{"id":"https://openalex.org/W4399418661","doi":"https://doi.org/10.1145/3651671.3651701","title":"SRCPT: Spatial Reconstruction Contrastive Pretext Task for Improving Few-Shot Image Classification","display_name":"SRCPT: Spatial Reconstruction Contrastive Pretext Task for Improving Few-Shot Image Classification","publication_year":2024,"publication_date":"2024-02-02","ids":{"openalex":"https://openalex.org/W4399418661","doi":"https://doi.org/10.1145/3651671.3651701"},"language":"en","primary_location":{"id":"doi:10.1145/3651671.3651701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5027259507","display_name":"Zhenbang Wang","orcid":"https://orcid.org/0009-0006-1802-4344"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenbang Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-1802-4344","affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080295555","display_name":"Pengfei Duan","orcid":"https://orcid.org/0000-0002-0912-0928"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Duan","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-0912-0928","affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101967703","display_name":"Yi Rong","orcid":"https://orcid.org/0000-0003-4867-6811"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Rong","raw_affiliation_strings":["Sanya Science and Education Innovation Park of Wuhan University of Technology, China and School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0003-4867-6811","affiliations":[{"raw_affiliation_string":"Sanya Science and Education Innovation Park of Wuhan University of Technology, China and School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027259507"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06234667,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"424","last_page":"432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9628000259399414,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9532999992370605,"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/computer-science","display_name":"Computer science","score":0.8182209134101868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7151596546173096},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5396803021430969},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5365291833877563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5233818292617798},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5178006887435913},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47756296396255493},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47215935587882996},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46205320954322815},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46135637164115906},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4481451213359833},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.425446480512619},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4116632640361786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8182209134101868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7151596546173096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5396803021430969},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5365291833877563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5233818292617798},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5178006887435913},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47756296396255493},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47215935587882996},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46205320954322815},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46135637164115906},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4481451213359833},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.425446480512619},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4116632640361786},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3651671.3651701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2042718506","https://openalex.org/W2115733720","https://openalex.org/W2163605009","https://openalex.org/W2194321275","https://openalex.org/W2432717477","https://openalex.org/W2604763608","https://openalex.org/W2742093937","https://openalex.org/W2768228940","https://openalex.org/W2787035179","https://openalex.org/W2798836702","https://openalex.org/W2904218366","https://openalex.org/W2919115771","https://openalex.org/W2932033800","https://openalex.org/W2950996604","https://openalex.org/W2965535361","https://openalex.org/W3012209922","https://openalex.org/W3013939433","https://openalex.org/W3034337759","https://openalex.org/W3036411233","https://openalex.org/W3108877122","https://openalex.org/W3109083691","https://openalex.org/W3115673813","https://openalex.org/W3134999934","https://openalex.org/W3137952714","https://openalex.org/W3159056052","https://openalex.org/W3174159092","https://openalex.org/W3177525997","https://openalex.org/W4285531802","https://openalex.org/W4298417552","https://openalex.org/W4312771536","https://openalex.org/W6735236233","https://openalex.org/W6743661861","https://openalex.org/W6751575707","https://openalex.org/W6758126075"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2088247287","https://openalex.org/W4309346246","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1,24,43,52,135],"(SSL)":[2],"has":[3],"been":[4],"widely":[5],"applied":[6],"in":[7,22,41,181],"the":[8,17,36,63,68,79,104,113,120,144,157],"pretraining":[9],"phase":[10],"of":[11,38,58,67,86,119,141,189],"models.":[12],"Among":[13],"these":[14,59],"SSL":[15],"methods,":[16],"various":[18],"data":[19,39],"augmentation":[20],"used":[21],"contrastive":[23,51,134],"for":[25,122],"constructing":[26,109],"positive":[27],"and":[28,76,83,126,152],"negative":[29],"sample":[30],"pairs":[31],"conveniently":[32],"contribute":[33],"to":[34,74,102,131,146,159,162],"alleviating":[35],"issue":[37],"scarcity":[40],"few-shot":[42,190],"(FSL)":[44],"tasks.":[45,55,193],"Therefore,":[46],"many":[47],"approaches":[48],"have":[49],"introduced":[50],"into":[53],"FSL":[54,105,139],"However,":[56],"most":[57],"methods":[60],"only":[61],"utilize":[62],"global":[64],"embedding":[65],"information":[66,82],"entire":[69],"image,":[70],"making":[71],"it":[72],"challenging":[73],"capture":[75,147],"fully":[77],"leverage":[78],"local":[80,117,153],"visual":[81],"structural":[84],"details":[85],"image":[87,121,191],"samples.":[88,169],"To":[89],"address":[90],"this,":[91],"we":[92],"proposes":[93],"a":[94,110,133,167],"novel":[95],"Spatial":[96],"Reconstruction":[97],"Contrastive":[98],"Pretext":[99],"Task":[100],"(SRCPT)":[101],"enhance":[103],"training":[106],"objective.":[107,136],"By":[108],"two-branch":[111],"network,":[112],"model":[114,145,158],"can":[115],"use":[116],"patches":[118],"feature":[123,154],"map":[124],"reconstruction":[125,129],"employ":[127],"spatial":[128,150],"weights":[130],"create":[132],"The":[137],"enhanced":[138],"objective":[140],"SRCPT":[142,176],"encourages":[143],"more":[148],"transferable":[149],"structures":[151],"information,":[155],"enabling":[156],"adapt":[160],"well":[161],"new":[163],"categories":[164],"even":[165],"with":[166],"few":[168],"Extensive":[170],"experiments":[171],"demonstrate":[172],"that":[173],"our":[174],"proposed":[175],"method":[177],"achieves":[178],"state-of-the-art":[179],"performance":[180],"three":[182,187],"popular":[183],"benchmark":[184],"datasets":[185],"across":[186],"types":[188],"classification":[192]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}