{"id":"https://openalex.org/W4414360661","doi":"https://doi.org/10.24963/ijcai.2025/143","title":"Seeing the Unseen: Composing Outliers for Compositional Zero-Shot Learning","display_name":"Seeing the Unseen: Composing Outliers for Compositional Zero-Shot Learning","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360661","doi":"https://doi.org/10.24963/ijcai.2025/143"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/143","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/143","pdf_url":"https://www.ijcai.org/proceedings/2025/0143.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2025/0143.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101867737","display_name":"Chenchen Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]},{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenchen Jing","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China","Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]},{"raw_affiliation_string":"Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054653829","display_name":"Mingyu Liu","orcid":"https://orcid.org/0000-0001-8188-9953"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Liu","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353596","display_name":"Hao Chen","orcid":"https://orcid.org/0000-0002-8400-3780"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035403237","display_name":"Yuling Xi","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuling Xi","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039569339","display_name":"Xingyuan Bu","orcid":"https://orcid.org/0000-0002-6445-4306"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingyuan Bu","raw_affiliation_strings":["Alibaba Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101768095","display_name":"Dong Gong","orcid":"https://orcid.org/0000-0002-2668-9630"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dong Gong","raw_affiliation_strings":["The University of New South Wales"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of New South Wales","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006294869","display_name":"Chunhua Shen","orcid":"https://orcid.org/0000-0002-8648-8718"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]},{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Shen","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China","Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]},{"raw_affiliation_string":"Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6504,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85151641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1278","last_page":"1286"},"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":0.9674000144004822,"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":0.9674000144004822,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9577999711036682,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.934499979019165,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7305999994277954},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5177000164985657},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5174999833106995},{"id":"https://openalex.org/keywords/composition","display_name":"Composition (language)","score":0.36500000953674316},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.30230000615119934},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2989000082015991}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7865999937057495},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7305999994277954},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6251999735832214},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5177000164985657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C40231798","wikidata":"https://www.wikidata.org/wiki/Q1333743","display_name":"Composition (language)","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34790000319480896},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/143","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/143","pdf_url":"https://www.ijcai.org/proceedings/2025/0143.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2025/143","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/143","pdf_url":"https://www.ijcai.org/proceedings/2025/0143.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414360661.pdf","grobid_xml":"https://content.openalex.org/works/W4414360661.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Compositional":[0],"zero-shot":[1,95],"learning":[2,11,96],"(CZSL)":[3],"is":[4,68],"to":[5,26,135,139,165],"recognize":[6,166],"unseen":[7,19,39,66,72,109,124,141],"attribute-object":[8,121],"compositions":[9,20,23,54,67,73,110,125,142,154],"by":[10],"from":[12,84],"seen":[13,22,37,85,107],"compositions.":[14,40,86,168],"The":[15,41],"distribution":[16],"shift":[17],"between":[18],"and":[21,38,55,78,108,111,155,162,185],"poses":[24],"challenges":[25],"CZSL":[27],"models,":[28],"especially":[29],"when":[30],"test":[31,146],"images":[32,64,132,151],"are":[33,74],"mixed":[34],"with":[35,58,65],"both":[36,181],"challenge":[42],"will":[43],"be":[44],"addressed":[45],"more":[46],"easily":[47],"if":[48],"a":[49,92],"model":[50,138],"can":[51],"distinguish":[52],"unseen/seen":[53],"treat":[56],"them":[57],"specific":[59,114],"recognition":[60],"strategies.":[61],"However,":[62],"identifying":[63],"non-trivial,":[69],"considering":[70],"that":[71],"absent":[75],"in":[76,103,143,180],"training":[77,104,131],"usually":[79],"contain":[80],"only":[81],"subtle":[82],"differences":[83],"In":[87],"this":[88],"paper,":[89],"we":[90,119],"propose":[91],"novel":[93],"compositional":[94],"method":[97,149,179],"called":[98],"COMO,":[99],"which":[100],"composes":[101],"outliers":[102,134],"for":[105,116,123,159],"distinguishing":[106],"further":[112],"applying":[113],"strategies":[115],"them.":[117],"Specifically,":[118],"compose":[120],"representations":[122,129],"based":[126],"on":[127,171],"primitive":[128,163],"of":[130,177],"as":[133],"enable":[136],"the":[137,148,175,182,186],"identify":[140],"inference.":[144],"At":[145],"time,":[147],"distinguishes":[150],"containing":[152],"seen/unseen":[153,167],"uses":[156],"different":[157],"weights":[158],"composition":[160],"classification":[161,164],"Experimental":[169],"results":[170],"three":[172],"datasets":[173],"show":[174],"effectiveness":[176],"our":[178],"closed-world":[183],"setting":[184],"open-world":[187],"setting.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}
