{"id":"https://openalex.org/W4385482615","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192039","title":"Few-Shot Action Recognition with A Transductive Maximum Margin Classifier","display_name":"Few-Shot Action Recognition with A Transductive Maximum Margin Classifier","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385482615","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192039"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10192039","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10192039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5063873349","display_name":"Fei Pan","orcid":"https://orcid.org/0000-0002-6361-0936"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Pan","raw_affiliation_strings":["Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042385843","display_name":"Jie Guo","orcid":"https://orcid.org/0000-0002-4176-7617"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Guo","raw_affiliation_strings":["Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009275869","display_name":"Yanwen Guo","orcid":"https://orcid.org/0000-0002-7605-5206"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanwen Guo","raw_affiliation_strings":["Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,National Key Lab for Novel Software Technology,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"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":"abs/1705.06950","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9853000044822693,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9815000295639038,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.8233861923217773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.722416877746582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7137539386749268},{"id":"https://openalex.org/keywords/hyperplane","display_name":"Hyperplane","score":0.6435950994491577},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.6337013840675354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6146641969680786},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.48508989810943604},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46731823682785034},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4593515694141388},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4542253315448761},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.4416906535625458},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4278516471385956},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19606852531433105},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.18445342779159546}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.8233861923217773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722416877746582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7137539386749268},{"id":"https://openalex.org/C68693459","wikidata":"https://www.wikidata.org/wiki/Q657586","display_name":"Hyperplane","level":2,"score":0.6435950994491577},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.6337013840675354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6146641969680786},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.48508989810943604},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46731823682785034},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4593515694141388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4542253315448761},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.4416906535625458},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4278516471385956},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19606852531433105},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.18445342779159546},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10192039","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10192039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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":33,"referenced_works":["https://openalex.org/W2012210378","https://openalex.org/W2108598243","https://openalex.org/W2128160875","https://openalex.org/W2194775991","https://openalex.org/W2472357683","https://openalex.org/W2507009361","https://openalex.org/W2601450892","https://openalex.org/W2607182503","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2770804203","https://openalex.org/W2917888609","https://openalex.org/W2981874246","https://openalex.org/W2997582214","https://openalex.org/W3035374961","https://openalex.org/W3041485444","https://openalex.org/W3095374178","https://openalex.org/W3173271747","https://openalex.org/W3175528717","https://openalex.org/W3200749679","https://openalex.org/W4221150062","https://openalex.org/W4301014524","https://openalex.org/W4385245566","https://openalex.org/W6603760306","https://openalex.org/W6676348322","https://openalex.org/W6717697761","https://openalex.org/W6720057410","https://openalex.org/W6736057607","https://openalex.org/W6753311412","https://openalex.org/W6755766585","https://openalex.org/W6766578407","https://openalex.org/W6789158709","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W26430904","https://openalex.org/W2320105591","https://openalex.org/W2391740273","https://openalex.org/W2348284307","https://openalex.org/W4307947749","https://openalex.org/W2010370304","https://openalex.org/W2163821662","https://openalex.org/W2162083125","https://openalex.org/W2352896153","https://openalex.org/W2087998900"],"abstract_inverted_index":{"Few-shot":[0],"action":[1,33,163],"recognition":[2,44,164],"aims":[3],"to":[4,41,60,79,93,127,132],"train":[5],"a":[6,14,26,63,96],"classifier":[7,30,58,64,97,115],"that":[8,71,104,172],"can":[9,74,124],"generalize":[10],"well":[11],"when":[12],"just":[13],"small":[15],"number":[16,82],"of":[17,53,83,113,120,156],"labeled":[18,84],"videos":[19,40,85,123],"per":[20],"class":[21,142],"are":[22,144],"given.":[23],"We":[24,102],"introduce":[25],"transductive":[27],"maximum":[28,56],"margin":[29,57,69],"for":[31,62],"few-shot":[32,139],"recognition,":[34],"which":[35,149],"leverages":[36],"the":[37,43,47,54,66,80,87,106,110,114,117,121,128,138,141,152,157],"unlabeled":[38],"query":[39,122],"improve":[42,133],"performance":[45],"in":[46,86,137,151],"test":[48],"task.":[49],"The":[50],"basic":[51],"idea":[52],"classical":[55],"is":[59,91],"search":[61],"with":[65,98],"largest":[67],"geometric":[68,107],"so":[70],"training":[72,154],"data":[73,134],"be":[75],"correctly":[76],"classified.":[77],"Due":[78],"insufficient":[81],"support":[88],"set,":[89],"it":[90],"challenging":[92],"find":[94],"such":[95],"good":[99],"generalization":[100],"ability.":[101],"observe":[103],"exploring":[105],"relationship":[108],"between":[109],"separating":[111],"hyperplane":[112],"and":[116,168],"feature":[118],"vectors":[119],"bring":[125],"improvements":[126],"classifier.":[129],"In":[130],"order":[131],"utilization":[135],"efficiency":[136],"setting,":[140],"prototypes":[143],"also":[145],"treated":[146],"as":[147],"examples,":[148],"participate":[150],"iterative":[153],"process":[155],"model.":[158],"Experimental":[159],"results":[160],"on":[161],"two":[162],"datasets":[165],"including":[166],"Kinetics":[167],"Something-Something":[169],"V2":[170],"show":[171],"our":[173],"method":[174],"achieves":[175],"state-of-the-art":[176],"performance.":[177]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
