{"id":"https://openalex.org/W7161033989","doi":"https://doi.org/10.48550/arxiv.2605.11208","title":"Hi-GaTA: Hierarchical Gated Temporal Aggregation Adapter for Surgical Video Report Generation","display_name":"Hi-GaTA: Hierarchical Gated Temporal Aggregation Adapter for Surgical Video Report Generation","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7161033989","doi":"https://doi.org/10.48550/arxiv.2605.11208"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11208","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.11208","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136065017","display_name":"Kedi Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Kedi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136075666","display_name":"Chaohui Dang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Chaohui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136075066","display_name":"Yue Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136072772","display_name":"James Glasbey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glasbey, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026657286","display_name":"Theodoros N. Arvanitis","orcid":"https://orcid.org/0000-0001-5473-135X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arvanitis, Theodoros N.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136031637","display_name":"Le Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Le","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9564999938011169,"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":0.9564999938011169,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.010300000198185444,"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":0.007000000216066837,"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/adapter","display_name":"Adapter (computing)","score":0.6413999795913696},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.579200029373169},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45159998536109924},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3952000141143799},{"id":"https://openalex.org/keywords/overlay","display_name":"Overlay","score":0.3797000050544739},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.37299999594688416},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.36480000615119934},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.34209999442100525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7739999890327454},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.6413999795913696},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734000205993652},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46540001034736633},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C136085584","wikidata":"https://www.wikidata.org/wiki/Q910289","display_name":"Overlay","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C2778594517","wikidata":"https://www.wikidata.org/wiki/Q2260058","display_name":"Chills","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11208","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.11208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11208","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"Automated,":[0],"clinician-grade":[1],"assessment":[2],"reports":[3],"for":[4,66],"surgical":[5,50,67,111,154],"procedures":[6],"could":[7],"reduce":[8],"documentation":[9],"burden":[10],"and":[11,31,128,136,151],"provide":[12],"objective":[13],"feedback,":[14],"yet":[15],"remain":[16],"challenging":[17],"due":[18],"to":[19,113,148],"the":[20,32,143,165,184],"difficulty":[21],"of":[22,34,109,186],"aligning":[23],"dense":[24],"spatio-temporal":[25,116],"video":[26,68,82,104],"representations":[27],"with":[28,53,124,169],"language-based":[29],"reasoning":[30],"scarcity":[33],"high-quality,":[35],"privacy-preserving":[36],"datasets.":[37],"To":[38],"address":[39],"this":[40,59],"gap,":[41],"we":[42,61,98,141],"establish":[43],"a":[44,63,73,101,121],"benchmark":[45],"comprising":[46],"214":[47],"high-quality":[48],"simulated":[49],"videos":[51,112],"paired":[52],"surgeon-authored":[54],"evaluation":[55],"reports.":[56],"Building":[57],"on":[58,106],"resource,":[60],"propose":[62],"Perception-Alignment-Reasoning":[64],"framework":[65],"report":[69,155],"generation,":[70],"featuring":[71],"Hi-GaTA,":[72],"novel":[74],"lightweight":[75],"temporal":[76,92,122],"adapter":[77],"that":[78],"efficiently":[79],"compresses":[80],"long":[81],"sequences":[83],"into":[84],"compact,":[85],"LLM-compatible":[86],"visual":[87,96],"prefix":[88],"tokens":[89],"through":[90,132],"short-to-long-range":[91],"aggregation.":[93],"For":[94],"robust":[95],"perception,":[97],"pretrain":[99],"Sur40k,":[100],"surgical-specific":[102],"ViViT-style":[103],"encoder":[105],"40,000":[107],"minutes":[108],"public":[110],"capture":[114],"fine-grained":[115],"procedural":[117],"priors.":[118],"Hi-GaTA":[119],"employs":[120],"pyramid":[123],"text-conditioned":[125],"dual":[126],"cross-attention,":[127],"improves":[129],"multi-scale":[130],"consistency":[131],"cross-level":[133],"gated":[134],"fusion":[135],"an":[137],"increasing-depth":[138],"strategy.":[139],"Finally,":[140],"fine-tune":[142],"LLM":[144],"backbone":[145],"using":[146],"LoRA":[147],"enable":[149],"coherent":[150],"stylistically":[152],"consistent":[153,170],"generation":[156],"under":[157],"limited":[158],"supervision.":[159],"Experiments":[160],"show":[161],"our":[162],"approach":[163],"achieves":[164],"best":[166],"overall":[167],"performance,":[168],"gains":[171],"over":[172],"strong":[173],"Multimodal":[174],"Large":[175],"Language":[176],"Model":[177],"(MLLM)":[178],"baselines.":[179],"Ablation":[180],"studies":[181],"further":[182],"validate":[183],"effectiveness":[185],"each":[187],"proposed":[188],"component.":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-14T00:00:00"}