{"id":"https://openalex.org/W4408354402","doi":"https://doi.org/10.1109/icassp49660.2025.10889228","title":"Part in Part Embedding Network for Zero-Shot Learning","display_name":"Part in Part Embedding Network for Zero-Shot Learning","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354402","doi":"https://doi.org/10.1109/icassp49660.2025.10889228"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":null,"display_name":"Zhexian Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhexian Zhou","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020302879","display_name":"Liang Xiao","orcid":"https://orcid.org/0000-0003-0178-9384"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xiao","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084688255","display_name":"Guo-Sen Xie","orcid":"https://orcid.org/0000-0002-5487-9845"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guo-Sen Xie","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36399199"],"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":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9901999831199646,"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":0.9901999831199646,"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":0.9103999733924866,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.640789270401001},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6388819217681885},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.5948034524917603},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.5502323508262634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3742552101612091},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.08348959684371948}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.640789270401001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388819217681885},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.5948034524917603},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5502323508262634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3742552101612091},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.08348959684371948},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2070148066","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2400717490","https://openalex.org/W2924476266","https://openalex.org/W2963283377","https://openalex.org/W2963499153","https://openalex.org/W2982407353","https://openalex.org/W2991813857","https://openalex.org/W3034359780","https://openalex.org/W3034379915","https://openalex.org/W3035297600","https://openalex.org/W3035388827","https://openalex.org/W3089741414","https://openalex.org/W3097309192","https://openalex.org/W3109728025","https://openalex.org/W3169024950","https://openalex.org/W3182605419","https://openalex.org/W3203055845","https://openalex.org/W3205534937","https://openalex.org/W4296425607","https://openalex.org/W4307093191","https://openalex.org/W4312771536","https://openalex.org/W4372260224","https://openalex.org/W4385453401","https://openalex.org/W4388240245","https://openalex.org/W4390871873","https://openalex.org/W6600609147","https://openalex.org/W6640963894","https://openalex.org/W6678815747","https://openalex.org/W6767354860","https://openalex.org/W6781630272","https://openalex.org/W6784486608","https://openalex.org/W6855628491"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574"],"abstract_inverted_index":{"Zero-shot":[0],"learning":[1],"(ZSL)":[2],"seeks":[3],"to":[4,14,54,100,133],"utilize":[5],"semantic":[6,34,136,145],"information":[7,116],"from":[8,105],"seen":[9],"classes":[10,18],"encountered":[11],"during":[12,19],"training":[13],"effectively":[15],"recognize":[16],"unseen":[17],"testing.":[20],"When":[21],"dealing":[22],"with":[23],"fine-grained":[24],"images,":[25],"capturing":[26],"local":[27,37,57,127],"features":[28,38,104,128,143],"heavily":[29],"influences":[30],"the":[31,82,88,109,113,121,134,139,162],"accuracy":[32],"of":[33,47,78,108,115,166],"descriptions.":[35,146],"Meanwhile,":[36],"are":[39,130],"represented":[40],"at":[41,173],"various":[42],"scales":[43],"across":[44,117],"different":[45,118],"layers":[46,107],"a":[48,66],"neural":[49],"network,":[50,72,110],"making":[51],"it":[52],"hard":[53],"capture":[55],"their":[56],"details":[58],"fully.":[59],"To":[60],"address":[61],"these":[62],"challenges,":[63],"we":[64],"propose":[65],"novel":[67],"part":[68,70,89,91],"in":[69,90],"embedding":[71],"termed":[73],"PPEN.":[74],"Specifically,":[75],"PPEN":[76],"consists":[77],"two":[79],"key":[80],"modules:":[81],"cross-layer":[83],"aggregation":[84],"(CLA)":[85],"module":[86,97,123],"and":[87,102,144,158,160,164],"attention":[92],"(PIPA)":[93],"module.":[94],"The":[95],"CLA":[96],"is":[98,171],"designed":[99],"fuse":[101],"preserve":[103],"multiple":[106],"thereby":[111],"maintaining":[112],"richness":[114],"scales.":[119],"Further,":[120],"PIPA":[122],"focuses":[124],"on":[125,151],"identifying":[126],"that":[129],"most":[131],"pertinent":[132],"class":[135],"vectors,":[137],"enhancing":[138],"alignment":[140],"between":[141],"visual":[142],"We":[147],"evaluate":[148],"our":[149,167],"approach":[150],"three":[152],"ZSL":[153],"benchmarks,":[154],"i.e.,":[155],"CUB,":[156],"SUN,":[157],"AWA2,":[159],"demonstrate":[161],"superiority":[163],"competitiveness":[165],"proposed":[168],"approach.":[169],"Code":[170],"available":[172],"https://github.com/zhou834177226/PPENet.":[174]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}