{"id":"https://openalex.org/W4405754105","doi":"https://doi.org/10.1109/tmm.2024.3521792","title":"Part-Level Relationship Learning for Fine-Grained Few-Shot Image Classification","display_name":"Part-Level Relationship Learning for Fine-Grained Few-Shot Image Classification","publication_year":2024,"publication_date":"2024-12-24","ids":{"openalex":"https://openalex.org/W4405754105","doi":"https://doi.org/10.1109/tmm.2024.3521792"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2024.3521792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2024.3521792","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-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/A5101705249","display_name":"Chuanming Wang","orcid":"https://orcid.org/0000-0001-6932-6226"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chuanming Wang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048944835","display_name":"Huiyuan Fu","orcid":"https://orcid.org/0000-0002-5276-4366"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiyuan Fu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008182432","display_name":"Peiye Liu","orcid":"https://orcid.org/0000-0002-6002-2899"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiye Liu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100710713","display_name":"Huad\u00f3ng Ma","orcid":"https://orcid.org/0000-0002-7199-5047"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huadong Ma","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101705249"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21613315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"1448","last_page":"1460"},"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":0.9769999980926514,"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":0.9769999980926514,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9440000057220459,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8434573411941528},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6666519641876221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5175616145133972},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.49875354766845703},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43321019411087036},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37496525049209595},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3591095209121704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8434573411941528},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6666519641876221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175616145133972},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.49875354766845703},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43321019411087036},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37496525049209595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3591095209121704},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2024.3521792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2024.3521792","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5046278692","display_name":null,"funder_award_id":"20230484406","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"},{"id":"https://openalex.org/G6287799790","display_name":null,"funder_award_id":"B18008","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"}],"funders":[{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1954152232","https://openalex.org/W2138011018","https://openalex.org/W2194775991","https://openalex.org/W2460852148","https://openalex.org/W2618530766","https://openalex.org/W2737725206","https://openalex.org/W2765268259","https://openalex.org/W2773003563","https://openalex.org/W2799123391","https://openalex.org/W2904218366","https://openalex.org/W2943605315","https://openalex.org/W2963070905","https://openalex.org/W2964105864","https://openalex.org/W2965572487","https://openalex.org/W2979689312","https://openalex.org/W2991559096","https://openalex.org/W3009081299","https://openalex.org/W3012255272","https://openalex.org/W3024041237","https://openalex.org/W3024314529","https://openalex.org/W3034312118","https://openalex.org/W3034637015","https://openalex.org/W3034978645","https://openalex.org/W3035143213","https://openalex.org/W3035607448","https://openalex.org/W3084193526","https://openalex.org/W3087963932","https://openalex.org/W3096805028","https://openalex.org/W3107763055","https://openalex.org/W3108878460","https://openalex.org/W3128632573","https://openalex.org/W3162481048","https://openalex.org/W3168054216","https://openalex.org/W3173788280","https://openalex.org/W3176276396","https://openalex.org/W3176278386","https://openalex.org/W3176341011","https://openalex.org/W3197005357","https://openalex.org/W3206402863","https://openalex.org/W3213639431","https://openalex.org/W4214562728","https://openalex.org/W4283834642","https://openalex.org/W4289752563","https://openalex.org/W4304098587","https://openalex.org/W6600609147","https://openalex.org/W6638677478","https://openalex.org/W6717697761","https://openalex.org/W6736057607","https://openalex.org/W6748555532","https://openalex.org/W6749327742","https://openalex.org/W6755766585","https://openalex.org/W6758126075","https://openalex.org/W6759000249","https://openalex.org/W6780975210","https://openalex.org/W6783967690"],"related_works":["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","https://openalex.org/W2359774528","https://openalex.org/W4298312966","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Recently,":[0],"an":[1],"increasing":[2],"number":[3],"of":[4,33,194],"few-shot":[5,37,70,187],"image":[6,57,71,131],"classification":[7,24,58,72],"methods":[8,38],"have":[9],"been":[10],"proposed,":[11],"and":[12,78,151],"they":[13],"aim":[14],"at":[15],"seeking":[16],"a":[17,22,68,143,153],"learning":[18,188],"paradigm":[19],"to":[20,39,41,53,105,135,147,156],"train":[21],"high-performance":[23],"model":[25,118],"with":[26],"limited":[27],"labeled":[28],"samples.":[29,83],"However,":[30],"the":[31,55,95,107,117,129,148,158,192],"neglect":[32],"part-level":[34],"relationships":[35,80],"causes":[36],"struggle":[40],"distinguish":[42],"between":[43],"closely":[44],"similar":[45,163],"subcategories,":[46],"which":[47,114,190],"makes":[48],"it":[49],"difficult":[50],"for":[51,123],"them":[52],"solve":[54],"fine-grained":[56,69,176],"problem.":[59],"To":[60,84,165],"tackle":[61],"this":[62,65],"challenging":[63],"task,":[64],"paper":[66],"proposes":[67],"method":[73],"that":[74],"exploits":[75],"both":[76],"intra-part":[77,112],"inter-part":[79,149],"among":[81,161],"different":[82,100],"establish":[85],"comprehensive":[86],"relationships,":[87,113],"we":[88,103,138,170],"first":[89],"extract":[90],"multiple":[91],"discriminative":[92],"descriptors":[93],"from":[94],"input":[96],"image,":[97],"representing":[98],"its":[99],"parts.":[101],"Then,":[102],"propose":[104],"define":[106],"metric":[108],"spaces":[109],"by":[110],"interpolating":[111],"can":[115],"help":[116],"adaptively":[119],"find":[120],"clear":[121],"boundaries":[122],"these":[124,140,162],"confusing":[125],"classes.":[126,164],"Finally,":[127],"since":[128],"unlabeled":[130],"has":[132],"high":[133],"similarities":[134,141],"all":[136],"classes,":[137],"project":[139],"into":[142],"high-dimension":[144],"space":[145],"according":[146],"relationship":[150],"interpolate":[152],"parameterized":[154],"classifier":[155],"discover":[157],"subtle":[159],"differences":[160],"evaluate":[166],"our":[167,182,195],"proposed":[168],"method,":[169],"conduct":[171],"extensive":[172],"experiments":[173],"on":[174],"various":[175],"datasets.":[177],"Without":[178],"any":[179],"pre-train/fine-tuning":[180],"process,":[181],"approach":[183],"clearly":[184],"outperforms":[185],"previous":[186],"methods,":[189],"demonstrates":[191],"effectiveness":[193],"approach.":[196]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
