{"id":"https://openalex.org/W7166139718","doi":"https://doi.org/10.48550/arxiv.2606.27192","title":"LISA: Likelihood Score Alignment for Visual-condition Controllable Generation","display_name":"LISA: Likelihood Score Alignment for Visual-condition Controllable Generation","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166139718","doi":"https://doi.org/10.48550/arxiv.2606.27192"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27192","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.2606.27192","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101486200","display_name":"Yanghao Wang","orcid":"https://orcid.org/0000-0002-6004-1483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yanghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139459225","display_name":"Hongxu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139459455","display_name":"Jiazhen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiazhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102718144","display_name":"Zhenqi He","orcid":"https://orcid.org/0009-0000-2265-7159"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Zhenqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139423738","display_name":"Rui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139420151","display_name":"Zhen Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139460754","display_name":"Long Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Long","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9388999938964844,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9388999938964844,"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":0.010400000028312206,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.007899999618530273,"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/inference","display_name":"Inference","score":0.7002999782562256},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6347000002861023},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4230000078678131},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3935000002384186},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.36390000581741333},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.34360000491142273},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.335099995136261},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.33169999718666077},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3154999911785126}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7002999782562256},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6347000002861023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6061000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4821999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44449999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C83282275","wikidata":"https://www.wikidata.org/wiki/Q7435350","display_name":"Scoring algorithm","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31200000643730164},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.3095000088214874},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2574999928474426},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27192","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.2606.27192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27192","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,65,79],"prevalent":[1],"dual-branch":[2],"paradigm,":[3],"i.e.,":[4],"training":[5,44,205,229],"a":[6,19,74,88,126,142],"side":[7,40,80,113,131,174,216],"network":[8,67,81,114,132,175],"to":[9,18,219],"encode":[10],"visual":[11,69],"conditions":[12],"and":[13,42,133,153,161,176,183,193,207,231],"fusing":[14],"its":[15,33,43],"intermediate-layer":[16],"features":[17,124,218],"frozen":[20],"pretrained":[21],"main":[22,66],"network,":[23],"has":[24],"shown":[25],"remarkable":[26],"success":[27],"in":[28],"visual-condition":[29],"controllable":[30],"generation.":[31],"Despite":[32],"widespread":[34],"adoption,":[35],"the":[36,39,58,108,112,130,137,155,158,173,204,215],"role":[37],"of":[38,60,111,129],"branch":[41],"efficiency":[45],"remain":[46],"underexplored.":[47],"In":[48],"this":[49,54,93,162],"paper,":[50],"we":[51,95,121,146,170],"first":[52,122],"revisit":[53],"mainstream":[55],"paradigm":[56],"through":[57],"lens":[59],"score-based":[61],"generative":[62],"modeling:":[63],"1)":[64],"preserves":[68],"perceptual":[70],"quality":[71],"by":[72,85,92,141],"providing":[73],"prior":[75],"unconditional":[76],"score.":[77,90,119],"2)":[78],"steers":[82],"conditional":[83,224],"control":[84],"implicitly":[86],"contributing":[87],"likelihood":[89,118,150],"Guided":[91],"perspective,":[94],"propose":[96],"LIkelihood":[97],"Score":[98],"Alignment":[99],"(LISA),":[100],"an":[101,116,148,165],"effective":[102],"regularization":[103,167,185],"method":[104],"that":[105,197],"explicitly":[106],"aligns":[107],"intermediate":[109],"feature":[110],"with":[115,178,226],"approximated":[117,149],"Specifically,":[120],"hook":[123],"from":[125],"designated":[127],"layer":[128],"project":[134],"them":[135],"into":[136],"score":[138,151],"latent":[139],"space":[140],"lightweight":[143],"decoder.":[144],"Then,":[145],"construct":[147],"target":[152,163],"calculate":[154],"distance":[156],"between":[157],"decoder's":[159],"output":[160],"as":[164],"additional":[166,228],"loss.":[168,186],"Finally,":[169],"jointly":[171],"optimize":[172],"decoder":[177],"both":[179],"standard":[180],"diffusion":[181],"loss":[182],"our":[184],"Experiments":[187],"across":[188],"various":[189],"image/video":[190],"tasks,":[191],"architectures,":[192],"diffusion/flow":[194],"models":[195],"demonstrated":[196],"LISA":[198],"can":[199],"not":[200],"only":[201],"consistently":[202],"accelerate":[203],"convergence":[206],"improve":[208],"final":[209],"synthetic":[210],"results,":[211],"but":[212],"also":[213],"encourage":[214],"network's":[217],"be":[220],"more":[221],"disentangled":[222],"for":[223],"modeling":[225],"negligible":[227],"cost":[230],"zero":[232],"extra":[233],"inference":[234],"cost.":[235]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-27T00:00:00"}
