{"id":"https://openalex.org/W3206381516","doi":"https://doi.org/10.1145/3474085.3475199","title":"Robust Shadow Detection by Exploring Effective Shadow Contexts","display_name":"Robust Shadow Detection by Exploring Effective Shadow Contexts","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3206381516","doi":"https://doi.org/10.1145/3474085.3475199","mag":"3206381516"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475199","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","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/A5033676752","display_name":"Xianyong Fang","orcid":"https://orcid.org/0000-0002-6045-8430"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianyong Fang","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017333049","display_name":"Xiaohao He","orcid":null},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohao He","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101802052","display_name":"Linbo Wang","orcid":"https://orcid.org/0009-0000-9331-396X"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linbo Wang","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023184215","display_name":"Jianbing Shen","orcid":"https://orcid.org/0000-0002-4109-8353"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jianbing Shen","raw_affiliation_strings":["University of Macau, Macau, China"],"affiliations":[{"raw_affiliation_string":"University of Macau, Macau, China","institution_ids":["https://openalex.org/I204512498"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033676752"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":null,"apc_paid":null,"fwci":1.5502,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.85277337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2927","last_page":"2935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9973000288009644,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9914000034332275,"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":0.804963231086731},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7014626860618591},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6886188983917236},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6800001859664917},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6613327264785767},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5636674165725708},{"id":"https://openalex.org/keywords/shadow","display_name":"Shadow (psychology)","score":0.552384078502655},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5322653651237488},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5015411376953125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4695449769496918},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45470860600471497},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.447493314743042},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4351496398448944}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.804963231086731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7014626860618591},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6886188983917236},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6800001859664917},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6613327264785767},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5636674165725708},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.552384078502655},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5322653651237488},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5015411376953125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4695449769496918},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45470860600471497},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.447493314743042},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4351496398448944},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474085.3475199","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1566291503","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1965740104","https://openalex.org/W1967913888","https://openalex.org/W1998270967","https://openalex.org/W2027850463","https://openalex.org/W2069088802","https://openalex.org/W2073839959","https://openalex.org/W2097117768","https://openalex.org/W2099471712","https://openalex.org/W2113449189","https://openalex.org/W2122514671","https://openalex.org/W2147558395","https://openalex.org/W2149550213","https://openalex.org/W2157030221","https://openalex.org/W2161236525","https://openalex.org/W2166502676","https://openalex.org/W2183341477","https://openalex.org/W2206865673","https://openalex.org/W2519623608","https://openalex.org/W2531409750","https://openalex.org/W2544806203","https://openalex.org/W2549139847","https://openalex.org/W2605126331","https://openalex.org/W2744263836","https://openalex.org/W2771617895","https://openalex.org/W2777654136","https://openalex.org/W2780708736","https://openalex.org/W2801375052","https://openalex.org/W2808548587","https://openalex.org/W2884217841","https://openalex.org/W2895126795","https://openalex.org/W2948670693","https://openalex.org/W2949117887","https://openalex.org/W2952671211","https://openalex.org/W2963014378","https://openalex.org/W2963968420","https://openalex.org/W2990984982","https://openalex.org/W3004335748","https://openalex.org/W3034627419","https://openalex.org/W3035462037","https://openalex.org/W3102699694","https://openalex.org/W3115300452","https://openalex.org/W3120736405","https://openalex.org/W3175990321"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W2116862786","https://openalex.org/W2963610131"],"abstract_inverted_index":{"Effective":[0],"contexts":[1,30,65],"for":[2,31],"separating":[3],"shadows":[4],"from":[5,50],"non-shadow":[6],"objects":[7],"can":[8,146],"appear":[9],"in":[10,171],"different":[11,15],"scales":[12],"due":[13],"to":[14,27,91,99,118],"object":[16,64],"sizes.":[17],"This":[18],"paper":[19],"introduces":[20],"a":[21,72],"new":[22],"module,":[23],"Effective-Context":[24],"Augmentation":[25],"(ECA),":[26],"utilize":[28],"these":[29],"robust":[32,59],"shadow":[33,77],"detection":[34,78],"with":[35,62,111],"deep":[36,40],"structures.":[37],"Taking":[38],"regular":[39],"features":[41,49,55,60,134],"as":[42,83],"global":[43],"references,":[44],"ECA":[45,81],"enhances":[46],"the":[47,51,84,89,97,100,104,107,124,132,143,164],"discriminative":[48],"parallelly":[52],"computed":[53],"fine-scale":[54],"and,":[56],"therefore,":[57],"obtains":[58],"embedded":[61],"effective":[63],"by":[66,127],"boosting":[67],"them.":[68],"We":[69],"further":[70],"propose":[71],"novel":[73],"encoder-decoder":[74],"style":[75],"of":[76,88,103],"method":[79,145,153],"where":[80],"acts":[82],"main":[85],"building":[86],"block":[87],"encoder":[90],"extract":[92],"strong":[93],"feature":[94],"representations":[95],"and":[96,120,157,161,167],"guidance":[98],"classification":[101],"process":[102],"decoder.":[105],"Moreover,":[106],"networks":[108],"are":[109],"optimized":[110],"only":[112],"one":[113],"loss,":[114],"which":[115],"is":[116],"easy":[117],"train":[119],"does":[121],"not":[122],"have":[123],"instability":[125],"caused":[126],"extra":[128],"losses":[129],"superimposed":[130],"on":[131,163],"intermediate":[133],"among":[135],"existing":[136],"popular":[137],"studies.":[138],"Experimental":[139],"results":[140],"show":[141],"that":[142],"proposed":[144],"effectively":[147],"eliminate":[148],"fake":[149],"detections.":[150],"Especially,":[151],"our":[152],"outperforms":[154],"state-of-the-arts":[155],"methods":[156],"improves":[158],"over":[159],"$13.97%$":[160],"$34.67%$":[162],"challenging":[165],"SBU":[166],"UCF":[168],"datasets":[169],"respectively":[170],"balance":[172],"error":[173],"rate.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
