{"id":"https://openalex.org/W4283274378","doi":"https://doi.org/10.1145/3529836.3529914","title":"Few-Shot Learning in Object Classification using Meta-Learning with Between-Class Attribute Transfer","display_name":"Few-Shot Learning in Object Classification using Meta-Learning with Between-Class Attribute Transfer","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283274378","doi":"https://doi.org/10.1145/3529836.3529914"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529914","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529914","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005823934","display_name":"Majed Alsadhan","orcid":null},"institutions":[{"id":"https://openalex.org/I189590672","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758","country_code":"US","type":"education","lineage":["https://openalex.org/I189590672"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Majed Alsadhan","raw_affiliation_strings":["computer science, Kansas State University, United States"],"affiliations":[{"raw_affiliation_string":"computer science, Kansas State University, United States","institution_ids":["https://openalex.org/I189590672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103860152","display_name":"William H. Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I189590672","display_name":"Kansas State University","ror":"https://ror.org/05p1j8758","country_code":"US","type":"education","lineage":["https://openalex.org/I189590672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William H. Hsu","raw_affiliation_strings":["computer science, Kansas State University, United States"],"affiliations":[{"raw_affiliation_string":"computer science, Kansas State University, United States","institution_ids":["https://openalex.org/I189590672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005823934"],"corresponding_institution_ids":["https://openalex.org/I189590672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05475784,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"560","last_page":"565"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9805999994277954,"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":0.9333999752998352,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8223161101341248},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.8151218891143799},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.792201042175293},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6287894248962402},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5856013298034668},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5499146580696106},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5395239591598511},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5079057812690735},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.5001673698425293},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4667046070098877},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4497334063053131},{"id":"https://openalex.org/keywords/probabilistic-classification","display_name":"Probabilistic classification","score":0.42463427782058716},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3433656096458435},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18237930536270142},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.12579086422920227},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07790520787239075},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.06360873579978943}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8223161101341248},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.8151218891143799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.792201042175293},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6287894248962402},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5856013298034668},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5499146580696106},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5395239591598511},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5079057812690735},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.5001673698425293},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4667046070098877},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4497334063053131},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.42463427782058716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3433656096458435},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18237930536270142},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.12579086422920227},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07790520787239075},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.06360873579978943},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529914","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3529836.3529914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529914","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283274378.pdf","grobid_xml":"https://content.openalex.org/works/W4283274378.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2038721957","https://openalex.org/W2108598243","https://openalex.org/W2143017621","https://openalex.org/W2157423479","https://openalex.org/W2194775991","https://openalex.org/W2798836702","https://openalex.org/W2894906112","https://openalex.org/W2963070905","https://openalex.org/W3034942609","https://openalex.org/W3099486271","https://openalex.org/W3143107425","https://openalex.org/W3182874523"],"related_works":["https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3176438653","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138","https://openalex.org/W3163412989"],"abstract_inverted_index":{"We":[0,113,131],"present":[1],"a":[2,64],"novel":[3],"framework":[4,79],"for":[5,89,111,148,155,187,194],"the":[6,40,77,159,162,170,174,177,198],"problem":[7],"of":[8,39,50,72,165,173],"transfer":[9,87,96],"learning":[10,91,97,117,126,150,157,189,196],"between":[11],"few-shot":[12],"source":[13],"and":[14,59,98,101,104,141,151,190],"target":[15,41],"domains,":[16],"using":[17,176],"synthetic":[18],"attributes":[19],"in":[20,128],"addition":[21],"to":[22,122,135,146,153,185,192],"convolutional":[23],"neural":[24],"networks":[25],"that":[26,68,71],"are":[27,43],"pre-trained":[28],"on":[29,158,197],"larger":[30],"image":[31],"corpora.":[32],"In":[33],"these":[34],"corpora,":[35],"no":[36],"labeled":[37],"instances":[38,49],"domains":[42],"present,":[44],"though":[45],"they":[46],"may":[47],"contain":[48],"their":[51],"superclasses.":[52],"Using":[53],"probabilistic":[54,99,116],"inference":[55],"over":[56],"predicted":[57],"classes":[58],"inferred":[60],"attributes,":[61],"we":[62,180],"developed":[63],"meta-learning":[65,109],"ensemble":[66],"method":[67],"builds":[69],"upon":[70,106],"[10].":[73],"This":[74],"paper":[75],"introduces":[76],"new":[78],"BCAT":[80],"(Between-Class":[81],"Attribute":[82],"Transfer),":[83],"adapting":[84],"inter-class":[85],"attribute":[86],"designed":[88],"zero-shot":[90],"(ZSL),":[92],"combined":[93],"with":[94,169],"fusing":[95],"priors,":[100],"thereby":[102],"extending":[103],"improving":[105],"existing":[107],"deep":[108,125],"models":[110],"FSL.":[112],"show":[114],"how":[115],"architectures":[118],"can":[119],"be":[120],"adapted":[121],"use":[123],"state-of-the-field":[124],"components":[127],"this":[129],"framework.":[130],"applied":[132],"our":[133],"technique":[134],"four":[136],"baseline":[137],"convnet-based":[138],"FSL":[139],"ensembles":[140],"boosted":[142],"accuracy":[143,182],"by":[144,183],"up":[145,152,184,191],"6.24%":[147],"1-shot":[149,188],"4.11%":[154],"5-shot":[156,195],"mini-ImageNet":[160],"dataset,":[161],"best":[163],"result":[164],"which":[166],"is":[167],"competitive":[168],"current":[171],"state":[172],"field;":[175],"same":[178],"technique,":[179],"improved":[181],"7.83%":[186],"3.67%":[193],"tiered-ImageNet":[199],"dataset.":[200]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}