{"id":"https://openalex.org/W6967186449","doi":"https://doi.org/10.48550/arxiv.2507.08248","title":"Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification","display_name":"Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classification","publication_year":2025,"publication_date":"2025-07-11","ids":{"openalex":"https://openalex.org/W6967186449","doi":"https://doi.org/10.48550/arxiv.2507.08248"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2507.08248","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08248","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2507.08248","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tam, Jason Kahei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tam, Jason Kahei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Gustineli, Murilo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gustineli, Murilo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Miyaguchi, Anthony","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miyaguchi, Anthony","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":true,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.20569999516010284,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.20569999516010284,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19709999859333038,"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/T10862","display_name":"AI in cancer detection","score":0.08309999853372574,"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/categorization","display_name":"Categorization","score":0.6995999813079834},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.6503000259399414},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6207000017166138},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43860000371932983},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.41769999265670776},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41110000014305115},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3921000063419342},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.3889999985694885},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.38420000672340393}],"concepts":[{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6995999813079834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880000233650208},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6503000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6432999968528748},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6207000017166138},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5677000284194946},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31679999828338623},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3149000108242035},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3075000047683716},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2507.08248","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08248","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2507.08248","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08248","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"identification":[1],"of":[2,94],"fungi":[3],"species":[4],"presents":[5,23],"a":[6],"unique":[7],"challenge":[8],"in":[9,111],"computer":[10],"vision":[11,50],"due":[12],"to":[13,76,80],"fine-grained":[14,35],"inter-species":[15],"variation":[16],"and":[17,57,90,98,124],"high":[18],"intra-species":[19],"variation.":[20],"This":[21],"paper":[22],"our":[24],"approach":[25,103],"for":[26,67],"the":[27,40,92,107],"FungiCLEF":[28],"2025":[29],"competition,":[30],"which":[31],"focuses":[32],"on":[33,106,121],"few-shot":[34],"visual":[36],"categorization":[37],"(FGVC)":[38],"using":[39,70],"FungiTastic":[41],"Few-Shot":[42],"dataset.":[43],"Our":[44,83,102,128],"team":[45],"(DS@GT)":[46],"experimented":[47],"with":[48],"multiple":[49],"transformer":[51],"models,":[52],"data":[53],"augmentation,":[54],"weighted":[55],"sampling,":[56],"incorporating":[58],"textual":[59],"information.":[60],"We":[61],"also":[62],"explored":[63],"generative":[64],"AI":[65],"models":[66],"zero-shot":[68],"classification":[69],"structured":[71],"prompting":[72],"but":[73],"found":[74],"them":[75],"significantly":[77],"underperform":[78],"relative":[79],"vision-based":[81],"models.":[82],"final":[84],"model":[85],"outperformed":[86],"both":[87],"competition":[88],"baselines":[89],"highlighted":[91],"effectiveness":[93],"domain":[95],"specific":[96],"pretraining":[97],"balanced":[99],"sampling":[100],"strategies.":[101],"ranked":[104],"35/74":[105],"private":[108],"test":[109],"set":[110],"post-completion":[112],"evaluation,":[113],"this":[114],"suggests":[115],"additional":[116],"work":[117],"can":[118],"be":[119],"done":[120],"metadata":[122],"selection":[123],"domain-adapted":[125],"multi-modal":[126],"learning.":[127],"code":[129],"is":[130],"available":[131],"at":[132],"https://github.com/dsgt-arc/fungiclef-2025.":[133]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}