{"id":"https://openalex.org/W3174586553","doi":"https://doi.org/10.1109/icmew53276.2021.9455945","title":"Adaptive Multi-Scale Semantic Fusion Network For Zero-Shot Learning","display_name":"Adaptive Multi-Scale Semantic Fusion Network For Zero-Shot Learning","publication_year":2021,"publication_date":"2021-06-21","ids":{"openalex":"https://openalex.org/W3174586553","doi":"https://doi.org/10.1109/icmew53276.2021.9455945","mag":"3174586553"},"language":"en","primary_location":{"id":"doi:10.1109/icmew53276.2021.9455945","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew53276.2021.9455945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5100664738","display_name":"Jing Song","orcid":"https://orcid.org/0000-0002-8187-6324"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Song","raw_affiliation_strings":["Peking University,Shenzhen Graduate School","Shenzhen Graduate School, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Shenzhen Graduate School","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Shenzhen Graduate School, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070755370","display_name":"Peixi Peng","orcid":"https://orcid.org/0000-0002-7427-8764"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peixi Peng","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045281526","display_name":"Yunpeng Zhai","orcid":"https://orcid.org/0000-0003-2246-8045"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunpeng Zhai","raw_affiliation_strings":["Peking University,Shenzhen Graduate School","Shenzhen Graduate School, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Shenzhen Graduate School","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Shenzhen Graduate School, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393882","display_name":"Chong Zhang","orcid":"https://orcid.org/0000-0001-5072-5219"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chong Zhang","raw_affiliation_strings":["Peking University,Shenzhen Graduate School","Shenzhen Graduate School, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Shenzhen Graduate School","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Shenzhen Graduate School, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023918894","display_name":"Yonghong Tian","orcid":"https://orcid.org/0000-0002-2978-5935"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Tian","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100664738"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08001014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9635000228881836,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9315000176429749,"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/softmax-function","display_name":"Softmax function","score":0.8138719797134399},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.752764105796814},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7523365020751953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.689407467842102},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.579955518245697},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5746467709541321},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.47079363465309143},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4696848690509796},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4650638699531555},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.4564947187900543},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4499463140964508},{"id":"https://openalex.org/keywords/semantic-matching","display_name":"Semantic matching","score":0.4158576428890228},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4096527099609375},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37383049726486206},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36845409870147705},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.296294629573822},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12899178266525269}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8138719797134399},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.752764105796814},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7523365020751953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.689407467842102},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.579955518245697},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5746467709541321},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.47079363465309143},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4696848690509796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4650638699531555},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.4564947187900543},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4499463140964508},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.4158576428890228},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4096527099609375},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37383049726486206},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36845409870147705},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.296294629573822},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12899178266525269},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew53276.2021.9455945","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew53276.2021.9455945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7699999809265137,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2156374963","https://openalex.org/W2605805765","https://openalex.org/W2752782242","https://openalex.org/W2889625178","https://openalex.org/W2924476266","https://openalex.org/W2962761264","https://openalex.org/W2963499153","https://openalex.org/W2979728922","https://openalex.org/W2982407353","https://openalex.org/W2991813857","https://openalex.org/W2998708909","https://openalex.org/W3034359780","https://openalex.org/W3034552520","https://openalex.org/W4294170691","https://openalex.org/W6682691769"],"related_works":["https://openalex.org/W3047363187","https://openalex.org/W2907506409","https://openalex.org/W3119773509","https://openalex.org/W3177373753","https://openalex.org/W3152143533","https://openalex.org/W2139549667","https://openalex.org/W4385572821","https://openalex.org/W2315716767","https://openalex.org/W2374902383","https://openalex.org/W4387489691"],"abstract_inverted_index":{"Zero-shot":[0],"learning":[1,9,26,39],"aims":[2],"at":[3],"accurately":[4],"recognizing":[5],"unseen":[6],"objects":[7],"by":[8],"matrices":[10],"that":[11,77],"bridge":[12],"the":[13,27,36,82,98,109,140,144],"gap":[14],"between":[15],"visual":[16,71,120],"information":[17,72],"and":[18,73,89,105,119,126,136,139],"semantic":[19,41,47,66,91,117],"attributes.":[20],"Existing":[21],"approaches":[22],"predominantly":[23],"focus":[24],"on":[25,133],"proper":[28],"mapping":[29],"function":[30],"for":[31,65],"visual-semantic":[32],"embedding":[33,102],"while":[34],"neglecting":[35],"effect":[37],"of":[38,101],"discriminative":[40,116],"features,":[42],"which":[43],"leads":[44],"to":[45,59,79,113],"severe":[46],"ambiguity.":[48],"We":[49],"propose":[50],"a":[51],"practical":[52],"Adaptive":[53],"Multi-scale":[54],"Semantic":[55],"Fusion":[56],"(AMSF)":[57],"framework":[58],"perform":[60],"object-based":[61],"multi-scale":[62],"attribute":[63],"attention":[64],"disambiguation.":[67],"Considering":[68],"both":[69],"low-level":[70],"global":[74,88],"class-level":[75],"features":[76,118,121],"relate":[78],"this":[80],"ambiguity,":[81],"proposed":[83],"method":[84,130,145],"jointly":[85],"learns":[86],"cooperative":[87],"local":[90],"attributes":[92],"from":[93],"different":[94],"scales.":[95],"Moreover,":[96],"with":[97,122],"joint":[99],"supervision":[100],"softmax":[103],"loss":[104],"class-center":[106],"triplet":[107],"loss,":[108],"model":[110],"is":[111,131],"encouraged":[112],"learn":[114],"high":[115,123],"interclass":[124],"dispersion":[125],"intra-class":[127],"compactness.":[128],"The":[129],"evaluated":[132],"CUB,":[134],"AwA2,":[135],"SUN":[137],"datasets,":[138],"experimental":[141],"results":[142],"indicate":[143],"achieves":[146],"state-of-the-art":[147],"performance.":[148]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}