{"id":"https://openalex.org/W7166671528","doi":"https://doi.org/10.48550/arxiv.2606.29029","title":"Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection","display_name":"Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection","publication_year":2026,"publication_date":"2026-06-27","ids":{"openalex":"https://openalex.org/W7166671528","doi":"https://doi.org/10.48550/arxiv.2606.29029"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.29029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29029","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.2606.29029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101893660","display_name":"Yi Guo","orcid":"https://orcid.org/0009-0006-1890-6726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139712032","display_name":"Zihan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018349076","display_name":"Feifei Kou","orcid":"https://orcid.org/0000-0001-9460-8102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kou, Feifei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139678691","display_name":"Yulan Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yulan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139661340","display_name":"Ran Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139697748","display_name":"Siyuan Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Siyuan","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":0.7477999925613403,"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":0.7477999925613403,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.05779999867081642,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.046799998730421066,"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/discriminative-model","display_name":"Discriminative model","score":0.7239999771118164},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6987000107765198},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.59579998254776},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5922999978065491},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5863000154495239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5853999853134155},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.49729999899864197},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4503999948501587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76910001039505},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7239999771118164},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6987000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6798999905586243},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5922999978065491},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5863000154495239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5853999853134155},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.49729999899864197},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4503999948501587},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4388999938964844},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4323999881744385},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41040000319480896},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.34790000319480896},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.29029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29029","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.2606.29029","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29029","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":[{"score":0.7610706686973572,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Small":[0],"Object":[1],"Detection":[2],"(SOD)":[3],"is":[4,55,156],"a":[5,46,114,151],"fundamental":[6],"yet":[7],"challenging":[8,141],"problem":[9],"in":[10,33,108],"computer":[11],"vision":[12],"due":[13],"to":[14,85,99],"its":[15],"limited":[16],"spatial":[17],"resolution":[18],"and":[19,128],"weak":[20],"visual":[21],"cues.":[22],"Although":[23],"recent":[24],"approaches":[25],"have":[26],"achieved":[27],"remarkable":[28],"advances,":[29],"the":[30,39,92,101,105,109,125,130],"background":[31,93],"distractors":[32,94],"different":[34],"frequency":[35],"spectra":[36],"still":[37],"degrade":[38],"performance.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,67,112],"propose":[45,68,113],"novel":[47],"small":[48,59],"object":[49,126],"detection":[50],"framework":[51],"termed":[52],"SFDNet,":[53],"which":[54,120],"capable":[56],"of":[57,104],"detecting":[58],"objects":[60,107],"via":[61],"efficient":[62,134],"spectrum-aware":[63],"feature":[64],"disentanglement.":[65],"Specifically,":[66],"an":[69],"Adaptive":[70],"Spectrum":[71],"Disentanglement":[72],"(ASD)":[73],"module":[74],"that":[75,144],"decomposes":[76],"backbone":[77],"features":[78],"into":[79],"multiple":[80,140],"complementary":[81],"spectral":[82],"components,":[83],"aiming":[84],"construct":[86],"discriminative":[87],"object-relevant":[88],"representations":[89],"by":[90,133,150],"discarding":[91],"for":[95,124],"each":[96],"component.":[97],"Afterwards,":[98],"strengthen":[100],"semantic":[102],"consistency":[103],"similar":[106],"same":[110],"class,":[111],"Class-Wise":[115],"Prototype":[116],"Distillation":[117],"(CPD)":[118],"procedure,":[119],"establishes":[121],"class":[122],"prototypes":[123],"instances":[127],"enforces":[129],"compact":[131],"representation":[132],"prototype":[135],"distillation.":[136],"Extensive":[137],"experiments":[138],"on":[139],"benchmarks":[142],"show":[143],"SFDNet":[145],"outperforms":[146],"existing":[147],"state-of-the-art":[148],"methods":[149],"large":[152],"margin.":[153],"Our":[154],"code":[155],"available":[157],"at":[158],"https://github.com/ManOfStory/SFDNet.":[159]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
