{"id":"https://openalex.org/W4399418692","doi":"https://doi.org/10.1145/3651671.3651676","title":"Structural Subspace Learning for Few-shot Fine-grained Recognition","display_name":"Structural Subspace Learning for Few-shot Fine-grained Recognition","publication_year":2024,"publication_date":"2024-02-02","ids":{"openalex":"https://openalex.org/W4399418692","doi":"https://doi.org/10.1145/3651671.3651676"},"language":"en","primary_location":{"id":"doi:10.1145/3651671.3651676","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","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/A5082224453","display_name":"Linjia Li","orcid":null},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linjia Li","raw_affiliation_strings":["South China Agricultural University, China and Pazhou Lab, China"],"raw_orcid":"https://orcid.org/0009-0003-0265-6281","affiliations":[{"raw_affiliation_string":"South China Agricultural University, China and Pazhou Lab, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102990202","display_name":"Jin Deng","orcid":"https://orcid.org/0000-0002-6842-8340"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Deng","raw_affiliation_strings":["South China Agricultural University, China and Pazhou Lab, China"],"raw_orcid":"https://orcid.org/0000-0002-6842-8340","affiliations":[{"raw_affiliation_string":"South China Agricultural University, China and Pazhou Lab, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088448859","display_name":"Ying Huang","orcid":"https://orcid.org/0009-0003-4495-8974"},"institutions":[{"id":"https://openalex.org/I4210106134","display_name":"Guangzhou Vocational College of Science and Technology","ror":"https://ror.org/01dan7p53","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210106134"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Huang","raw_affiliation_strings":["Guangzhou Vocational College of Technology & Business, China"],"raw_orcid":"https://orcid.org/0009-0003-4495-8974","affiliations":[{"raw_affiliation_string":"Guangzhou Vocational College of Technology & Business, China","institution_ids":["https://openalex.org/I4210106134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077404267","display_name":"Yanyan Chen","orcid":"https://orcid.org/0009-0000-3007-253X"},"institutions":[{"id":"https://openalex.org/I4210106134","display_name":"Guangzhou Vocational College of Science and Technology","ror":"https://ror.org/01dan7p53","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210106134"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Chen","raw_affiliation_strings":["Guangzhou Vocational College of Technology & Business, China"],"raw_orcid":"https://orcid.org/0009-0000-3007-253X","affiliations":[{"raw_affiliation_string":"Guangzhou Vocational College of Technology & Business, China","institution_ids":["https://openalex.org/I4210106134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100718116","display_name":"Wei Luo","orcid":"https://orcid.org/0000-0003-1431-4134"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Luo","raw_affiliation_strings":["South China Agricultural University, China and Pazhou Lab, China"],"raw_orcid":"https://orcid.org/0000-0003-1431-4134","affiliations":[{"raw_affiliation_string":"South China Agricultural University, China and Pazhou Lab, China","institution_ids":["https://openalex.org/I101479585"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5082224453"],"corresponding_institution_ids":["https://openalex.org/I101479585"],"apc_list":null,"apc_paid":null,"fwci":0.6623,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.73205583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"693","last_page":"699"},"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":0.9994999766349792,"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":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9858999848365784,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/subspace-topology","display_name":"Subspace topology","score":0.8299022912979126},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.7888990640640259},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7438029050827026},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7048904895782471},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6730974316596985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6539360284805298},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.61180579662323},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.49613460898399353},{"id":"https://openalex.org/keywords/orthographic-projection","display_name":"Orthographic projection","score":0.4840228259563446},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4693984091281891},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4568321406841278},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4439375400543213},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4184337258338928},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3890232443809509},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20774424076080322},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1794610321521759},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1489979326725006},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07511568069458008}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.8299022912979126},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.7888990640640259},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438029050827026},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7048904895782471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6730974316596985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6539360284805298},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.61180579662323},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.49613460898399353},{"id":"https://openalex.org/C175694140","wikidata":"https://www.wikidata.org/wiki/Q980329","display_name":"Orthographic projection","level":2,"score":0.4840228259563446},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4693984091281891},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4568321406841278},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4439375400543213},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4184337258338928},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3890232443809509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20774424076080322},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1794610321521759},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1489979326725006},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07511568069458008},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3651671.3651676","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[{"id":"https://openalex.org/G1384952411","display_name":null,"funder_award_id":"No.2020A1515010813","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G481531038","display_name":null,"funder_award_id":"No.61702197","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1785227512","https://openalex.org/W2112796928","https://openalex.org/W2138011018","https://openalex.org/W2194775991","https://openalex.org/W2479109623","https://openalex.org/W2604134068","https://openalex.org/W2798836702","https://openalex.org/W2934497416","https://openalex.org/W2952523436","https://openalex.org/W2962987395","https://openalex.org/W2963078860","https://openalex.org/W2963101867","https://openalex.org/W2963446712","https://openalex.org/W2963845150","https://openalex.org/W2964105864","https://openalex.org/W3012255272","https://openalex.org/W3035143213","https://openalex.org/W3122230582","https://openalex.org/W3176341011","https://openalex.org/W4206908526","https://openalex.org/W4247179425","https://openalex.org/W4297733041","https://openalex.org/W4312959838"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W3040691452","https://openalex.org/W156213964","https://openalex.org/W2050960118"],"abstract_inverted_index":{"Classifying":[0],"fine-grained":[1,22,80,96,182],"objects":[2,73,81],"with":[3,46,169],"few-shot":[4,32,95,193,203],"reference":[5],"samples":[6],"is":[7],"a":[8,75,87,102,117,124,162],"big":[9],"challenge":[10],"due":[11],"to":[12,37,133,137,155,172,191],"the":[13,25,31,56,79,111,135,145,148,164,197],"intrinsic":[14],"large":[15],"intra-class":[16],"and":[17,24,78,123,150,201],"small":[18],"inter-class":[19],"variances":[20],"in":[21,74,120],"tasks":[23,41,194],"additional":[26],"overfitting":[27],"risk":[28],"brought":[29],"by":[30,67],"setting.":[33],"Previous":[34],"work":[35],"resorts":[36],"models":[38],"pretrained":[39],"on":[40,180,199],"sampled":[42],"from":[43,107,175],"base":[44,200],"classes":[45],"sufficient":[47],"training":[48],"data.":[49],"Although":[50],"much":[51],"progress":[52],"has":[53],"been":[54],"achieved,":[55],"performance":[57,198],"still":[58],"lags":[59],"far":[60],"behind":[61],"satisfaction.":[62],"In":[63],"this":[64,100],"study,":[65],"inspired":[66],"that":[68,185],"our":[69,186],"human":[70],"vision":[71],"recognizes":[72],"compositional":[76],"way":[77],"share":[82],"morphology":[83],"structures,":[84],"we":[85],"study":[86],"weakly-supervised":[88],"structural":[89,140],"subspace":[90,105,122,128,151,166],"learning":[91],"(W3SL)":[92],"method":[93,187],"for":[94],"recognition":[97],"(FSFGR).":[98],"To":[99],"end,":[101],"group":[103],"of":[104,110,147],"features":[106,152,167],"linear":[108],"projections":[109],"CNN":[112,149],"feature":[113],"are":[114,131,153,168],"achieved.":[115],"Specifically,":[116],"classification":[118],"loss":[119],"each":[121],"similarity":[125],"regularization":[126],"between":[127,144],"projection":[129],"matrices":[130],"applied":[132],"guide":[134],"subspaces":[136],"have":[138],"discriminative":[139],"geometry.":[141],"Moreover,":[142],"KL-divergences":[143],"outputs":[146],"implemented":[154],"distill":[156],"knowledge":[157],"into":[158],"these":[159],"subspaces.":[160],"As":[161],"result,":[163],"low-dimensional":[165],"strong":[170],"capacity":[171],"represent":[173],"data":[174],"different":[176],"classes.":[177],"Extensive":[178],"experiments":[179],"five":[181],"benchmarks":[183],"verify":[184],"can":[188],"effectively":[189],"generalize":[190],"novel":[192],"without":[195],"hurting":[196],"whole-class":[202],"tasks.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}