{"id":"https://openalex.org/W3009483577","doi":"https://doi.org/10.1109/wacv45572.2020.9093287","title":"Multi-Scale Adversarial Cross-Domain Detection with Robust Discriminative Learning","display_name":"Multi-Scale Adversarial Cross-Domain Detection with Robust Discriminative Learning","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3009483577","doi":"https://doi.org/10.1109/wacv45572.2020.9093287","mag":"3009483577"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5112533820","display_name":"YoungSun Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"YoungSun Pan","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085704922","display_name":"J. Andy","orcid":"https://orcid.org/0000-0002-0165-8416"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Andy J Ma","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074618663","display_name":"Yuan Gao","orcid":"https://orcid.org/0000-0001-7007-9605"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Gao","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376310","display_name":"Jinpeng Wang","orcid":"https://orcid.org/0000-0001-6127-9146"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JinPeng Wang","raw_affiliation_strings":["School of Electronics and Information Technology, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Technology, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101545962","display_name":"Yiqi Lin","orcid":"https://orcid.org/0000-0002-8208-3705"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqi Lin","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1313","last_page":"1321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8516141176223755},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7836418151855469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.709509015083313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.554224967956543},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5422000885009766},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5071094036102295},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3734220862388611},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36941903829574585},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10373958945274353},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05878251791000366}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8516141176223755},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7836418151855469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.709509015083313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.554224967956543},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5422000885009766},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5071094036102295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3734220862388611},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36941903829574585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10373958945274353},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05878251791000366},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1032927584","https://openalex.org/W1536680647","https://openalex.org/W1565327149","https://openalex.org/W1594039573","https://openalex.org/W1686810756","https://openalex.org/W1731081199","https://openalex.org/W1861492603","https://openalex.org/W1932624639","https://openalex.org/W2031489346","https://openalex.org/W2099471712","https://openalex.org/W2102605133","https://openalex.org/W2128053425","https://openalex.org/W2149466042","https://openalex.org/W2165698076","https://openalex.org/W2248269543","https://openalex.org/W2279034837","https://openalex.org/W2340897893","https://openalex.org/W2528963632","https://openalex.org/W2565639579","https://openalex.org/W2592691248","https://openalex.org/W2593768305","https://openalex.org/W2613718673","https://openalex.org/W2739748921","https://openalex.org/W2748021867","https://openalex.org/W2795155917","https://openalex.org/W2798991696","https://openalex.org/W2904706552","https://openalex.org/W2951292523","https://openalex.org/W2953070460","https://openalex.org/W2962859295","https://openalex.org/W2962879692","https://openalex.org/W2963037989","https://openalex.org/W2963149653","https://openalex.org/W2963150697","https://openalex.org/W2963275094","https://openalex.org/W2963351448","https://openalex.org/W2963730616","https://openalex.org/W2964115968","https://openalex.org/W2964278684","https://openalex.org/W2968634921","https://openalex.org/W2979548969","https://openalex.org/W2982593143","https://openalex.org/W3121281282","https://openalex.org/W4295521014","https://openalex.org/W4320013936","https://openalex.org/W6620707391","https://openalex.org/W6633949838","https://openalex.org/W6637373629","https://openalex.org/W6637618735","https://openalex.org/W6639102338","https://openalex.org/W6640759364","https://openalex.org/W6681637710","https://openalex.org/W6684207430","https://openalex.org/W6695692224","https://openalex.org/W6713955831","https://openalex.org/W6733814495","https://openalex.org/W6735913928","https://openalex.org/W6741832134","https://openalex.org/W6750109254","https://openalex.org/W6764266060"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W2965546495","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534"],"abstract_inverted_index":{"Domain":[0],"shift":[1,38,41,44],"practically":[2],"exists":[3],"in":[4,75],"almost":[5],"all":[6],"computer":[7],"vision":[8],"tasks":[9],"including":[10],"object":[11,32,191],"detection,":[12,33],"caused":[13],"by":[14,133],"which":[15,61],"the":[16,66,72,91,111,125,147,188,194],"performance":[17,84,92],"drops":[18],"evidently.":[19],"Most":[20],"existing":[21,34],"methods":[22,35],"for":[23,29,163,190],"domain":[24,37,113,198],"adaptation":[25,199],"are":[26,58],"specially":[27],"designed":[28],"classification.":[30],"For":[31],"separate":[36],"into":[39],"image-level":[40,47,112],"and":[42,45,49,141,160,176],"instance-level":[43,50],"align":[46],"feature":[48,51,136],"respectively.":[52],"However,":[53],"we":[54],"find":[55],"that":[56,153,184],"there":[57],"two":[59],"problems":[60],"remain":[62],"unsolved":[63],"yet.":[64],"First,":[65],"scale":[67],"of":[68,93,127,149,196],"objects":[69],"is":[70],"not":[71,86],"same":[73],"even":[74],"an":[76],"image.":[77],"Second,":[78],"negative":[79,128],"transfer":[80],"can":[81],"affect":[82],"model":[83,169],"if":[85],"handled":[87],"properly.":[88],"We":[89,166],"improve":[90],"cross-domain":[94,164],"detection":[95,192],"from":[96,138],"three":[97],"perspectives:":[98],"1)":[99],"using":[100],"multiple":[101],"dilated":[102],"convolution":[103],"kernels":[104],"with":[105,120,170],"different":[106],"dilation":[107],"rate":[108],"to":[109,123,146],"reduce":[110],"discrepancy;":[114],"2)":[115],"removing":[116],"images":[117],"or":[118],"instances":[119],"low":[121],"transferability":[122],"weaken":[124],"influence":[126],"transfer;":[129],"3)":[130],"diversifying":[131],"distributions":[132],"keeping":[134],"instances'":[135],"away":[137],"each":[139,150],"other,":[140],"then":[142],"pull":[143],"them":[144],"closer":[145],"center":[148],"category,":[151],"so":[152],"make":[154],"source":[155],"samples":[156],"distribution":[157],"more":[158,161],"dispersed":[159],"robust":[162],"detection.":[165],"test":[167],"our":[168,185],"Cityscapes":[171],"[5],":[172],"Foggy":[173],"Cityscape":[174],"[30]":[175],"SIM":[177],"10K":[178],"[18]":[179],"datasets,":[180],"experimental":[181],"results":[182],"show":[183],"method":[186],"outperforms":[187],"state-of-the-art":[189],"under":[193],"setting":[195],"unsupervised":[197],"(UDA).":[200]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}