{"id":"https://openalex.org/W7163573722","doi":"https://doi.org/10.48550/arxiv.2606.04493","title":"SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning","display_name":"SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163573722","doi":"https://doi.org/10.48550/arxiv.2606.04493"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04493","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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.04493","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137916796","display_name":"Zhihua Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhihua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137820724","display_name":"Yanping Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yanping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137874627","display_name":"Yizhang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yizhang","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.2524999976158142,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.2524999976158142,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11400000005960464,"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":0.07699999958276749,"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/graph","display_name":"Graph","score":0.5166000127792358},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5013999938964844},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.459199994802475},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.412200003862381},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.388700008392334},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.38499999046325684},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3797999918460846},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.3149000108242035},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.31369999051094055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5587000250816345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.38499999046325684},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3310000002384186},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04493","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.2606.04493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04493","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":[{"id":"https://metadata.un.org/sdg/10","score":0.7104398012161255,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Correspondence":[0],"pruning":[1,96],"aims":[2],"to":[3,30,53,66,129,170],"identify":[4],"inliers":[5,68],"from":[6,24,69],"an":[7],"initial":[8],"set":[9],"of":[10,103,133,183],"correspondences.":[11],"Most":[12],"existing":[13],"Graph":[14],"Neural":[15],"Network":[16],"(GNN)-based":[17],"methods":[18,41,209],"rely":[19],"on":[20,210],"geometric":[21,34,135],"features":[22,57],"mapped":[23],"coarse":[25],"Euclidean":[26],"coordinates,":[27],"which":[28],"struggle":[29],"capture":[31,132],"the":[32,59,83,131,160,164,181],"subtle":[33,134],"consistencies":[35,136],"presented":[36],"by":[37,168],"inliers.":[38],"While":[39],"Mamba-based":[40,93],"possess":[42],"global":[43,193],"receptive":[44],"fields":[45],"and":[46,86,124,137,179,190],"long":[47],"sequence":[48],"modeling":[49,195],"capabilities,":[50],"they":[51],"tend":[52],"accumulate":[54],"substantial":[55],"inconsistent":[56,184],"within":[58,159,176],"hidden":[60,177],"state":[61,161],"space,":[62,162],"making":[63],"it":[64],"difficult":[65],"distinguish":[67],"outliers.":[70],"In":[71],"this":[72,80],"paper,":[73],"we":[74,107,145],"integrate":[75],"frequency":[76,156,165],"domain":[77],"perception":[78],"into":[79,120],"task":[81],"for":[82],"first":[84],"time":[85],"propose":[87],"SFMambaNet,":[88],"a":[89,109,147,155],"novel":[90],"Spectral-Frequency":[91],"enhanced":[92],"two-view":[94],"correspondence":[95],"network.":[97],"Our":[98],"method":[99],"is":[100,216],"collaboratively":[101],"composed":[102],"two":[104],"components:":[105],"First,":[106],"design":[108,146],"Local":[110],"Spectral-Geometric":[111],"Attention":[112],"(LSGA)":[113],"block.":[114,152],"LSGA":[115,169],"incorporates":[116],"spectral":[117],"positional":[118],"encoding":[119],"local":[121,139],"graph":[122],"interactions":[123],"introduces":[125],"multi-scale":[126],"Mamba":[127,150],"processing":[128],"enhance":[130],"improve":[138],"feature":[140],"discriminability.":[141],"Building":[142],"upon":[143],"this,":[144],"Spectral-Integrated":[148],"Global":[149],"(SIGM)":[151],"SIGM":[153],"embeds":[154],"gating":[157],"mechanism":[158],"utilizing":[163],"information":[166],"provided":[167],"explicitly":[171],"suppress":[172],"high-frequency":[173],"noise":[174],"accumulation":[175],"states":[178],"mitigate":[180],"propagation":[182],"features.":[185],"This":[186],"enhances":[187],"inlier-outlier":[188],"separability":[189],"achieves":[191],"robust":[192],"context":[194],"capabilities":[196],"with":[197],"nearly":[198],"linear":[199],"complexity.":[200],"Extensive":[201],"experiments":[202],"demonstrate":[203],"that":[204],"SFMambaNet":[205],"outperforms":[206],"current":[207],"state-of-the-art":[208],"several":[211],"challenging":[212],"tasks.":[213],"The":[214],"code":[215],"available":[217],"at":[218],"https://github.com/Kirito14IT/SFMambaNet.":[219]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}
