{"id":"https://openalex.org/W7131844272","doi":"https://doi.org/10.1109/tmm.2026.3668641","title":"ClassBooth: Boost Class Semantics With Bidirectional Feature Fusion in Text-to-Image Diffusion Models","display_name":"ClassBooth: Boost Class Semantics With Bidirectional Feature Fusion in Text-to-Image Diffusion Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131844272","doi":"https://doi.org/10.1109/tmm.2026.3668641"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2026.3668641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3668641","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127059035","display_name":"Yunfei Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Zhang","raw_affiliation_strings":["South China University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108047889","display_name":"Yi-Xiang Wang","orcid":"https://orcid.org/0000-0001-5697-0717"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Wang","raw_affiliation_strings":["South China University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127328256","display_name":"Yan Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Huang","raw_affiliation_strings":["South China University of Technology"],"raw_orcid":"https://orcid.org/0000-0001-9136-195X","affiliations":[{"raw_affiliation_string":"South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136146807","display_name":"Tianyi Chen","orcid":"https://orcid.org/0000-0002-9708-8939"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tianyi Chen","raw_affiliation_strings":["City University of Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-9708-8939","affiliations":[{"raw_affiliation_string":"City University of Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014196059","display_name":"Hau\u2212San Wong","orcid":"https://orcid.org/0000-0002-1530-7529"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hau-San Wong","raw_affiliation_strings":["City University of Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-1530-7529","affiliations":[{"raw_affiliation_string":"City University of Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127117249","display_name":"Si Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Wu","raw_affiliation_strings":["South China University of Technology"],"raw_orcid":"https://orcid.org/0000-0003-4022-0852","affiliations":[{"raw_affiliation_string":"South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15772699,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"6593","last_page":"6604"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2425999939441681,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2425999939441681,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.16459999978542328,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11919999867677689,"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/encode","display_name":"ENCODE","score":0.578499972820282},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5404999852180481},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.531499981880188},{"id":"https://openalex.org/keywords/fusion-mechanism","display_name":"Fusion mechanism","score":0.46779999136924744},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.446399986743927},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4221999943256378},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3781999945640564},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37369999289512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8765000104904175},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.578499972820282},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5404999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.536899983882904},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.531499981880188},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.46779999136924744},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2913999855518341},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2565000057220459},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2026.3668641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3668641","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41306620836257935,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G7620326010","display_name":null,"funder_award_id":"2024A1515011437","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2533598788","https://openalex.org/W2962770929","https://openalex.org/W3035574324","https://openalex.org/W3094502228","https://openalex.org/W3174194560","https://openalex.org/W4312911498","https://openalex.org/W4312933868","https://openalex.org/W4312977351","https://openalex.org/W4377715513","https://openalex.org/W4385270985","https://openalex.org/W4386047807","https://openalex.org/W4386071985","https://openalex.org/W4386072096","https://openalex.org/W4386076027","https://openalex.org/W4386076458","https://openalex.org/W4390871835","https://openalex.org/W4390872297","https://openalex.org/W4390873054","https://openalex.org/W4393148714","https://openalex.org/W4396817358","https://openalex.org/W4400410743","https://openalex.org/W4402753775","https://openalex.org/W4406857644","https://openalex.org/W4406982886","https://openalex.org/W4413146880","https://openalex.org/W7133185348","https://openalex.org/W7133196220","https://openalex.org/W7133221421"],"related_works":[],"abstract_inverted_index":{"Text-to-image":[0],"(T2I)":[1],"diffusion":[2,55,81],"models":[3,23,56],"aim":[4],"to":[5,48,64,113,123],"generate":[6,49],"images":[7],"that":[8,38,77],"are":[9,108],"both":[10],"visually":[11],"realistic":[12],"and":[13,141,173],"aligned":[14],"with":[15,110],"open-domain":[16,90],"textual":[17],"prompts.":[18],"However,":[19],"the":[20,46,50,89,120,135],"leading":[21],"T2I":[22,54,80,121],"often":[24],"fall":[25],"short":[26],"in":[27,83],"capturing":[28],"semantic":[29,101,148],"details,":[30],"especially":[31],"for":[32,146],"fine-grained":[33,115,125],"object":[34],"categories.":[35],"We":[36,158],"find":[37],"simply":[39],"expanding":[40],"original":[41],"prompts":[42],"cannot":[43],"effectively":[44],"guide":[45],"model":[47,82,122],"desired":[51],"content,":[52,86],"since":[53],"pre-trained":[57,79],"on":[58,104,134,138],"generic":[59],"datasets":[60],"lack":[61],"a":[62,74],"mechanism":[63],"boost":[65],"class":[66],"semantics.":[67],"In":[68],"this":[69,94,142],"work,":[70],"we":[71,96,130],"present":[72],"ClassBooth,":[73],"flexible":[75],"framework":[76],"improves":[78],"rendering":[84],"class-specific":[85,100,147,156],"while":[87],"preserving":[88],"generation":[91],"capability.":[92],"Toward":[93],"end,":[95],"introduce":[97],"an":[98],"auxiliary":[99],"booster":[102],"conditioned":[103,137],"learnable":[105],"prompts,":[106],"which":[107],"associated":[109],"specific":[111,128],"classes":[112],"encode":[114],"conditioning":[116],"information.":[117],"To":[118],"enable":[119],"synthesize":[124],"details":[126],"of":[127],"categories,":[129],"perform":[131],"bidirectional":[132],"fusion":[133],"features":[136],"different":[139],"information,":[140],"design":[143],"is":[144],"beneficial":[145],"expression,":[149],"thereby":[150],"synthesizing":[151],"high-fidelity":[152],"data":[153],"encapsulating":[154],"precise":[155],"details.":[157],"validate":[159],"ClassBooth":[160],"across":[161],"multiple":[162],"benchmarks,":[163],"demonstrating":[164],"its":[165],"superiority":[166],"over":[167],"existing":[168],"methods":[169],"through":[170],"comprehensive":[171],"quantitative":[172],"qualitative":[174],"evaluations.":[175]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-02-28T00:00:00"}