{"id":"https://openalex.org/W7130547479","doi":"https://doi.org/10.1109/tip.2026.3663882","title":"Scale-Invariant Feature Matching Network for V-D-T Few-Shot Semantic Segmentation","display_name":"Scale-Invariant Feature Matching Network for V-D-T Few-Shot Semantic Segmentation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7130547479","doi":"https://doi.org/10.1109/tip.2026.3663882","pmid":"https://pubmed.ncbi.nlm.nih.gov/41712390"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2026.3663882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2026.3663882","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126420563","display_name":"Xiaofei Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofei Zhou","raw_affiliation_strings":["School of Automation, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7977-9728","affiliations":[{"raw_affiliation_string":"School of Automation, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126433767","display_name":"Jia Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Lin","raw_affiliation_strings":["School of Automation, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0004-9710-2456","affiliations":[{"raw_affiliation_string":"School of Automation, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dongmei Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongmei Chen","raw_affiliation_strings":["School of Automation, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126379118","display_name":"Deyang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I46482218","display_name":"Anqing Normal University","ror":"https://ror.org/0127ytz78","country_code":"CN","type":"education","lineage":["https://openalex.org/I46482218"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyang Liu","raw_affiliation_strings":["School of Computer and Information, Anqing Normal University, Anqing, China"],"raw_orcid":"https://orcid.org/0000-0001-7991-8735","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anqing Normal University, Anqing, China","institution_ids":["https://openalex.org/I46482218"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiyong Zhang","orcid":"https://orcid.org/0000-0001-9600-8477"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyong Zhang","raw_affiliation_strings":["School of Automation, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9600-8477","affiliations":[{"raw_affiliation_string":"School of Automation, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122871275","display_name":"Runmin Cong","orcid":null},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runmin Cong","raw_affiliation_strings":["School of Control Science and Engineering, Shandong University, Jinan, China"],"raw_orcid":"https://orcid.org/0000-0003-0972-4008","affiliations":[{"raw_affiliation_string":"School of Control Science and Engineering, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.8969,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96241455,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"35","issue":null,"first_page":"2198","last_page":"2209"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5464000105857849,"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":0.5464000105857849,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.1111999973654747,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.04569999873638153,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6596999764442444},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5917999744415283},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5595999956130981},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4699000120162964},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4668000042438507},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.44269999861717224},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4320000112056732},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4059999883174896},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7932000160217285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7621999979019165},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6596999764442444},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5917999744415283},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45910000801086426},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4320000112056732},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.35179999470710754},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.32100000977516174},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.25600001215934753}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2026.3663882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2026.3663882","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:41712390","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41712390","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1587805876","display_name":null,"funder_award_id":"62171002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1914386590","display_name":null,"funder_award_id":"62471278","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7062312508","display_name":null,"funder_award_id":"ZCLZ24F0201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7596411858","display_name":null,"funder_award_id":"62271180","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8978989278","display_name":null,"funder_award_id":"62276086","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-modal":[0],"few-shot":[1,96],"semantic":[2,97],"segmentation":[3,242],"(FSS)":[4],"aims":[5],"to":[6,59,92,141,162,187,225],"perform":[7],"dense":[8],"prediction":[9,204],"from":[10],"multiple":[11,44],"modality":[12,33],"images":[13],"including":[14],"visible":[15],"image,":[16,18],"depth":[17,190],"and":[19,53,89,117,124,150,234,237,257],"thermal":[20,118],"image":[21,191],"with":[22,192,268],"a":[23,68,81,85,90,134,193,211,220,269],"few":[24],"annotated":[25],"samples.":[26],"However,":[27],"some":[28],"efforts":[29],"treat":[30],"the":[31,40,47,54,101,105,121,125,130,143,151,155,167,171,176,182,189,197,201,216,228,240,253,258,265],"three":[32],"information":[34],"equally,":[35],"where":[36,154],"they":[37],"don't":[38],"incorporate":[39],"inherent":[41],"differences":[42],"among":[43],"modalities.":[45],"Besides,":[46],"objects":[48],"vary":[49],"in":[50,100,129,175,210,215],"size":[51],"greatly,":[52],"cutting-edge":[55],"matching":[56,72,83,132],"paradigms":[57],"fail":[58],"establish":[60],"an":[61,79],"effective":[62],"support-query":[63],"connection.":[64],"Therefore,":[65],"we":[66,111,180,218],"propose":[67],"novel":[69],"scale-invariant":[70,164],"feature":[71,82,86,116,131,149,177],"network":[73],"(i.e.,":[74],"SFM-Net),":[75],"which":[76,206],"consists":[77],"of":[78,107],"encoder,":[80],"block,":[84,88,133,179],"elevation":[87,178],"decoder,":[91,217],"conduct":[93],"visible-depth-thermal":[94],"(V-D-T)":[95],"segmentation.":[98],"Firstly,":[99],"encoder":[102],"part,":[103],"after":[104],"extraction":[106],"multi-level":[108],"initial":[109],"features,":[110],"fuse":[112],"each":[113,146],"level's":[114,147],"RGB":[115],"feature,":[119,153],"yielding":[120,200],"support":[122,148],"features":[123],"query":[126,152,172],"features.":[127],"Secondly,":[128],"pixel-to-patch":[135,156],"cross-attention":[136,198],"(PTPCA)":[137],"module":[138,186],"is":[139,207],"deployed":[140],"explore":[142,227],"correlation":[144],"between":[145,230],"pooling":[157],"(PTP-pool)":[158],"units":[159],"are":[160,250],"designed":[161],"build":[163],"relationships,":[165],"generating":[166],"coarse":[168,194,203],"mask":[169,195],"for":[170],"image.":[173],"Thirdly,":[174],"employ":[181],"prior-related":[183],"fusion":[184],"(PF)":[185],"integrate":[188],"via":[196,244],"mechanism,":[199],"enhanced":[202],"result,":[205],"further":[208,238],"aggregated":[209],"bottom-up":[212],"way.":[213],"Finally,":[214],"deploy":[219],"reverse":[221],"attention":[222],"(RA)":[223],"unit":[224],"gradually":[226],"complementarity":[229],"object":[231],"internal":[232],"regions":[233],"spatial":[235],"details,":[236],"generate":[239],"final":[241],"results":[243,259],"conventional":[245],"convolution":[246],"layers.":[247],"Extensive":[248],"experiments":[249],"conducted":[251],"on":[252],"VDT-2048-":[254],"$5^{i}$":[255],"dataset,":[256],"show":[260],"that":[261],"our":[262],"model":[263],"outperforms":[264],"state-of-the-art":[266],"methods":[267],"large":[270],"margin.":[271]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-02-25T06:17:34.324206","created_date":"2026-02-20T00:00:00"}
