{"id":"https://openalex.org/W7155186584","doi":"https://doi.org/10.48550/arxiv.2604.19345","title":"Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data","display_name":"Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155186584","doi":"https://doi.org/10.48550/arxiv.2604.19345"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19345","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19345","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134279755","display_name":"Shijie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134283204","display_name":"Yadan Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yadan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134219257","display_name":"Zijian Wang (481291)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zijian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134258168","display_name":"Haojie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134348445","display_name":"Zi Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014648528","display_name":"Mahsa Baktashmotlagh","orcid":"https://orcid.org/0000-0001-5255-8194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baktashmotlagh, Mahsa","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/T10616","display_name":"Smart Agriculture and AI","score":0.5037999749183655,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.5037999749183655,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.13089999556541443,"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/T10057","display_name":"Face and Expression Recognition","score":0.066600002348423,"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/categorization","display_name":"Categorization","score":0.649399995803833},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6083999872207642},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46239998936653137},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4587000012397766},{"id":"https://openalex.org/keywords/geometric-data-analysis","display_name":"Geometric data analysis","score":0.4546000063419342},{"id":"https://openalex.org/keywords/geometric-shape","display_name":"Geometric shape","score":0.4300000071525574},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.40529999136924744},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6771000027656555},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.649399995803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166999936103821},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6083999872207642},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C136520226","wikidata":"https://www.wikidata.org/wiki/Q302814","display_name":"Geometric data analysis","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C7305733","wikidata":"https://www.wikidata.org/wiki/Q207961","display_name":"Geometric shape","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3824999928474426},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C32990609","wikidata":"https://www.wikidata.org/wiki/Q306542","display_name":"Transformation geometry","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C160697094","wikidata":"https://www.wikidata.org/wiki/Q1233197","display_name":"Oblique case","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C56435381","wikidata":"https://www.wikidata.org/wiki/Q1196371","display_name":"Geometric transformation","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C181095308","wikidata":"https://www.wikidata.org/wiki/Q1541599","display_name":"Geometric primitive","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C2993928531","wikidata":"https://www.wikidata.org/wiki/Q2083958","display_name":"Geometric pattern","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2667999863624573},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19345","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.19345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19345","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":"Preprint"},"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":{"This":[0],"paper":[1],"investigates":[2],"the":[3,18,29,65,74,127,135],"intrinsic":[4],"geometrical":[5],"features":[6],"of":[7,131],"highly":[8],"similar":[9],"objects":[10,95],"and":[11],"introduces":[12],"a":[13,101,122],"general":[14],"self-supervised":[15],"framework":[16],"called":[17],"Geometric":[19],"Attribute":[20],"Exploration":[21],"Network":[22],"(GAEor),":[23],"which":[24],"is":[25],"designed":[26],"to":[27],"address":[28],"ultra-fine-grained":[30],"visual":[31,98,119],"categorization":[32],"(Ultra-FGVC)":[33],"task":[34],"in":[35,78,105,148],"data-limited":[36],"scenarios.":[37],"Unlike":[38],"prior":[39],"work":[40],"that":[41,88,141],"often":[42],"captures":[43],"subtle":[44],"yet":[45],"critical":[46],"distinctions,":[47],"GAEor":[48,108,142],"generates":[49],"geometric":[50,70,86,111],"attributes":[51,58,112],"as":[52,73,90],"novel":[53],"alternative":[54],"recognition":[55],"cues.":[56],"These":[57],"are":[59],"determined":[60],"by":[61,113],"various":[62],"details":[63,117,133],"within":[64],"object,":[66],"aligned":[67],"with":[68,96],"its":[69],"patterns,":[71],"such":[72],"intricate":[75],"vein":[76],"structures":[77],"soybean":[79],"leaves.":[80],"Crucially,":[81],"each":[82],"category":[83],"exhibits":[84],"distinct":[85],"descriptors":[87],"serve":[89],"powerful":[91],"cues,":[92],"even":[93],"among":[94],"minimal":[97],"variation":[99],"--":[100],"factor":[102],"largely":[103],"overlooked":[104],"recent":[106],"research.":[107],"discovers":[109],"these":[110,132],"first":[114],"amplifying":[115],"geometry-relevant":[116],"via":[118],"feedback":[120],"from":[121],"backbone":[123],"network,":[124],"then":[125],"embedding":[126],"relative":[128],"polar":[129],"coordinates":[130],"into":[134],"final":[136],"representation.":[137],"Extensive":[138],"experiments":[139],"demonstrate":[140],"significantly":[143],"sets":[144],"new":[145],"state-of-the-art":[146],"records":[147],"five":[149],"widely-used":[150],"Ultra-FGVC":[151],"benchmarks.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-23T00:00:00"}