{"id":"https://openalex.org/W7165662584","doi":"https://doi.org/10.48550/arxiv.2606.22604","title":"A Theory-grounded Hybrid Neural Network Integrating Complementary Estimation Mechanisms for Stable Visual Object TrackingA","display_name":"A Theory-grounded Hybrid Neural Network Integrating Complementary Estimation Mechanisms for Stable Visual Object TrackingA","publication_year":2026,"publication_date":"2026-06-21","ids":{"openalex":"https://openalex.org/W7165662584","doi":"https://doi.org/10.48550/arxiv.2606.22604"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22604","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.2606.22604","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139154921","display_name":"Yancheng Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yancheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060331822","display_name":"Hanle Zheng","orcid":"https://orcid.org/0009-0002-9622-780X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Hanle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139130660","display_name":"Lei Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139149019","display_name":"Yujie Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yujie","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.6593000292778015,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.6593000292778015,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.10130000114440918,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.020899999886751175,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.666700005531311},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4555000066757202},{"id":"https://openalex.org/keywords/hybrid-system","display_name":"Hybrid system","score":0.43959999084472656},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3889999985694885},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.373199999332428},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3702000081539154},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.3544999957084656},{"id":"https://openalex.org/keywords/hybrid-neural-network","display_name":"Hybrid neural network","score":0.3424000144004822},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3409000039100647}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988999843597412},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.666700005531311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6208999752998352},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38029998540878296},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.373199999332428},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.3346000015735626},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C164380108","wikidata":"https://www.wikidata.org/wiki/Q507187","display_name":"Attractor","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22604","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.2606.22604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22604","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":{"Hybrid":[0],"neural":[1,7,12,49,56,95],"networks":[2,8,13,50],"(HNNs)":[3],"that":[4],"integrate":[5],"artificial":[6],"(ANNs)":[9],"with":[10,113],"brain-inspired":[11],"have":[14],"achieved":[15],"broad":[16],"success":[17,28],"across":[18,164],"perception":[19],"and":[20,88,161,173,193],"control":[21],"tasks.":[22,44],"However,":[23],"much":[24],"of":[25],"the":[26,117,122,129],"current":[27],"is":[29],"confined":[30],"to":[31,41,59,65,126],"neuron-scale":[32],"hybridization,":[33],"where":[34],"discrete,":[35],"spike-based":[36],"coding":[37],"fundamentally":[38],"limits":[39],"applicability":[40],"continuous-state":[42,104],"estimation":[43,105],"In":[45,78],"neuroscience,":[46],"continuous":[47,53],"attractor":[48],"(CANNs)":[51],"represent":[52],"states":[54],"through":[55,128],"ensembles,":[57],"pointing":[58],"a":[60,83,92,102,136,203],"population-scale":[61,210],"route":[62],"for":[63,72,98,206],"HNNs":[64,208],"address":[66],"this":[67,79,156,197],"limitation.":[68],"Yet,":[69],"principled":[70],"methodologies":[71],"ANN-CANN":[73,85],"integration":[74],"remain":[75],"largely":[76],"underexplored.":[77],"work,":[80],"we":[81,134],"propose":[82],"theory-grounded":[84],"hybridization":[86],"framework":[87,108,201],"instantiate":[89],"it":[90],"as":[91],"hybrid":[93,175],"tracking":[94,163,167],"network":[96],"(HTNN)":[97],"visual":[99,166],"object":[100],"tracking,":[101],"representative":[103],"task.":[106],"The":[107],"aligns":[109],"ANN":[110],"response":[111],"maps":[112],"CANN":[114,147],"dynamics":[115],"in":[116],"same":[118],"state":[119,131],"space,":[120],"enabling":[121],"two":[123],"heterogeneous":[124],"branches":[125],"interact":[127],"shared":[130],"representation.":[132],"Furthermore,":[133],"uncover":[135],"functional":[137],"bias-variance":[138],"complementarity:":[139],"data-driven":[140],"ANNs":[141],"provide":[142],"asymptotically":[143],"unbiased":[144],"estimates,":[145],"while":[146],"estimates":[148],"are":[149,181],"low-variance":[150],"but":[151],"temporally":[152],"lagged.":[153],"By":[154],"operationalizing":[155],"complementarity,":[157],"HTNN":[158],"achieves":[159],"stable":[160],"accurate":[162],"nine":[165],"benchmarks,":[168],"consistently":[169],"outperforming":[170],"single-network":[171],"baselines":[172],"existing":[174],"models.":[176],"Notably,":[177],"these":[178],"performance":[179],"gains":[180],"robustly":[182],"maintained":[183],"even":[184],"under":[185],"diverse":[186],"environmental":[187],"variations,":[188],"including":[189],"occlusion,":[190],"motion":[191],"blur,":[192],"background":[194],"interference.":[195],"Through":[196],"proof-of-concept":[198],"study,":[199],"our":[200],"offers":[202],"generalizable":[204],"foundation":[205],"advancing":[207],"toward":[209],"hybridization.":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
