[115],"efficiency":[116],"than":[117,130],"previous":[118],"methods":[119],"on":[120],"two":[121],"public":[122],"datasets,":[123],"achieve":[126],"continuous":[127],"improvement":[129],"other":[131],"complex-designed":[132],"solutions.":[133]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
