{"id":"https://openalex.org/W7143463309","doi":"https://doi.org/10.48550/arxiv.2603.26109","title":"SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection","display_name":"SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7143463309","doi":"https://doi.org/10.48550/arxiv.2603.26109"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26109","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130909481","display_name":"Jiaming Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liang, Jiaming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130999546","display_name":"Yifeng Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Yifeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130483094","display_name":"Chunlin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chunlin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126831365","display_name":"Weihua Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Weihua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130931406","display_name":"Bingye Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Bingye","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125530564","display_name":"Qiwei Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Qiwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130989078","display_name":"Boyang Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Boyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034208042","display_name":"Xiaochun Mai","orcid":"https://orcid.org/0000-0002-0756-9924"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mai, Xiaochun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130975703","display_name":"Qiang Nie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Qiang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5130909481"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.8313000202178955,"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":0.8313000202178955,"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":0.03689999878406525,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.011800000444054604,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7006999850273132},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.692799985408783},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6603999733924866},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.600600004196167},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42250001430511475},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.40230000019073486},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.3840000033378601}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7993000149726868},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7616999745368958},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7006999850273132},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.692799985408783},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6603999733924866},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6391000151634216},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.600600004196167},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3840000033378601},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C2778949103","wikidata":"https://www.wikidata.org/wiki/Q600717","display_name":"Staring","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.26109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26109","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7476689219474792,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-vocabulary":[0],"object":[1,78,92,100],"detection":[2,101],"(OVOD)":[3],"aims":[4,182],"to":[5,45,85,150,164,183,188],"detect":[6],"known":[7],"and":[8,47,57,127,177],"unknown":[9],"objects":[10,49,56,160,191],"in":[11],"the":[12,21,41,51,55,58,86,115,152,155,185,195,199,208],"open":[13],"world":[14],"by":[15,73,119],"leveraging":[16],"text":[17],"prompts.":[18],"Benefiting":[19],"from":[20,192],"emergence":[22],"of":[23,54,89,158,166,205],"large-scale":[24,99],"vision--language":[25],"pre-trained":[26,97],"models,":[27,122],"OVOD":[28],"has":[29],"demonstrated":[30],"strong":[31],"zero-shot":[32,111],"generalization":[33,112],"capabilities.":[34],"However,":[35],"when":[36],"dealing":[37],"with":[38,80],"camouflaged":[39,77,91,159,190],"objects,":[40],"detector":[42],"often":[43],"fails":[44],"distinguish":[46],"localize":[48],"because":[50],"visual":[52,156],"features":[53,157],"background":[59],"are":[60,161],"highly":[61,162],"similar.":[62],"To":[63,131],"bridge":[64],"this":[65],"gap,":[66],"we":[67,94,135,170],"construct":[68],"a":[69,137,172],"benchmark":[70],"named":[71],"OVCOD-D":[72,209],"augmenting":[74],"carefully":[75],"selected":[76],"images":[79],"fine-grained":[81],"textual":[82,147],"descriptions.":[83],"Due":[84],"limited":[87],"scale":[88],"available":[90],"datasets,":[93],"adopt":[95],"detectors":[96],"on":[98,207],"datasets":[102],"as":[103,107],"our":[104],"baseline":[105],"methods,":[106],"they":[108],"possess":[109],"stronger":[110],"ability.":[113],"In":[114],"specificity-aware":[116],"sub-descriptions":[117],"generated":[118],"multimodal":[120],"large":[121],"there":[123],"still":[124],"exist":[125],"confusing":[126],"overly":[128],"decorative":[129],"modifiers.":[130],"mitigate":[132],"such":[133],"interference,":[134],"design":[136],"sub-description":[138],"principal":[139],"component":[140],"contrastive":[141],"fusion":[142],"strategy":[143],"that":[144,154],"reduces":[145],"noisy":[146],"components.":[148],"Furthermore,":[149],"address":[151],"challenge":[153],"similar":[163],"those":[165],"their":[167],"surrounding":[168],"environment,":[169],"propose":[171],"specificity-guided":[173],"regional":[174],"weak":[175],"alignment":[176],"dynamic":[178],"focusing":[179],"method,":[180],"which":[181],"strengthen":[184],"detector's":[186],"ability":[187],"discriminate":[189],"background.":[193],"Under":[194],"open-set":[196],"evaluation":[197],"setting,":[198],"proposed":[200],"method":[201],"achieves":[202],"an":[203],"AP":[204],"56.4":[206],"benchmark.":[210]},"counts_by_year":[],"updated_date":"2026-03-31T06:07:48.031334","created_date":"2026-03-31T00:00:00"}