{"id":"https://openalex.org/W4391092724","doi":"https://doi.org/10.1109/tip.2024.3354104","title":"Cross-Modal Contrastive Learning Network for Few-Shot Action Recognition","display_name":"Cross-Modal Contrastive Learning Network for Few-Shot Action Recognition","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391092724","doi":"https://doi.org/10.1109/tip.2024.3354104","pmid":"https://pubmed.ncbi.nlm.nih.gov/38252570"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2024.3354104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2024.3354104","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/A5100411483","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0002-6021-4938"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-3674-7160","affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071329803","display_name":"Hai\u2010Miao Hu","orcid":"https://orcid.org/0000-0001-6811-9209"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai-Miao Hu","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6811-9209","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758169","display_name":"Bo Li","orcid":"https://orcid.org/0000-0001-5980-4861"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5980-4861","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","Shanghai Artificial Intelligence Laboratory, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6913-9786","affiliations":[{"raw_affiliation_string":"Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"School of Informatics, Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Shanghai Artificial Intelligence Laboratory, Shanghai, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4391012619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2776,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.933618,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"33","issue":null,"first_page":"1257","last_page":"1271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9850999712944031,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9775000214576721,"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/overfitting","display_name":"Overfitting","score":0.8322370648384094},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.790709376335144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7899904847145081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7430906295776367},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5673519372940063},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5509985685348511},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5504530072212219},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4950093626976013},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4790959656238556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4720505177974701},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45527026057243347},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.43870651721954346},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4178640842437744},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.41370660066604614},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2622596323490143}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8322370648384094},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.790709376335144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7899904847145081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7430906295776367},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5673519372940063},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5509985685348511},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5504530072212219},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4950093626976013},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4790959656238556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4720505177974701},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45527026057243347},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.43870651721954346},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4178640842437744},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.41370660066604614},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2622596323490143},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2024.3354104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2024.3354104","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:38252570","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38252570","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/G2022121445","display_name":null,"funder_award_id":"U21A20514","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2323943204","display_name":null,"funder_award_id":"62122011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4782616423","display_name":null,"funder_award_id":"62372388","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8055606312","display_name":"\u57fa\u4e8e\u9c81\u68d2\u6a21\u578b\u62df\u5408\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u4eba\u8138\u5c5e\u6027\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"62071404","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8864390640","display_name":null,"funder_award_id":"2022ZD0160402","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1614298861","https://openalex.org/W2108598243","https://openalex.org/W2115733720","https://openalex.org/W2125389028","https://openalex.org/W2126579184","https://openalex.org/W2194775991","https://openalex.org/W2507009361","https://openalex.org/W2593414223","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2726717203","https://openalex.org/W2742093937","https://openalex.org/W2770173563","https://openalex.org/W2770804203","https://openalex.org/W2890773082","https://openalex.org/W2894873912","https://openalex.org/W2898553394","https://openalex.org/W2904218366","https://openalex.org/W2907214745","https://openalex.org/W2948171095","https://openalex.org/W2949269099","https://openalex.org/W2962858109","https://openalex.org/W2963524571","https://openalex.org/W2963680240","https://openalex.org/W2964105864","https://openalex.org/W2981874246","https://openalex.org/W2982806777","https://openalex.org/W2988501586","https://openalex.org/W2990503944","https://openalex.org/W3021778166","https://openalex.org/W3030509278","https://openalex.org/W3034572008","https://openalex.org/W3035163205","https://openalex.org/W3035303837","https://openalex.org/W3035374961","https://openalex.org/W3035524453","https://openalex.org/W3093455342","https://openalex.org/W3095374178","https://openalex.org/W3124568760","https://openalex.org/W3168565360","https://openalex.org/W3173271747","https://openalex.org/W3175528717","https://openalex.org/W3175613352","https://openalex.org/W3202188231","https://openalex.org/W3203574385","https://openalex.org/W3205333272","https://openalex.org/W4213376695","https://openalex.org/W4225383611","https://openalex.org/W4225911012","https://openalex.org/W4226271323","https://openalex.org/W4281752744","https://openalex.org/W4294691145","https://openalex.org/W4297697565","https://openalex.org/W4306832769","https://openalex.org/W4312659503","https://openalex.org/W4312959318","https://openalex.org/W4313046672","https://openalex.org/W4376167342","https://openalex.org/W4383899713","https://openalex.org/W4386065563","https://openalex.org/W4386065787","https://openalex.org/W4386071476","https://openalex.org/W4386071512","https://openalex.org/W6636510571","https://openalex.org/W6678815747","https://openalex.org/W6717697761","https://openalex.org/W6720691552","https://openalex.org/W6736057607","https://openalex.org/W6740472315","https://openalex.org/W6742288159","https://openalex.org/W6746638498","https://openalex.org/W6759807521","https://openalex.org/W6763641750","https://openalex.org/W6766578407","https://openalex.org/W6789158709","https://openalex.org/W6853229473","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W4298017035","https://openalex.org/W3128220493","https://openalex.org/W2792147139","https://openalex.org/W3110700750","https://openalex.org/W2998675825","https://openalex.org/W4226354336","https://openalex.org/W4394636190","https://openalex.org/W2736804899","https://openalex.org/W2897443685","https://openalex.org/W4307654087"],"abstract_inverted_index":{"Few-shot":[0],"action":[1,59,203],"recognition":[2,204],"aims":[3],"to":[4,31,55,74,132,150,176],"recognize":[5],"new":[6],"unseen":[7],"categories":[8],"with":[9,139],"only":[10],"a":[11,40,52,68,126,171],"few":[12],"labeled":[13],"samples":[14,77,98,138],"of":[15,25,47,137,142],"each":[16],"class.":[17],"However,":[18],"it":[19],"still":[20],"suffers":[21],"from":[22],"the":[23,32,62,85,92,96,101,121,140,148,152,157,178,185,196,200],"limitation":[24],"inadequate":[26],"data,":[27],"which":[28,82,145],"easily":[29],"leads":[30],"overfitting":[33,93],"and":[34,51,89,165,184,214],"low-generalization":[35],"problems.":[36],"Therefore,":[37],"we":[38,65,124,169],"propose":[39,125],"cross-modal":[41,127],"contrastive":[42,53,122,128],"learning":[43,129,154],"network":[44,72,149],"(CCLN),":[45],"consisting":[46],"an":[48],"adversarial":[49,63,71],"branch":[50],"branch,":[54,64,123],"perform":[56],"effective":[57],"few-shot":[58,202],"recognition.":[60],"In":[61],"elaborately":[66],"design":[67],"prototypical":[69],"generative":[70],"(PGAN)":[73],"obtain":[75,133],"synthesized":[76],"for":[78,108],"increasing":[79],"training":[80,97],"samples,":[81],"can":[83,146],"mitigate":[84],"data":[86],"scarcity":[87],"problem":[88],"thereby":[90],"alleviate":[91],"problem.":[94],"When":[95],"are":[99,105],"limited,":[100],"obtained":[102],"visual":[103],"features":[104],"usually":[106],"suboptimal":[107],"video":[109,182,189],"understanding":[110],"as":[111],"they":[112],"lack":[113],"discriminative":[114,134],"information.":[115],"To":[116],"address":[117],"this":[118],"issue,":[119],"in":[120],"module":[130,174],"(CCLM)":[131],"feature":[135,153],"representations":[136],"help":[141],"semantic":[143],"information,":[144,167],"enable":[147],"enhance":[151],"ability":[155],"at":[156],"class-level.":[158],"Moreover,":[159],"since":[160],"videos":[161],"contain":[162],"crucial":[163],"sequences":[164],"ordering":[166],"thus":[168],"introduce":[170],"spatial-temporal":[172],"enhancement":[173],"(SEM)":[175],"model":[177],"spatial":[179],"context":[180,187],"within":[181],"frames":[183],"temporal":[186],"across":[188],"frames.":[190],"The":[191],"experimental":[192],"results":[193],"show":[194],"that":[195],"proposed":[197],"CCLN":[198],"outperforms":[199],"state-of-the-art":[201],"methods":[205],"on":[206],"four":[207],"challenging":[208],"benchmarks,":[209],"including":[210],"Kinetics,":[211],"UCF101,":[212],"HMDB51":[213],"SSv2.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6}],"updated_date":"2026-07-27T08:26:11.824852","created_date":"2025-10-10T00:00:00"}
