{"id":"https://openalex.org/W7140180594","doi":"https://doi.org/10.48550/arxiv.2603.21115","title":"LiFR-Seg: Anytime High-Frame-Rate Segmentation via Event-Guided Propagation","display_name":"LiFR-Seg: Anytime High-Frame-Rate Segmentation via Event-Guided Propagation","publication_year":2026,"publication_date":"2026-03-22","ids":{"openalex":"https://openalex.org/W7140180594","doi":"https://doi.org/10.48550/arxiv.2603.21115"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21115","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.2603.21115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wu, Xiaoshan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Xiaoshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lyu, Xiaoyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, Xiaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yu, Yifei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yifei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Bo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Zhongrui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhongrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Qi, Xiaojuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Xiaojuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.44859999418258667,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.44859999418258667,"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":0.18649999797344208,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.07100000232458115,"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":0.6718999743461609},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5343000292778015},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.5239999890327454},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5022000074386597},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.47530001401901245},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.44909998774528503},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4408000111579895},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4397999942302704},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42149999737739563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.800599992275238},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6718999743461609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6491000056266785},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5364999771118164},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5022000074386597},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4397999942302704},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C39394851","wikidata":"https://www.wikidata.org/wiki/Q921594","display_name":"Inter frame","level":4,"score":0.35519999265670776},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2709999978542328}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21115","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.2603.21115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21115","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dense":[0],"semantic":[1,68,105],"segmentation":[2,38],"in":[3,87,138],"dynamic":[4,89,139],"environments":[5],"is":[6,114,169],"fundamentally":[7],"limited":[8],"by":[9,102,120],"the":[10,146,183],"low-frame-rate":[11,199],"(LFR)":[12],"nature":[13],"of":[14,53,111],"standard":[15],"cameras,":[16],"which":[17],"creates":[18],"critical":[19],"perceptual":[20],"gaps":[21],"between":[22],"frames.":[23],"To":[24],"solve":[25],"this,":[26],"we":[27,156],"introduce":[28],"Anytime":[29],"Interframe":[30],"Semantic":[31],"Segmentation:":[32],"a":[33,45,51,60,71,94,150,189],"new":[34,151],"task":[35,58],"for":[36,193],"predicting":[37],"at":[39],"any":[40],"arbitrary":[41],"time":[42],"using":[43,70],"only":[44],"single":[46],"past":[47],"RGB":[48],"frame":[49],"and":[50,77,125,149],"stream":[52],"asynchronous":[54],"event":[55,80],"data.":[56],"This":[57,186],"presents":[59,188],"core":[61,110],"challenge:":[62],"how":[63],"to":[64,182],"robustly":[65],"propagate":[66],"dense":[67],"features":[69,106],"motion":[72,123],"field":[73,124],"derived":[74],"from":[75,172],"sparse":[76],"often":[78],"noisy":[79],"data,":[81],"all":[82],"while":[83],"mitigating":[84],"feature":[85],"degradation":[86],"highly":[88],"scenes.":[90],"We":[91,141],"propose":[92],"LiFR-Seg,":[93],"novel":[95],"framework":[96],"that":[97,168,178],"directly":[98],"addresses":[99],"these":[100],"challenges":[101],"propagating":[103],"deep":[104],"through":[107],"time.":[108],"The":[109],"our":[112,143,159],"method":[113,144],"an":[115,121,173],"uncertainty-aware":[116],"warping":[117],"process,":[118],"guided":[119],"event-driven":[122],"its":[126],"learned,":[127],"explicit":[128],"confidence.":[129],"A":[130],"temporal":[131],"memory":[132],"attention":[133],"module":[134],"further":[135],"ensures":[136],"coherence":[137],"scenarios.":[140],"validate":[142],"on":[145,166],"DSEC":[147],"dataset":[148],"high-frequency":[152],"synthetic":[153],"benchmark":[154],"(SHF-DSEC)":[155],"contribute.":[157],"Remarkably,":[158],"LFR":[160],"system":[161],"achieves":[162],"performance":[163],"(73.82%":[164],"mIoU":[165],"DSEC)":[167],"statistically":[170],"indistinguishable":[171],"HFR":[174],"upper-bound":[175],"(within":[176],"0.09%)":[177],"has":[179],"full":[180],"access":[181],"target":[184],"frame.":[185],"work":[187],"new,":[190],"efficient":[191],"paradigm":[192],"achieving":[194],"robust,":[195],"high-frame-rate":[196],"perception":[197],"with":[198],"hardware.":[200],"Project":[201],"Page:":[202],"https://candy-crusher.github.io/LiFR_Seg_Proj/#;":[203],"Code:":[204],"https://github.com/Candy-Crusher/LiFR-Seg.git.":[205]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-25T00:00:00"}
