{"id":"https://openalex.org/W7160949430","doi":"https://doi.org/10.48550/arxiv.2605.08839","title":"Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning","display_name":"Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160949430","doi":"https://doi.org/10.48550/arxiv.2605.08839"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08839","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.2605.08839","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124935476","display_name":"Zhen-Hao Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Zhen-Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135920382","display_name":"Yan Qing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135953753","display_name":"Hao Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135910635","display_name":"Han-Jia Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Han-Jia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135955100","display_name":"De-Chuan Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, De-Chuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135926063","display_name":"Da-Wei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Da-Wei","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":0.9354000091552734,"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":0.9354000091552734,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.02419999986886978,"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/T10028","display_name":"Topic Modeling","score":0.006399999838322401,"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/domain","display_name":"Domain (mathematical analysis)","score":0.5971999764442444},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5892999768257141},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.5856999754905701},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5192000269889832},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5091000199317932},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4706999957561493},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.42329999804496765},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4198000133037567}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595000267028809},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5892999768257141},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.5856999754905701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5809000134468079},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5192000269889832},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5091000199317932},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5084999799728394},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4706999957561493},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41909998655319214},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.3714999854564667},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.2985000014305115},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C2779178101","wikidata":"https://www.wikidata.org/wiki/Q6987274","display_name":"Negative transfer","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08839","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.2605.08839","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08839","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Class-Incremental":[0],"Learning":[1],"(CIL)":[2],"requires":[3,51],"a":[4,24,76,128],"learning":[5,119],"system":[6,25],"to":[7,36,56,62,79,102,112,157],"learn":[8],"new":[9],"classes":[10],"while":[11],"retaining":[12],"previously":[13],"learned":[14],"knowledge.":[15],"However,":[16],"in":[17,30,38],"real-world":[18],"scenarios":[19],"such":[20],"as":[21],"autonomous":[22],"driving,":[23],"trained":[26],"on":[27],"urban":[28],"roads":[29],"sunny":[31],"weather":[32,48],"may":[33],"later":[34],"need":[35],"operate":[37],"rural":[39],"or":[40],"highway":[41],"environments":[42],"with":[43],"different":[44],"traffic":[45],"patterns":[46],"and":[47,83],"conditions.":[49],"This":[50],"the":[52,118],"model":[53],"not":[54],"only":[55],"overcome":[57],"catastrophic":[58,84],"forgetting,":[59,125],"but":[60],"also":[61],"effectively":[63],"handle":[64],"domain":[65,81],"shifts.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70,90,108,126],"propose":[71,127],"CrOss-sample":[72],"Relational":[73],"Fusion":[74],"(CORF),":[75],"unified":[77],"framework":[78,131],"address":[80],"shift":[82],"forgetting":[85],"simultaneously.":[86],"To":[87,123],"enhance":[88,158],"generalizability,":[89,160],"perform":[91],"selective":[92],"refinement":[93],"of":[94,120],"training":[95],"samples":[96],"by":[97],"leveraging":[98],"spatial":[99],"contribution":[100],"maps":[101],"highlight":[103],"semantically":[104],"informative":[105],"regions.":[106],"Furthermore,":[107],"incorporate":[109],"predictive":[110],"confidence":[111],"adaptively":[113],"weigh":[114],"samples,":[115],"thereby":[116],"facilitating":[117],"domain-agnostic":[121],"representations.":[122],"alleviate":[124],"cascaded":[129],"distillation":[130],"that":[132],"captures":[133],"cross-sample":[134],"relational":[135],"dependencies":[136],"across":[137,164],"multiple":[138],"feature":[139],"hierarchies,":[140],"enabling":[141],"multi-grained":[142],"knowledge":[143],"transfer":[144],"from":[145],"previous":[146],"tasks.":[147],"CORF":[148],"can":[149],"be":[150],"seamlessly":[151],"integrated":[152],"into":[153],"existing":[154],"CIL":[155],"algorithms":[156],"their":[159],"achieving":[161],"competitive":[162],"performance":[163],"various":[165],"benchmark":[166],"datasets.":[167],"Code":[168],"is":[169],"available":[170],"at":[171],"https://github.com/LAMDA-CL/TMM26-CORF":[172],".":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}