{"id":"https://openalex.org/W3201985263","doi":"https://doi.org/10.1109/dicta52665.2021.9647081","title":"A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization","display_name":"A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W3201985263","doi":"https://doi.org/10.1109/dicta52665.2021.9647081","mag":"3201985263"},"language":"en","primary_location":{"id":"doi:10.1109/dicta52665.2021.9647081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta52665.2021.9647081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Digital Image Computing: Techniques and Applications (DICTA)","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":"https://openalex.org/A5002690169","display_name":"Yajie Sun","orcid":"https://orcid.org/0000-0003-4611-230X"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yajie Sun","raw_affiliation_strings":["School of Engineering and Built Environment Griffith University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Built Environment Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063479373","display_name":"Miaohua Zhang","orcid":"https://orcid.org/0000-0002-5281-7851"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Miaohua Zhang","raw_affiliation_strings":["School of Engineering and Built Environment Griffith University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Built Environment Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070802592","display_name":"Xiaohan Yu","orcid":"https://orcid.org/0000-0001-6186-0520"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiaohan Yu","raw_affiliation_strings":["School of Engineering and Built Environment Griffith University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Built Environment Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070883156","display_name":"Yi Liao","orcid":"https://orcid.org/0000-0002-4594-1416"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yi Liao","raw_affiliation_strings":["School of Engineering and Built Environment Griffith University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Built Environment Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071549690","display_name":"Yongsheng Gao","orcid":"https://orcid.org/0000-0002-5382-5351"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yongsheng Gao","raw_affiliation_strings":["School of Engineering and Built Environment Griffith University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Built Environment Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I11701301"],"apc_list":null,"apc_paid":null,"fwci":1.0257,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79790231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9987000226974487,"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":0.9987000226974487,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9944000244140625,"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/overfitting","display_name":"Overfitting","score":0.876589834690094},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.7818182706832886},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.776310920715332},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7219951748847961},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6946207284927368},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6943647861480713},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6755669116973877},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6687642931938171},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.649423360824585},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5828792452812195},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.46165335178375244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45924097299575806},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4481387436389923},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4236263632774353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24399426579475403},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18677794933319092},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15179705619812012}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.876589834690094},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.7818182706832886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.776310920715332},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7219951748847961},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6946207284927368},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6943647861480713},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6755669116973877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6687642931938171},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649423360824585},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5828792452812195},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.46165335178375244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45924097299575806},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4481387436389923},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4236263632774353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24399426579475403},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18677794933319092},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15179705619812012},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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":2,"locations":[{"id":"doi:10.1109/dicta52665.2021.9647081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta52665.2021.9647081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/411309","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/411309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1797268635","https://openalex.org/W2085925674","https://openalex.org/W2086377778","https://openalex.org/W2095999100","https://openalex.org/W2097777575","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2557371752","https://openalex.org/W2565591746","https://openalex.org/W2618530766","https://openalex.org/W2737701595","https://openalex.org/W2740620254","https://openalex.org/W2746314669","https://openalex.org/W2763070548","https://openalex.org/W2773003563","https://openalex.org/W2919115771","https://openalex.org/W2944962432","https://openalex.org/W2961018736","https://openalex.org/W2962761264","https://openalex.org/W2963066927","https://openalex.org/W2963163009","https://openalex.org/W2964350570","https://openalex.org/W2970832409","https://openalex.org/W2990952853","https://openalex.org/W2992308087","https://openalex.org/W2997737174","https://openalex.org/W3138369833","https://openalex.org/W3170284232","https://openalex.org/W3190140211","https://openalex.org/W3203876692","https://openalex.org/W6637373629","https://openalex.org/W6741183605","https://openalex.org/W6746649447","https://openalex.org/W6749781174","https://openalex.org/W6770376703","https://openalex.org/W6801954078"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4221142204","https://openalex.org/W3040691452"],"abstract_inverted_index":{"Fine-grained":[0],"visual":[1,22],"categorization":[2,23],"(FGVC),":[3],"which":[4,25,54,138],"aims":[5],"at":[6,27],"classifying":[7],"objects":[8],"with":[9,30,157,224],"small":[10],"inter-class":[11],"variances,":[12],"has":[13,34],"been":[14],"significantly":[15],"advanced":[16],"in":[17,92,102,176],"recent":[18,225],"years.":[19],"However,":[20],"ultra-fine-grained":[21],"(ultra-FGVC),":[24],"targets":[26],"identifying":[28],"subclasses":[29],"extremely":[31],"similar":[32],"patterns,":[33],"not":[35,165],"received":[36],"much":[37],"attention.":[38],"In":[39,187],"ultra-FGVC":[40,218],"datasets,":[41],"the":[42,50,61,93,103,116,125,134,161,167,180,185,188,201,206,211,231,236],"samples":[43,143,159],"per":[44],"category":[45],"are":[46,110,130],"always":[47],"scarce":[48],"as":[49],"granularity":[51],"moves":[52],"down,":[53],"will":[55],"lead":[56],"to":[57,69,132,163,199],"overfitting":[58,148],"problems.":[59],"Moreover,":[60],"difference":[62],"among":[63],"different":[64,119],"categories":[65,120],"is":[66,197],"too":[67],"subtle":[68],"distinguish":[70],"even":[71],"for":[72],"professional":[73],"experts.":[74],"Motivated":[75],"by":[76,113,204],"these":[77,108],"issues,":[78],"this":[79],"paper":[80],"proposes":[81],"a":[82,193],"novel":[83],"compositional":[84,94,189],"feature":[85,95],"embedding":[86,96],"and":[87,107,127,144,182,209,220],"similarity":[88,190,195],"metric":[89,191,196],"(CECS).":[90],"Specifically,":[91],"module,":[97,192],"we":[98],"randomly":[99],"select":[100],"patches":[101,109,114],"original":[104,135],"input":[105,136],"image,":[106],"then":[111],"replaced":[112,126],"from":[115,151],"images":[117,129],"of":[118,184],"or":[121],"masked":[122,128],"out.":[123],"Then":[124],"used":[131],"augment":[133],"images,":[137],"can":[139],"provide":[140],"more":[141],"diverse":[142,158],"thus":[145],"largely":[146],"alleviate":[147],"problem":[149],"resulted":[150],"limited":[152],"training":[153],"samples.":[154],"Besides,":[155],"learning":[156],"forces":[160],"model":[162],"learn":[164],"only":[166],"most":[168],"discriminative":[169],"features":[170,175],"but":[171],"also":[172],"other":[173],"informative":[174],"remaining":[177],"regions,":[178],"enhancing":[179],"generalization":[181],"robustness":[183],"model.":[186],"new":[194],"developed":[198],"improve":[200],"classification":[202],"performance":[203],"narrowing":[205],"intra-category":[207],"distance":[208],"enlarging":[210],"inter-category":[212],"distance.":[213],"Experimental":[214],"results":[215],"on":[216],"two":[217],"datasets":[219],"one":[221],"FGVC":[222],"dataset":[223],"benchmark":[226],"methods":[227],"consistently":[228],"demonstrate":[229],"that":[230],"proposed":[232],"CECS":[233],"method":[234],"achieves":[235],"state-of-the-art":[237],"performance.":[238]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}