{"id":"https://openalex.org/W7154738293","doi":"https://doi.org/10.48550/arxiv.2604.14762","title":"OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism","display_name":"OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154738293","doi":"https://doi.org/10.48550/arxiv.2604.14762"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14762","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.14762","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042618090","display_name":"Jordan Shipard","orcid":"https://orcid.org/0000-0002-0403-262X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shipard, Jordan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063048878","display_name":"Arnold Wiliem","orcid":"https://orcid.org/0000-0001-8746-9394"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wiliem, Arnold","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100033079","display_name":"Kien Nguyen Thanh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thanh, Kien Nguyen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133838039","display_name":"Wei Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133838919","display_name":"Clinton Fookes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fookes, Clinton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042618090"],"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6546000242233276,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6546000242233276,"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/T10028","display_name":"Topic Modeling","score":0.11050000041723251,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.028599999845027924,"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/encoder","display_name":"Encoder","score":0.5394999980926514},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5083000063896179},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.48429998755455017},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4542999863624573},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43059998750686646},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4147999882698059},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4115999937057495},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4075999855995178},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.383899986743927},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3831999897956848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6438999772071838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5784000158309937},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5394999980926514},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5083000063896179},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.48429998755455017},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4542999863624573},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42080000042915344},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3831999897956848},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C189645446","wikidata":"https://www.wikidata.org/wiki/Q350865","display_name":"Mirroring","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35510000586509705},{"id":"https://openalex.org/C119322782","wikidata":"https://www.wikidata.org/wiki/Q2662236","display_name":"VC dimension","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.325300008058548},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.31790000200271606},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C2780617661","wikidata":"https://www.wikidata.org/wiki/Q541563","display_name":"Subcategory","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C44083865","wikidata":"https://www.wikidata.org/wiki/Q3853443","display_name":"Mean reciprocal rank","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14762","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.14762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14762","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5034956932067871,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generalized":[0],"Category":[1],"Discovery":[2],"(GCD)":[3],"challenges":[4],"methods":[5,168],"to":[6,59,66],"identify":[7],"known":[8,129],"and":[9,30,130,143,149],"novel":[10,86,131],"classes":[11,132],"using":[12,84],"partially":[13],"labeled":[14],"data,":[15],"mirroring":[16],"human":[17,43],"category":[18,46,108,164,196],"learning.":[19],"Unlike":[20],"prior":[21],"GCD":[22,38,98,118,188],"methods,":[23],"which":[24,72],"operate":[25],"within":[26],"a":[27,36,68,78,85,96,183],"single":[28],"modality":[29],"require":[31],"dataset-specific":[32,102],"fine-tuning,":[33],"we":[34,94],"propose":[35],"modality-agnostic":[37,107,167,187],"approach":[39],"inspired":[40],"by":[41,63],"the":[42,154,191],"brain's":[44],"abstract":[45],"formation.":[47],"Our":[48,179],"$\\textbf{OmniGCD}$":[49],"leverages":[50],"modality-specific":[51],"encoders":[52,158],"(e.g.,":[53],"vision,":[54,146],"audio,":[55],"text,":[56,147],"remote":[57,150],"sensing)":[58],"process":[60],"inputs,":[61],"followed":[62],"dimension":[64],"reduction":[65],"construct":[67],"$\\textbf{GCD":[69],"latent":[70],"space}$,":[71],"is":[73,104,200],"transformed":[74],"at":[75],"test-time":[76],"into":[77],"representation":[79,161],"better":[80],"suited":[81],"for":[82,128,145,185,193],"clustering":[83],"synthetically":[87],"trained":[88],"Transformer-based":[89],"model.":[90],"To":[91],"evaluate":[92],"OmniGCD,":[93],"introduce":[95],"$\\textbf{zero-shot":[97],"setting}$":[99],"where":[100],"no":[101],"fine-tuning":[103],"allowed,":[105],"enabling":[106,173],"discovery.":[109,165,197],"$\\textbf{Trained":[110],"once":[111],"on":[112],"synthetic":[113],"data}$,":[114],"OmniGCD":[115],"performs":[116],"zero-shot":[117],"across":[119,171],"16":[120],"datasets":[121],"spanning":[122],"four":[123],"modalities,":[124,172],"improving":[125],"classification":[126],"accuracy":[127],"over":[133],"baselines":[134],"(average":[135],"percentage":[136],"point":[137],"improvement":[138],"of":[139,156,177],"$\\textbf{+6.2}$,":[140],"$\\textbf{+17.9}$,":[141],"$\\textbf{+1.5}$":[142],"$\\textbf{+12.7}$":[144],"audio":[148],"sensing).":[151],"This":[152],"highlights":[153],"importance":[155],"strong":[157],"while":[159],"decoupling":[160],"learning":[162],"from":[163],"Improving":[166],"will":[169],"propagate":[170],"encoder":[174],"development":[175],"independent":[176],"GCD.":[178],"work":[180],"serves":[181],"as":[182],"benchmark":[184],"future":[186],"works,":[189],"paving":[190],"way":[192],"scalable,":[194],"human-inspired":[195],"All":[198],"code":[199],"available":[201],"$\\href{https://github.com/Jordan-HS/OmniGCD}{here}$":[202]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-04-18T00:00:00"}
