{"id":"https://openalex.org/W2516449915","doi":"https://doi.org/10.1109/icip.2016.7533150","title":"Recognizing unseen actions in a domain-adapted embedding space","display_name":"Recognizing unseen actions in a domain-adapted embedding space","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2516449915","doi":"https://doi.org/10.1109/icip.2016.7533150","mag":"2516449915"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7533150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7533150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5100610029","display_name":"Yikang Li","orcid":"https://orcid.org/0000-0003-4666-9642"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yikang Li","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069951623","display_name":"Sheng-Hung Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheng-hung Hu","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032615847","display_name":"Baoxin Li","orcid":"https://orcid.org/0000-0002-9294-4572"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoxin Li","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100610029"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":2.9993,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.92870843,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4195","last_page":"4199"},"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":0.9976999759674072,"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":0.9976999759674072,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9969000220298767,"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":0.9886999726295471,"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":0.8378921747207642},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7641053795814514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6710346937179565},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6532195806503296},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6423588395118713},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5348637700080872},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.49231088161468506},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4627779424190521},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4433944821357727},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40610411763191223},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3994290828704834},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3698136806488037}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8378921747207642},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7641053795814514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6710346937179565},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6532195806503296},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6423588395118713},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5348637700080872},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.49231088161468506},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4627779424190521},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4433944821357727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40610411763191223},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3994290828704834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3698136806488037},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7533150","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7533150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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":30,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W93016980","https://openalex.org/W841113906","https://openalex.org/W1542713999","https://openalex.org/W1614298861","https://openalex.org/W1744759976","https://openalex.org/W1805946669","https://openalex.org/W1867429401","https://openalex.org/W1923332106","https://openalex.org/W1932262286","https://openalex.org/W1968197118","https://openalex.org/W1994002998","https://openalex.org/W2105101328","https://openalex.org/W2123024445","https://openalex.org/W2128532956","https://openalex.org/W2153579005","https://openalex.org/W2342924590","https://openalex.org/W2963061446","https://openalex.org/W2963173190","https://openalex.org/W4294170691","https://openalex.org/W6600983433","https://openalex.org/W6603820874","https://openalex.org/W6623333368","https://openalex.org/W6632702419","https://openalex.org/W6636510571","https://openalex.org/W6639126518","https://openalex.org/W6640178978","https://openalex.org/W6648737282","https://openalex.org/W6678470764","https://openalex.org/W6704594945"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"With":[0],"the":[1,53,136,147,162],"sustaining":[2],"bloom":[3],"of":[4,36,131,138],"multimedia":[5],"data,":[6],"Zero-shot":[7],"Learning":[8],"(ZSL)":[9],"techniques":[10],"have":[11],"attracted":[12],"much":[13],"attention":[14],"in":[15],"recent":[16],"years":[17],"for":[18,73],"its":[19],"ability":[20],"to":[21,59,105,108,134],"train":[22],"learning":[23,165],"models":[24],"that":[25],"can":[26],"handle":[27],"\u201cunseen\u201d":[28],"categories.":[29],"Existing":[30],"ZSL":[31,49,75],"algorithms":[32],"mainly":[33],"take":[34],"advantages":[35,152],"attribute-based":[37],"semantic":[38,54,109],"space":[39],"and":[40,57,76,84,96,160],"only":[41],"focus":[42],"on":[43,143],"static":[44],"image":[45],"data.":[46],"Besides,":[47],"most":[48],"studies":[50],"merely":[51],"consider":[52],"embedded":[55],"labels":[56],"fail":[58],"address":[60],"domain":[61,117],"shift":[62],"problem.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67,114],"purpose":[68],"a":[69,98,116],"deep":[70],"two-output":[71],"model":[72,149],"video":[74,88,157],"action":[77],"recognition":[78],"tasks":[79],"by":[80,123],"computing":[81],"both":[82],"spatial":[83],"temporal":[85],"features":[86,104],"from":[87,126],"contents":[89],"through":[90],"distinct":[91,128],"Convolutional":[92],"Neural":[93],"Networks":[94],"(CNNs)":[95],"training":[97],"Multi-layer":[99],"Perceptron":[100],"(MLP)":[101],"upon":[102],"extracted":[103],"map":[106],"videos":[107],"embedding":[110,158],"word":[111],"vectors.":[112],"Moreover,":[113],"introduce":[115],"adaptation":[118],"strategy":[119],"named":[120],"\u201cConSSEV\u201d":[121],"-":[122],"combining":[124],"outputs":[125],"two":[127],"output":[129],"layers":[130],"our":[132],"MLP":[133],"improve":[135],"results":[137],"zero-shot":[139,164],"learning.":[140],"Our":[141],"experiments":[142],"UCF101":[144],"dataset":[145],"demonstrate":[146],"purposed":[148],"has":[150],"more":[151,155],"associated":[153],"with":[154],"complex":[156],"schemes,":[159],"outperforms":[161],"state-of-the-art":[163],"techniques.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
