{"id":"https://openalex.org/W4415708441","doi":"https://doi.org/10.1109/icme59968.2025.11210175","title":"Frequency-guided Camouflaged Object Detection with Perceptual Enhancement and Dynamic Balance","display_name":"Frequency-guided Camouflaged Object Detection with Perceptual Enhancement and Dynamic Balance","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708441","doi":"https://doi.org/10.1109/icme59968.2025.11210175"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5022853722","display_name":"Yuetong Li","orcid":"https://orcid.org/0009-0005-2353-4886"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuetong Li","raw_affiliation_strings":["Shanghai Institute of Technology,College of Sciences,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,College of Sciences,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101361471","display_name":"Yilin Zhao","orcid":"https://orcid.org/0000-0001-5324-332X"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilin Zhao","raw_affiliation_strings":["Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054054170","display_name":"Qing Zhang","orcid":"https://orcid.org/0000-0003-1947-2406"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Zhang","raw_affiliation_strings":["Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110493142","display_name":"Qiangqiang Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I53592917","display_name":"Jiangxi Normal University","ror":"https://ror.org/05nkgk822","country_code":"CN","type":"education","lineage":["https://openalex.org/I53592917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangqiang Zhou","raw_affiliation_strings":["Jiangxi Normal University,School of Software,Nanchang,China"],"affiliations":[{"raw_affiliation_string":"Jiangxi Normal University,School of Software,Nanchang,China","institution_ids":["https://openalex.org/I53592917"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013600952","display_name":"Yanjiao Shi","orcid":"https://orcid.org/0000-0001-9689-4165"},"institutions":[{"id":"https://openalex.org/I67001856","display_name":"Shanghai Institute of Technology","ror":"https://ror.org/00fjzqj15","country_code":"CN","type":"education","lineage":["https://openalex.org/I67001856"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjiao Shi","raw_affiliation_strings":["Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Institute of Technology,Faculty of Intelligence Technology,Shanghai,China","institution_ids":["https://openalex.org/I67001856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5022853722"],"corresponding_institution_ids":["https://openalex.org/I67001856"],"apc_list":null,"apc_paid":null,"fwci":1.1366,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8378146,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9502999782562256,"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":0.9502999782562256,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0052999998442828655,"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":0.0044999998062849045,"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/object-detection","display_name":"Object detection","score":0.6396999955177307},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6265000104904175},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6243000030517578},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5605000257492065},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.48840001225471497},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4544999897480011},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3443000018596649}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7864000201225281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6970999836921692},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6396999955177307},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6243000030517578},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5774999856948853},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5605000257492065},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C166151169","wikidata":"https://www.wikidata.org/wiki/Q16946937","display_name":"Dynamic balance","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309612","display_name":"Natural Science Foundation of Shanghai","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1968773971","https://openalex.org/W1982075130","https://openalex.org/W1994922096","https://openalex.org/W2051624325","https://openalex.org/W2770189357","https://openalex.org/W2943545929","https://openalex.org/W2963529609","https://openalex.org/W2963604034","https://openalex.org/W2963868681","https://openalex.org/W2998449272","https://openalex.org/W3027763298","https://openalex.org/W3034684132","https://openalex.org/W3092344722","https://openalex.org/W3164098653","https://openalex.org/W3168112135","https://openalex.org/W3173782971","https://openalex.org/W3176152216","https://openalex.org/W4386075673","https://openalex.org/W4386075678","https://openalex.org/W4386076039","https://openalex.org/W4387969595","https://openalex.org/W4390492164","https://openalex.org/W4391407087","https://openalex.org/W4401328675","https://openalex.org/W4402727814","https://openalex.org/W4402727868","https://openalex.org/W4403780636","https://openalex.org/W4405141126","https://openalex.org/W4407146886"],"related_works":[],"abstract_inverted_index":{"Camouflaged":[0],"object":[1],"detection":[2],"(COD)":[3],"aims":[4],"to":[5,45,48,52,69,86,103,120],"segment":[6],"concealed":[7],"objects":[8,27,93],"from":[9,82],"their":[10],"surroundings.":[11],"However,":[12],"most":[13],"existing":[14],"frequency-domain":[15,50,83],"methods":[16],"rely":[17],"on":[18,72,135],"simplistic":[19],"fusion":[20],"schemes":[21],"with":[22,28,130],"RGB":[23,74],"features":[24,75,109,123],"especially":[25],"for":[26],"severe":[29],"occlusion,":[30],"varying":[31,95],"scales,":[32,96],"or":[33],"ambiguous":[34],"appearance.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39],"propose":[40],"a":[41],"frequency-guided":[42],"COD":[43],"network":[44],"explore":[46],"how":[47],"utilize":[49],"information":[51],"enhance":[53,87],"the":[54,63,88,97,114,140],"learning":[55],"and":[56,78,107,142],"presentation":[57],"ability":[58],"of":[59,94,144],"RGB-domain":[60],"features.":[61],"Specifically,":[62],"frequency-aware":[64],"query":[65],"module":[66,100,117],"is":[67,101,118,150],"designed":[68],"selectively":[70],"focus":[71],"essential":[73],"by":[76,124],"capturing":[77],"integrating":[79],"long-term":[80],"dependencies":[81],"information.":[84,133],"Additionally,":[85],"model\u2019s":[89],"capability":[90],"in":[91],"identify":[92],"perception":[98],"enhancement":[99],"proposed":[102],"sufficiently":[104],"integrate":[105],"related":[106],"complementary":[108],"across":[110],"adjacent":[111],"levels.":[112],"Finally,":[113],"dynamic":[115],"balance":[116],"introduced":[119],"aggregate":[121],"multi-level":[122],"adaptively":[125],"balancing":[126],"global":[127],"contextual":[128],"knowledge":[129],"local":[131],"details":[132],"Experiments":[134],"three":[136],"widely-used":[137],"benchmarks":[138],"demonstrate":[139],"effectiveness":[141],"superiority":[143],"our":[145],"network.":[146],"The":[147],"source":[148],"code":[149],"available":[151],"at":[152],"https://github.com/iuueong/FPDNet.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-30T00:00:00"}
