{"id":"https://openalex.org/W7155394506","doi":"https://doi.org/10.48550/arxiv.2604.20606","title":"Beyond ZOH: Advanced Discretization Strategies for Vision Mamba","display_name":"Beyond ZOH: Advanced Discretization Strategies for Vision Mamba","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155394506","doi":"https://doi.org/10.48550/arxiv.2604.20606"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20606","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20606","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.20606","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116248712","display_name":"Fady Ibrahim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ibrahim, Fady","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134367156","display_name":"Guangjun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Guangjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134372885","display_name":"Guanghui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guanghui","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/T10036","display_name":"Advanced Neural Network Applications","score":0.19269999861717224,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.19269999861717224,"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":0.14659999310970306,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.10339999943971634,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.8077999949455261},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5095000267028809},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4991999864578247},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43230000138282776},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.42739999294281006},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41850000619888306},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.37229999899864197},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.365200012922287}],"concepts":[{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.8077999949455261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5741999745368958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5658000111579895},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5095000267028809},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41850000619888306},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3228999972343445},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31040000915527344},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3075000047683716},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26510000228881836},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20606","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20606","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20606","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20606","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Vision":[0,60],"Mamba,":[1],"as":[2,164],"a":[3,9,48],"state":[4],"space":[5],"model":[6],"(SSM),":[7],"employs":[8],"zero-order":[10],"hold":[11,65,74],"(ZOH)":[12],"discretization,":[13],"which":[14],"assumes":[15],"that":[16,105],"input":[17],"signals":[18],"remain":[19],"constant":[20],"between":[21,140],"sampling":[22],"instants.":[23],"This":[24],"assumption":[25],"degrades":[26],"temporal":[27],"fidelity":[28],"in":[29,94,113,152],"dynamic":[30],"visual":[31,88],"environments":[32],"and":[33,50,76,99,107,142,156],"constrains":[34],"the":[35,59,77,110,116,124,136,147,165],"attainable":[36],"accuracy":[37,114],"of":[38,53,118,150],"modern":[39],"SSM-based":[40,153],"vision":[41,154],"models.":[42,172],"In":[43,122],"this":[44],"paper,":[45],"we":[46],"present":[47],"systematic":[49],"controlled":[51],"comparison":[52],"six":[54],"discretization":[55,151,167],"schemes":[56],"instantiated":[57],"within":[58],"Mamba":[61],"framework:":[62],"ZOH,":[63],"first-order":[64],"(FOH),":[66],"bilinear/Tustin":[67],"transform":[68],"(BIL),":[69],"polynomial":[70],"interpolation":[71],"(POL),":[72],"higher-order":[73],"(HOH),":[75],"fourth-order":[78],"Runge-Kutta":[79],"method":[80,85],"(RK4).":[81],"We":[82],"evaluate":[83],"each":[84],"on":[86],"standard":[87],"benchmarks":[89],"to":[90],"quantify":[91],"its":[92],"influence":[93],"image":[95],"classification,":[96],"semantic":[97],"segmentation,":[98],"object":[100],"detection.":[101],"Our":[102],"results":[103],"demonstrate":[104],"POL":[106],"HOH":[108],"yield":[109],"largest":[111],"gains":[112],"at":[115],"cost":[117],"higher":[119],"training-time":[120],"computation.":[121],"contrast,":[123],"BIL":[125,163],"provides":[126],"consistent":[127],"improvements":[128],"over":[129],"ZOH":[130],"with":[131],"modest":[132],"additional":[133],"overhead,":[134],"offering":[135],"most":[137],"favorable":[138],"trade-off":[139],"precision":[141],"efficiency.":[143],"These":[144],"findings":[145],"elucidate":[146],"pivotal":[148],"role":[149],"architectures":[155],"furnish":[157],"empirically":[158],"grounded":[159],"justification":[160],"for":[161,169],"adopting":[162],"default":[166],"baseline":[168],"state-of-the-art":[170],"SSM":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}
