{"id":"https://openalex.org/W7165656933","doi":"https://doi.org/10.48550/arxiv.2606.23473","title":"C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model","display_name":"C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165656933","doi":"https://doi.org/10.48550/arxiv.2606.23473"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23473","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23473","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139149741","display_name":"Wei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139207727","display_name":"Jingyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jingyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139214686","display_name":"Guoan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guoan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101263879","display_name":"Junzhi Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Junzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139168248","display_name":"Yang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139206391","display_name":"Guang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Guang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022846767","display_name":"Lixu Gu","orcid":"https://orcid.org/0000-0002-6210-4847"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Lixu","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6550999879837036,"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":0.6550999879837036,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09070000052452087,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.049800001084804535,"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/segmentation","display_name":"Segmentation","score":0.6610000133514404},{"id":"https://openalex.org/keywords/synchronizing","display_name":"Synchronizing","score":0.5562000274658203},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4383000135421753},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4104999899864197},{"id":"https://openalex.org/keywords/synchronization","display_name":"Synchronization (alternating current)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.399399995803833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7648000121116638},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6610000133514404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6269000172615051},{"id":"https://openalex.org/C162932704","wikidata":"https://www.wikidata.org/wiki/Q1058791","display_name":"Synchronizing","level":3,"score":0.5562000274658203},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45739999413490295},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4383000135421753},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.4050999879837036},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4000000059604645},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35040000081062317},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2822999954223633},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2671999931335449},{"id":"https://openalex.org/C130727458","wikidata":"https://www.wikidata.org/wiki/Q1639109","display_name":"Coarticulation","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23473","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23473","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23473","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":"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":{"Universal":[0],"segmentation":[1,14,58],"models":[2],"exhibit":[3,51],"significant":[4],"potential":[5],"for":[6,96,108,153],"diverse":[7,162],"tasks":[8,29,50,81,160],"involving":[9],"different":[10],"imaging":[11],"modalities":[12,163],"and":[13,57,88,99,106,146,164],"objectives.":[15],"Task-Incremental":[16],"Learning":[17],"provides":[18],"a":[19,25,66,114,134,149,171],"privacy-preserving":[20],"approach":[21],"to":[22,82,141,178],"continually":[23],"evolve":[24],"universal":[26],"model":[27,37],"on":[28,39,45,158],"from":[30,188],"sequentially-arriving":[31],"medical":[32],"departments.":[33],"However,":[34],"training":[35,180],"the":[36,40,104,122,144,189],"solely":[38],"incoming":[41],"task":[42,183],"induces":[43],"forgetting":[44,84,187],"past":[46],"tasks,":[47],"since":[48],"consecutive":[49],"concurrent":[52,86,190],"shifts":[53],"in":[54,174],"image":[55],"appearance":[56,87],"objective.":[59],"To":[60],"address":[61],"this":[62],"problem,":[63],"we":[64,112,132],"propose":[65,113],"novel":[67],"Coupled":[68],"Comprehensive":[69],"Generative":[70],"Replay":[71],"(C^2GR)":[72],"framework":[73],"that":[74,120,167],"simultaneously":[75,142],"synthesizes":[76],"image-mask":[77,94],"pairs":[78],"of":[79,103],"previous":[80],"mitigate":[83],"under":[85],"objective":[89],"shifts.":[90,191],"This":[91],"requires":[92],"preserving":[93],"correspondence":[95,123],"structure-realistic":[97],"generation":[98],"bridging":[100],"asynchronous":[101],"optimization":[102],"generator":[105,145],"segmentor":[107,147],"segmentation-oriented":[109],"generation.":[110],"Specifically,":[111],"Bayesian":[115],"Joint":[116],"Diffusion":[117],"(BJD)":[118],"method":[119],"formulates":[121],"as":[124],"conditional":[125,129],"distributions":[126],"optimized":[127],"via":[128,148],"denoising.":[130],"Furthermore,":[131],"develop":[133],"Relation-aware":[135],"Unified":[136],"Prompt":[137],"Synchronization":[138],"(RUPS)":[139],"scheme":[140],"modulate":[143],"shared":[150],"task-relation-aware":[151],"prompt":[152],"synchronizing":[154],"their":[155],"optimization.":[156],"Experiments":[157],"20":[159],"spanning":[161],"objectives":[165],"demonstrate":[166],"C^2GR":[168],"exhibits":[169],"only":[170],"2.44%":[172],"drop":[173],"overall":[175],"performance":[176],"compared":[177],"joint":[179],"with":[181],"all":[182],"data,":[184],"effectively":[185],"alleviating":[186],"Our":[192],"code":[193],"will":[194],"be":[195],"made":[196],"publicly":[197],"available":[198],"at":[199],"https://github.com/mar-cry/C2GR.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}
