{"id":"https://openalex.org/W4414321224","doi":"https://doi.org/10.1145/3768584","title":"Boosting Foreground-Background Disentanglement for Camouflaged Object Detection","display_name":"Boosting Foreground-Background Disentanglement for Camouflaged Object Detection","publication_year":2025,"publication_date":"2025-09-18","ids":{"openalex":"https://openalex.org/W4414321224","doi":"https://doi.org/10.1145/3768584"},"language":"en","primary_location":{"id":"doi:10.1145/3768584","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768584","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-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/A5068315404","display_name":"Jiesheng Wu","orcid":"https://orcid.org/0000-0002-6941-3300"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiesheng Wu","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China","School of Computer and Information, Anhui Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-6941-3300","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]},{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, China","institution_ids":["https://openalex.org/I4472751"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119665083","display_name":"Fangwei Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangwei Hao","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China","College of Artificial Intelligence, Nankai University, China"],"raw_orcid":"https://orcid.org/0009-0009-7320-9423","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042066408","display_name":"Jing Xu","orcid":"https://orcid.org/0000-0001-8532-2241"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xu","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China","College of Artificial Intelligence, Nankai University, China"],"raw_orcid":"https://orcid.org/0000-0001-8532-2241","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]},{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7655,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75150242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"21","issue":"12","first_page":"1","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T11019","display_name":"Image Enhancement Techniques","score":0.9975000023841858,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.991599977016449,"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/boosting","display_name":"Boosting (machine learning)","score":0.7706000208854675},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6563000082969666},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5965999960899353},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4659999907016754},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.444599986076355},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.439300000667572},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.421099990606308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.864300012588501},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7706000208854675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6579999923706055},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6563000082969666},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5965999960899353},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4659999907016754},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36800000071525574},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32760000228881836},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.26499998569488525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3768584","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3768584","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6157584034","display_name":null,"funder_award_id":"2408085MF169","funder_id":"https://openalex.org/F4320334897","funder_display_name":"Natural Science Foundation of Anhui Province"},{"id":"https://openalex.org/G7126356451","display_name":null,"funder_award_id":"52374155","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334897","display_name":"Natural Science Foundation of Anhui Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W1982075130","https://openalex.org/W1994922096","https://openalex.org/W2021088830","https://openalex.org/W2051624325","https://openalex.org/W2108598243","https://openalex.org/W2187089797","https://openalex.org/W2285968993","https://openalex.org/W2796975587","https://openalex.org/W2884436604","https://openalex.org/W2884585870","https://openalex.org/W2922509574","https://openalex.org/W2928165649","https://openalex.org/W2962858109","https://openalex.org/W2963112696","https://openalex.org/W2963868681","https://openalex.org/W2979515228","https://openalex.org/W2990984982","https://openalex.org/W2997286550","https://openalex.org/W2998449272","https://openalex.org/W3034684132","https://openalex.org/W3035422681","https://openalex.org/W3035524453","https://openalex.org/W3092344722","https://openalex.org/W3132401450","https://openalex.org/W3164098653","https://openalex.org/W3168112135","https://openalex.org/W3173782971","https://openalex.org/W3175515048","https://openalex.org/W3176152216","https://openalex.org/W3177634011","https://openalex.org/W3179443972","https://openalex.org/W3203700770","https://openalex.org/W3204995672","https://openalex.org/W3210073375","https://openalex.org/W4252395698","https://openalex.org/W4283724275","https://openalex.org/W4285259878","https://openalex.org/W4285310685","https://openalex.org/W4300011764","https://openalex.org/W4304098552","https://openalex.org/W4306830744","https://openalex.org/W4308180443","https://openalex.org/W4312258849","https://openalex.org/W4312349930","https://openalex.org/W4312880622","https://openalex.org/W4313023779","https://openalex.org/W4315490105","https://openalex.org/W4321021814","https://openalex.org/W4323891945","https://openalex.org/W4367146759","https://openalex.org/W4376467320","https://openalex.org/W4382119582","https://openalex.org/W4386232833","https://openalex.org/W4387010134","https://openalex.org/W4390492164","https://openalex.org/W4398226159","https://openalex.org/W4399880785","https://openalex.org/W4399923948","https://openalex.org/W4401163468","https://openalex.org/W4401328675","https://openalex.org/W4403792529","https://openalex.org/W4405754613","https://openalex.org/W4406457466","https://openalex.org/W4406520988"],"related_works":[],"abstract_inverted_index":{"In":[0],"nature,":[1],"certain":[2],"objects":[3],"exhibit":[4],"patterns":[5],"that":[6,23,60,157],"closely":[7],"resemble":[8],"their":[9],"backgrounds,":[10],"a":[11,39,54],"phenomenon":[12],"commonly":[13],"referred":[14],"to":[15,38,65,96],"as":[16],"Camouflaged":[17],"Object":[18],"Detection":[19],"(COD).":[20],"We":[21],"argue":[22],"existing":[24],"COD":[25,167],"approaches":[26],"often":[27],"suffer":[28],"from":[29],"insufficient":[30],"discriminability":[31],"for":[32,129,139,150],"these":[33],"objects,":[34],"which":[35,77],"we":[36,52,69,88,119],"attribute":[37],"lack":[40],"of":[41,44,82,102,108],"effective":[42],"disentangling":[43],"foreground":[45,83],"and":[46,84,114,143,161],"background":[47,85],"representations.":[48,86],"To":[49],"address":[50],"this,":[51],"propose":[53,120],"novel":[55],"Foreground-Background":[56,73,91],"Disentanglement":[57],"Network":[58],"(FBD-Net)":[59],"enhances":[61],"foreground-background":[62],"disentanglement":[63],"learning":[64,81],"improve":[66],"discriminability.":[67],"Specifically,":[68],"design":[70],"an":[71],"Edge-guided":[72],"Decoupling":[74],"(EFBD)":[75],"module,":[76],"facilitates":[78],"the":[79,90,99,103,111,115,124,134,144],"separated":[80],"Additionally,":[87],"introduce":[89],"Representation":[92],"Disentangling":[93],"Head":[94],"(DisHead)":[95],"further":[97],"boost":[98],"discriminative":[100],"power":[101],"model.":[104],"The":[105],"DisHead":[106],"consists":[107],"two":[109],"objectives:":[110],"Edge":[112],"Objective":[113],"FoBa":[116],"Objective.":[117],"Furthermore,":[118],"three":[121],"complementary":[122],"modules:":[123],"Context":[125],"Aggregation":[126],"Module":[127],"(CAM)":[128],"initial":[130],"coarse":[131],"object":[132],"detection,":[133],"Scale-Interaction":[135],"Enhanced":[136],"Pyramid":[137],"(SIEP)":[138],"multi-scale":[140],"information":[141],"extraction,":[142],"Cross-Stage":[145],"Adaptive":[146],"Fusion":[147],"(CSAF)":[148],"module":[149],"subtle":[151],"clue":[152],"accumulation.":[153],"Extensive":[154],"experiments":[155],"demonstrate":[156],"both":[158],"our":[159],"CNN-based":[160],"Transformer-based":[162],"FBD-Nets":[163],"outperform":[164],"26":[165],"state-of-the-art":[166],"methods":[168],"across":[169],"four":[170],"public":[171],"datasets.":[172],"Codes":[173],"will":[174],"be":[175],"released":[176],"on":[177],"https://github.com/TomorrowJW/FBD-Net-COD":[178],".":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
