{"id":"https://openalex.org/W7154715788","doi":"https://doi.org/10.48550/arxiv.2604.14958","title":"Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification","display_name":"Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154715788","doi":"https://doi.org/10.48550/arxiv.2604.14958"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14958","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14958","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.2604.14958","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133838863","display_name":"Meijia Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Meijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133868487","display_name":"Guochao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guochao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073852890","display_name":"Haozhen Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Haozhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100757927","display_name":"Bin Yao","orcid":"https://orcid.org/0000-0002-6478-4209"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133887207","display_name":"Weichuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weichuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133888190","display_name":"Yuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026686074","display_name":"Junpo Yang","orcid":"https://orcid.org/0000-0003-0449-6333"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Junpo","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/T11448","display_name":"Face recognition and analysis","score":0.6682999730110168,"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/T11448","display_name":"Face recognition and analysis","score":0.6682999730110168,"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":0.1459999978542328,"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/T10057","display_name":"Face and Expression Recognition","score":0.03220000118017197,"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/overfitting","display_name":"Overfitting","score":0.7930999994277954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6035000085830688},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5230000019073486},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5120000243186951},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.45890000462532043},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.44200000166893005},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.42570000886917114},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3874000012874603},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.362199991941452}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7930999994277954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.660099983215332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6324999928474426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6035000085830688},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5230000019073486},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5120000243186951},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.362199991941452},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.271699994802475},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.26809999346733093},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.2572000026702881},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25049999356269836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14958","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14958","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.2604.14958","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14958","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Few-shot":[0],"fine-grained":[1,225],"image":[2],"classification":[3,186],"aims":[4],"to":[5,32,59,95,116,136,156,163,195],"recognize":[6],"subcategories":[7],"with":[8,47],"high":[9,213],"visual":[10,226],"similarity":[11],"using":[12],"only":[13],"a":[14,91,209],"limited":[15],"number":[16],"of":[17,152],"annotated":[18],"samples.":[19],"Existing":[20],"metric":[21,197],"learning-based":[22],"methods":[23],"typically":[24],"rely":[25],"solely":[26],"on":[27,168],"spatial":[28,103,124,159],"domain":[29],"features.":[30,129,165],"Confined":[31],"this":[33,61,74],"single":[34],"perspective,":[35],"models":[36],"inevitably":[37],"suffer":[38],"from":[39,102,142,161],"inherent":[40],"texture":[41,125],"biases,":[42],"entangling":[43],"essential":[44],"structural":[45,65,100,128,150],"details":[46],"high-frequency":[48],"background":[49,107,164],"noise.":[50],"Furthermore,":[51],"lacking":[52],"cross-view":[53],"geometric":[54],"constraints,":[55],"single-view":[56],"metrics":[57],"tend":[58],"overfit":[60],"noise,":[62],"resulting":[63],"in":[64],"instability":[66],"under":[67],"few-shot":[68,224],"conditions.":[69],"To":[70],"address":[71],"these":[72,143],"issues,":[73],"paper":[75],"proposes":[76],"the":[77,85,139,149,153,158,205],"Frequency-Enhanced":[78],"Dual-Subspace":[79],"Network":[80],"(FEDSNet).":[81],"Specifically,":[82],"FEDSNet":[83,183],"utilizes":[84],"Discrete":[86],"Cosine":[87],"Transform":[88],"(DCT)":[89],"and":[90,126,178,188,215],"low-pass":[92],"filtering":[93],"mechanism":[94,133],"explicitly":[96],"isolate":[97],"low-frequency":[98],"global":[99],"components":[101],"features,":[104],"thereby":[105],"suppressing":[106],"interference.":[108],"Truncated":[109],"Singular":[110],"Value":[111],"Decomposition":[112],"(SVD)":[113],"is":[114,134],"employed":[115],"construct":[117],"independent,":[118],"low-rank":[119],"linear":[120],"subspaces":[121],"for":[122,223],"both":[123],"frequency":[127,154],"An":[130],"adaptive":[131],"gating":[132],"designed":[135],"dynamically":[137],"fuse":[138],"projection":[140],"distances":[141],"dual":[144],"views.":[145],"This":[146],"strategy":[147],"leverages":[148],"stability":[151],"subspace":[155,160],"prevent":[157],"overfitting":[162],"Extensive":[166],"experiments":[167],"four":[169],"benchmark":[170],"datasets":[171],"-":[172,180],"CUB-200-2011,":[173],"Stanford":[174,176],"Cars,":[175],"Dogs,":[177],"FGVC-Aircraft":[179],"demonstrate":[181],"that":[182,204],"exhibits":[184],"excellent":[185],"performance":[187],"robustness,":[189],"achieving":[190],"highly":[191],"competitive":[192],"results":[193],"compared":[194],"existing":[196],"learning":[198],"algorithms.":[199],"Complexity":[200],"analysis":[201],"further":[202],"confirms":[203],"proposed":[206],"network":[207],"achieves":[208],"favorable":[210],"balance":[211],"between":[212],"accuracy":[214],"computational":[216],"efficiency,":[217],"providing":[218],"an":[219],"effective":[220],"new":[221],"paradigm":[222],"recognition.":[227]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-18T00:00:00"}