{"id":"https://openalex.org/W7139927925","doi":"https://doi.org/10.48550/arxiv.2603.18541","title":"Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection","display_name":"Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139927925","doi":"https://doi.org/10.48550/arxiv.2603.18541"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18541","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.18541","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130252934","display_name":"Yongwei Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yongwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130219125","display_name":"Yixiong Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Yixiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130235154","display_name":"Yuhua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130245323","display_name":"Ruixuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5782999992370605,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5782999992370605,"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":0.2231999933719635,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.08169999718666077,"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":0.6098999977111816},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5946999788284302},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.510200023651123},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4503999948501587},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43799999356269836},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.43470001220703125},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.43459999561309814},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39430001378059387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7408000230789185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6601999998092651},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6098999977111816},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5946999788284302},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5827999711036682},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.510200023651123},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4503999948501587},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.3605000078678131},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18541","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.18541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18541","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6455575823783875}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-domain":[0],"few-shot":[1],"object":[2],"detection":[3,204],"(CD-FSOD)":[4],"aims":[5],"to":[6,14,48,86,100,133,153,173,192],"adapt":[7],"pretrained":[8],"detectors":[9],"from":[10,21,95],"a":[11,34,56,84,120,128,148,168],"source":[12],"domain":[13,23],"target":[15,45,193],"domains":[16],"with":[17],"limited":[18],"annotations,":[19],"suffering":[20],"severe":[22],"shifts":[24],"and":[25,41,51,166,206],"data":[26],"scarcity":[27],"problems.":[28],"In":[29],"this":[30,88,103],"work,":[31],"we":[32,64,98,113],"find":[33],"previously":[35],"overlooked":[36],"phenomenon:":[37],"models":[38],"exhibit":[39],"dispersed":[40],"unfocused":[42],"attention":[43,73,122,135,185],"in":[44,102],"domains,":[46],"leading":[47],"imprecise":[49],"localization":[50],"redundant":[52],"predictions,":[53],"just":[54],"like":[55],"human":[57,109],"cannot":[58],"focus":[59],"on":[60,72,196],"visual":[61,111,144,163],"objects.":[62],"Therefore,":[63],"call":[65],"it":[66],"the":[67,108,115,143,162],"target-domain":[68],"Astigmatism":[69],"problem.":[70],"Analysis":[71],"distances":[74],"across":[75],"transformer":[76],"layers":[77],"reveals":[78],"that":[79],"regular":[80],"fine-tuning":[81],"inherently":[82],"shows":[83],"trend":[85,118],"remedy":[87],"problem,":[89],"but":[90],"results":[91],"are":[92],"still":[93],"far":[94],"satisfactory,":[96],"which":[97,125],"aim":[99],"enhance":[101,114,154],"paper.":[104],"Biologically":[105],"inspired":[106],"by":[107,157],"fovea-style":[110],"system,":[112],"fine-tuning's":[116],"inherent":[117],"through":[119,177],"center-periphery":[121,175],"refinement":[123],"framework,":[124],"contains":[126],"(1)":[127],"Positive":[129],"Pattern":[130],"Refinement":[131],"module":[132,152,172],"reshape":[134],"toward":[136],"semantic":[137],"objects":[138],"using":[139],"class-specific":[140],"prototypes,":[141],"simulating":[142,161],"center":[145],"region;":[146,165],"(2)":[147],"Negative":[149],"Context":[150],"Modulation":[151],"boundary":[155],"discrimination":[156],"modeling":[158],"background":[159],"context,":[160],"periphery":[164],"(3)":[167],"Textual":[169],"Semantic":[170],"Alignment":[171],"strengthen":[174],"distinction":[176],"cross-modal":[178],"cues.":[179],"Our":[180],"bio-inspired":[181],"approach":[182],"transforms":[183],"astigmatic":[184],"into":[186],"focused":[187],"patterns,":[188],"substantially":[189],"improving":[190],"adaptation":[191],"domains.":[194],"Experiments":[195],"six":[197],"challenging":[198],"CD-FSOD":[199],"benchmarks":[200],"consistently":[201],"demonstrate":[202],"improved":[203],"accuracy":[205],"establish":[207],"new":[208],"state-of-the-art":[209],"results.":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-21T00:00:00"}
