{"id":"https://openalex.org/W7161061957","doi":"https://doi.org/10.48550/arxiv.2605.12145","title":"Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations","display_name":"Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161061957","doi":"https://doi.org/10.48550/arxiv.2605.12145"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12145","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.12145","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058913842","display_name":"S. N. Sen","orcid":"https://orcid.org/0009-0000-7486-2613"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sen, Souptik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079990847","display_name":"Raneen Younis","orcid":"https://orcid.org/0000-0002-0403-6495"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Younis, Raneen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136025430","display_name":"Zahra Ahmadi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmadi, Zahra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.42160001397132874,"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":0.42160001397132874,"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":0.09269999712705612,"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":0.05180000141263008,"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/generalization","display_name":"Generalization","score":0.7488999962806702},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6753000020980835},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.6128000020980835},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4853000044822693},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.45410001277923584},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.40220001339912415},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3504999876022339}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7488999962806702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7014999985694885},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6753000020980835},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6128000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690000057220459},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.40220001339912415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36489999294281006},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28769999742507935},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12145","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12145","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12145","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":"article"},"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":{"Multimodal":[0],"learning":[1],"seeks":[2],"to":[3,14,75],"integrate":[4],"information":[5],"across":[6,64],"diverse":[7],"sensory":[8],"sources,":[9],"yet":[10],"current":[11],"approaches":[12,34],"struggle":[13],"balance":[15],"cross-modal":[16,82,111,134],"generalizability":[17],"with":[18,123],"modality-specific":[19,65],"structure.":[20],"Continuous":[21],"(implicit)":[22],"methods":[23],"preserve":[24,76],"fine-grained":[25,100],"priors":[26],"but":[27],"render":[28],"generalization":[29],"challenging,":[30],"while":[31,79],"discrete":[32,87,160],"(explicit)":[33],"enforce":[35],"shared":[36],"prototypes":[37],"at":[38],"the":[39],"expense":[40],"of":[41],"modality":[42],"specificity.":[43],"We":[44],"introduce":[45],"CoDAAR":[46,74,89,131,151],"(Cross-modal":[47],"Discrete":[48,94],"Alignment":[49,96,106],"And":[50],"Reconstruction),":[51],"a":[52,85,117,156],"novel":[53],"framework":[54],"that":[55],"resolves":[56],"this":[57],"long-standing":[58],"trade-off":[59],"by":[60],"establishing":[61,155],"semantic":[62,112],"consensus":[63],"codebooks":[66],"through":[67],"index-level":[68],"alignment.":[69],"This":[70],"design":[71],"uniquely":[72],"allows":[73],"modality-unique":[77],"structures":[78],"achieving":[80],"generalizable":[81,162],"representations":[83],"within":[84],"unified":[86,119],"space.":[88,121],"combines":[90],"two":[91],"complementary":[92],"mechanisms:":[93],"Temporal":[95],"(DTA),":[97],"which":[98,108],"enables":[99],"temporal":[101],"quantization,":[102],"and":[103,135,148,161],"Cascading":[104],"Semantic":[105],"(CSA),":[107],"promotes":[109],"progressive":[110],"agreement.":[113],"Together,":[114],"they":[115],"establish":[116],"competition-free":[118],"representation":[120,164],"Trained":[122],"self-supervised":[124],"reconstruction":[125],"objectives":[126],"on":[127],"paired":[128],"multimodal":[129,163],"sequences,":[130],"demonstrates":[132],"robust":[133],"cross-domain":[136],"generalization.":[137],"Across":[138],"Cross-Modal":[139],"Generalization":[140],"benchmarks,":[141],"including":[142],"event":[143],"classification,":[144],"localization,":[145],"video":[146],"segmentation,":[147],"cross-dataset":[149],"transfer,":[150],"achieves":[152],"state-of-the-art":[153],"performance,":[154],"new":[157],"paradigm":[158],"for":[159],"learning.":[165]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-14T00:00:00"}
