{"id":"https://openalex.org/W7135053102","doi":"https://doi.org/10.1109/tip.2026.3671655","title":"Semi-Negative Contrastive Subclass Discriminative Network for Compositional Zero-Shot Learning","display_name":"Semi-Negative Contrastive Subclass Discriminative Network for Compositional Zero-Shot Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7135053102","doi":"https://doi.org/10.1109/tip.2026.3671655","pmid":"https://pubmed.ncbi.nlm.nih.gov/41817992"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2026.3671655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2026.3671655","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-5917-8653"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0001-5917-8653","affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056851669","display_name":"Xinshuo Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinshuo Wang","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0000-3204-3337","affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinbo Gao","orcid":"https://orcid.org/0000-0002-7985-0037"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinbo Gao","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-7985-0037","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128866809","display_name":"Jungong Han","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jungong Han","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4361-956X","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128916852","display_name":"Ling Shao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Shao","raw_affiliation_strings":["UCAS-Terminus AI Lab, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8264-6117","affiliations":[{"raw_affiliation_string":"UCAS-Terminus AI Lab, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.1051,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.97467829,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"35","issue":null,"first_page":"2640","last_page":"2652"},"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":0.9395999908447266,"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":0.9395999908447266,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0071000000461936,"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/T11448","display_name":"Face recognition and analysis","score":0.00559999980032444,"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/discriminative-model","display_name":"Discriminative model","score":0.7177000045776367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5964000225067139},{"id":"https://openalex.org/keywords/subclass","display_name":"Subclass","score":0.5324000120162964},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3345000147819519},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33410000801086426},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.30720001459121704}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7177000045776367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.699999988079071},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5544000267982483},{"id":"https://openalex.org/C2776194381","wikidata":"https://www.wikidata.org/wiki/Q3965271","display_name":"Subclass","level":3,"score":0.5324000120162964},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39969998598098755},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.29600000381469727},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2712000012397766},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C2987247673","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Character recognition","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2026.3671655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2026.3671655","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:41817992","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41817992","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.8234250545501709,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G4854076541","display_name":null,"funder_award_id":"62376207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1948251820","https://openalex.org/W2093848332","https://openalex.org/W2108598243","https://openalex.org/W2128532956","https://openalex.org/W2250539671","https://openalex.org/W2552383788","https://openalex.org/W2610366607","https://openalex.org/W2736809457","https://openalex.org/W2806457960","https://openalex.org/W2889167066","https://openalex.org/W2962933664","https://openalex.org/W2963499153","https://openalex.org/W2963677766","https://openalex.org/W2982407353","https://openalex.org/W2986385672","https://openalex.org/W3034665604","https://openalex.org/W3035084814","https://openalex.org/W3035524453","https://openalex.org/W3094575670","https://openalex.org/W3158711590","https://openalex.org/W3162332195","https://openalex.org/W3176646400","https://openalex.org/W3189076762","https://openalex.org/W3196690531","https://openalex.org/W3203460211","https://openalex.org/W4281722009","https://openalex.org/W4292974991","https://openalex.org/W4304080190","https://openalex.org/W4312400874","https://openalex.org/W4312539810","https://openalex.org/W4312578829","https://openalex.org/W4313183962","https://openalex.org/W4319299836","https://openalex.org/W4320713198","https://openalex.org/W4382240738","https://openalex.org/W4386065859","https://openalex.org/W4386071542","https://openalex.org/W4386076688","https://openalex.org/W4390873075","https://openalex.org/W4393149562","https://openalex.org/W4393159686","https://openalex.org/W4394597571","https://openalex.org/W4394625573","https://openalex.org/W4401454109","https://openalex.org/W4402716195","https://openalex.org/W4402816857","https://openalex.org/W4403827186","https://openalex.org/W4404721783","https://openalex.org/W4406526548"],"related_works":[],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,55,74,144,188,197],"compositional":[3,176],"zero-shot":[4],"learning":[5],"(CZSL)":[6],"is":[7],"to":[8,12,31,79,117,127,137,163,183],"train":[9],"a":[10,45,84,98,158],"model":[11,30,116,218],"recognize":[13],"images":[14],"containing":[15],"known":[16],"attribute-object":[17],"pairs.":[18],"This":[19,112,178],"reduces":[20],"the":[21,29,71,109,115,134,165,193,198,241],"reliance":[22],"on":[23,92,229],"extensive":[24],"training":[25,49,110],"data":[26],"and":[27,50,58,140,170,210,244],"enables":[28,114],"identify":[32],"unseen":[33],"combinations.":[34,76,204],"Current":[35],"CZSL":[36],"methods":[37],"face":[38],"several":[39],"challenges,":[40,81],"including":[41],"multiple":[42],"attributes":[43,169],"for":[44],"single":[46],"object,":[47],"disconnected":[48],"test":[51],"sets,":[52],"long-tailed":[53,221],"distribution":[54],"visual":[56,195],"categories,":[57],"substantial":[59],"differences":[60,139],"in":[61,220],"state":[62],"representation":[63,208],"between":[64,120,168],"different":[65,121,203],"objects.":[66],"These":[67],"factors":[68],"collectively":[69],"impede":[70],"precise":[72],"identification":[73],"new":[75],"In":[77],"response":[78],"these":[80],"we":[82,96,156],"propose":[83,97],"Semi-Negative":[85],"Contrastive":[86],"Subclass":[87],"Discriminative":[88],"Network":[89],"(SN-CSDN)":[90],"based":[91],"contrastive":[93],"learning.":[94],"Firstly,":[95],"semi-negative":[99],"sampling":[100],"strategy":[101,149],"that":[102,239],"incorporates":[103],"carefully":[104],"selected":[105],"negative":[106],"samples":[107],"into":[108],"process.":[111],"approach":[113],"effectively":[118,216],"distinguish":[119],"classes":[122],"while":[123,191],"enhancing":[124],"its":[125,142],"ability":[126],"capture":[128,164],"fine-grained":[129],"subclass":[130,181],"features.":[131],"By":[132,205],"improving":[133,206],"model's":[135],"sensitivity":[136],"inter-class":[138],"refining":[141],"recognition":[143],"subtle":[145],"intra-class":[146],"variations,":[147],"this":[148,214],"significantly":[150],"boosts":[151],"overall":[152],"discrimination":[153],"performance.":[154],"Additionally,":[155],"introduce":[157],"decoupled":[159,200],"network":[160],"branch":[161,179],"designed":[162],"intricate":[166],"relationships":[167],"objects":[171],"by":[172],"generating":[173],"more":[174],"representative":[175],"embeddings.":[177],"leverages":[180],"information":[182],"ensure":[184],"an":[185],"accurate":[186],"classification":[187],"synthesized":[189],"embeddings":[190,201],"preserving":[192],"inherent":[194],"distinctions":[196],"original":[199],"across":[202],"feature":[207],"capacity":[209],"mitigating":[211],"sample":[212],"imbalance,":[213],"design":[215],"improves":[217],"performance":[219,237],"distributions.":[222],"Our":[223],"method":[224],"has":[225],"been":[226],"comprehensively":[227],"evaluated":[228],"three":[230],"benchmark":[231],"datasets,":[232],"with":[233],"results":[234],"showing":[235],"significant":[236],"improvements":[238],"demonstrate":[240],"method's":[242],"effectiveness":[243],"reliability.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-03-13T00:00:00"}