{"id":"https://openalex.org/W7134891690","doi":"https://doi.org/10.48550/arxiv.2603.09696","title":"TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering","display_name":"TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134891690","doi":"https://doi.org/10.48550/arxiv.2603.09696"},"language":"en","primary_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/866aa700-cd24-4bcd-9bff-73d8731e1567","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/866aa700-cd24-4bcd-9bff-73d8731e1567","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Carlini, L, Lena, C, Hassan, C, Stoyanov, D, Momi, E D, Bano, S & Hoque, M I 2026 'TemporalDoRA : Temporal PEFT for Robust Surgical Video Question Answering' arXiv, pp. 1-10. https://doi.org/10.48550/arXiv.2603.09696","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.09696","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109668550","display_name":"Luca Carlini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlini, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066281103","display_name":"Chiara Lena","orcid":"https://orcid.org/0009-0003-0326-0342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lena, Chiara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128734561","display_name":"Cesare Hassan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hassan, Cesare","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128702974","display_name":"Danail Stoyanov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stoyanov, Danail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083795699","display_name":"Elena De Momi","orcid":"https://orcid.org/0000-0002-8819-2734"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Momi, Elena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071609030","display_name":"Sophia Bano","orcid":"https://orcid.org/0000-0003-1329-4565"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bano, Sophia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011069641","display_name":"M. I. Hoque","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoque, Mobarak I.","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":true,"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":0.9847999811172485,"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":0.9847999811172485,"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":0.005200000014156103,"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":0.0019000000320374966,"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/question-answering","display_name":"Question answering","score":0.6614999771118164},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6227999925613403},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.44839999079704285},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.430400013923645},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.38679999113082886},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.37209999561309814},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.3427000045776367},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.337799996137619},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.3361000120639801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7612000107765198},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6614999771118164},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6227999925613403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5943999886512756},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36660000681877136},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36160001158714294},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.2883000075817108},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2838999927043915},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/866aa700-cd24-4bcd-9bff-73d8731e1567","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/866aa700-cd24-4bcd-9bff-73d8731e1567","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Carlini, L, Lena, C, Hassan, C, Stoyanov, D, Momi, E D, Bano, S & Hoque, M I 2026 'TemporalDoRA : Temporal PEFT for Robust Surgical Video Question Answering' arXiv, pp. 1-10. https://doi.org/10.48550/arXiv.2603.09696","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2603.09696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09696","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.2603.09696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09696","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":[{"score":0.6048977971076965,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Surgical":[0],"Video":[1],"Question":[2],"Answering":[3],"(VideoQA)":[4],"requires":[5],"accurate":[6],"temporal":[7,50,68,170],"grounding":[8],"while":[9,103],"remaining":[10],"robust":[11],"to":[12,47,87,155,158,189],"natural":[13],"variation":[14],"in":[15],"how":[16],"clinicians":[17],"phrase":[18],"questions,":[19],"where":[20],"linguistic":[21,159],"bias":[22],"can":[23],"arise.":[24],"Standard":[25],"Parameter":[26],"Efficient":[27],"Fine":[28],"Tuning":[29],"(PEFT)":[30],"methods":[31],"adapt":[32],"pretrained":[33],"projections":[34],"without":[35],"explicitly":[36],"modeling":[37],"frame-to-frame":[38],"interactions":[39],"within":[40,116],"the":[41,73,77,88,94,117,173,177,197],"adaptation":[42,118],"pathway,":[43],"limiting":[44],"their":[45],"ability":[46],"exploit":[48],"sparse":[49],"evidence.":[51],"We":[52,183],"introduce":[53],"TemporalDoRA,":[54],"a":[55,105,142],"video-specific":[56],"PEFT":[57],"formulation":[58],"that":[59,169],"extends":[60],"Weight-Decomposed":[61],"Low-Rank":[62],"Adaptation":[63],"by":[64],"(i)":[65],"inserting":[66],"lightweight":[67],"Multi-Head":[69],"Attention":[70],"(MHA)":[71],"inside":[72,172],"low-rank":[74,90,174],"bottleneck":[75],"of":[76,180],"vision":[78],"encoder":[79],"and":[80,108,128,165,192,201],"(ii)":[81],"selectively":[82],"applying":[83],"weight":[84],"decomposition":[85],"only":[86],"trainable":[89],"branch":[91,175],"rather":[92],"than":[93],"full":[95],"adapted":[96,188],"weight.":[97],"This":[98],"design":[99],"enables":[100],"temporally-aware":[101],"updates":[102,122],"preserving":[104],"frozen":[106],"backbone":[107],"stable":[109],"scaling.":[110],"By":[111],"mixing":[112,171],"information":[113],"across":[114],"frames":[115],"subspace,":[119],"TemporalDoRA":[120,161],"steers":[121],"toward":[123],"temporally":[124],"consistent":[125,194],"visual":[126],"cues":[127],"improves":[129,162],"robustness":[130],"with":[131,146],"minimal":[132],"parameter":[133],"overhead.":[134],"To":[135],"benchmark":[136],"this":[137],"setting,":[138],"we":[139],"present":[140],"REAL-Colon-VQA,":[141],"colonoscopy":[143],"VideoQA":[144],"dataset":[145,202],"6,424":[147],"clip--question":[148],"pairs,":[149],"including":[150],"paired":[151],"rephrased":[152],"Out-of-Template":[153,163,198],"questions":[154],"evaluate":[156],"sensitivity":[157],"variation.":[160],"performance,":[164],"ablation":[166],"studies":[167],"confirm":[168],"is":[176],"primary":[178],"driver":[179],"these":[181],"gains.":[182],"also":[184],"validate":[185],"on":[186,196],"EndoVis18-VQA":[187],"short":[190],"clips":[191],"observe":[193],"improvements":[195],"split.":[199],"Code":[200],"available":[203],"at~\\href{https://anonymous.4open.science/r/TemporalDoRA-BFC8/}{Anonymous":[204],"GitHub}.":[205]},"counts_by_year":[],"updated_date":"2026-07-14T08:27:34.040176","created_date":"2026-03-12T00:00:00"}