{"id":"https://openalex.org/W4285268823","doi":"https://doi.org/10.1109/tmm.2022.3177929","title":"Semantic-Aware Triplet Loss for Image Classification","display_name":"Semantic-Aware Triplet Loss for Image Classification","publication_year":2022,"publication_date":"2022-05-26","ids":{"openalex":"https://openalex.org/W4285268823","doi":"https://doi.org/10.1109/tmm.2022.3177929"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2022.3177929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3177929","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/A5101945490","display_name":"Guangzhi Wang","orcid":"https://orcid.org/0000-0002-5036-4051"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Guangzhi Wang","raw_affiliation_strings":["Graduate School for Integrative Sciences and Engineering and Institute of Data Science, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-5036-4051","affiliations":[{"raw_affiliation_string":"Graduate School for Integrative Sciences and Engineering and Institute of Data Science, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054313646","display_name":"Yangyang Guo","orcid":"https://orcid.org/0000-0001-8691-5372"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yangyang Guo","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8691-5372","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101854466","display_name":"Ziwei Xu","orcid":"https://orcid.org/0000-0003-0600-2579"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ziwei Xu","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-0600-2579","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020006712","display_name":"Yongkang Wong","orcid":"https://orcid.org/0000-0002-1239-4428"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yongkang Wong","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-1239-4428","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016415049","display_name":"Mohan Kankanhalli","orcid":"https://orcid.org/0000-0002-4846-2015"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Mohan S. Kankanhalli","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-4846-2015","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101945490"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":0.694,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74967008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"25","issue":null,"first_page":"4563","last_page":"4572"},"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":0.9994000196456909,"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":0.9994000196456909,"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":0.9994000196456909,"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/T11448","display_name":"Face recognition and analysis","score":0.9990000128746033,"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/computer-science","display_name":"Computer science","score":0.7866940498352051},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.669096052646637},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5113427042961121},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5029281973838806},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47733408212661743},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.4728282392024994},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.46310633420944214},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.45289650559425354},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4395788908004761},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4361109435558319},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4271264672279358},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4203483760356903},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3745419979095459},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3608945608139038},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3597528636455536},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2921472191810608}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7866940498352051},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.669096052646637},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5113427042961121},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5029281973838806},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47733408212661743},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.4728282392024994},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.46310633420944214},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.45289650559425354},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4395788908004761},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4361109435558319},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4271264672279358},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4203483760356903},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3745419979095459},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3608945608139038},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3597528636455536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2921472191810608},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2022.3177929","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2022.3177929","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":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1960364170","https://openalex.org/W2096733369","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2138621090","https://openalex.org/W2157364932","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2398118205","https://openalex.org/W2479423890","https://openalex.org/W2520774990","https://openalex.org/W2604763608","https://openalex.org/W2605102252","https://openalex.org/W2606377603","https://openalex.org/W2625674597","https://openalex.org/W2746314669","https://openalex.org/W2883780447","https://openalex.org/W2895115565","https://openalex.org/W2895347732","https://openalex.org/W2904365664","https://openalex.org/W2932399282","https://openalex.org/W2962869940","https://openalex.org/W2962898354","https://openalex.org/W2963026686","https://openalex.org/W2963163009","https://openalex.org/W2963350250","https://openalex.org/W2963351448","https://openalex.org/W2963446712","https://openalex.org/W2963466847","https://openalex.org/W2964105864","https://openalex.org/W2964137095","https://openalex.org/W2982083293","https://openalex.org/W2991642706","https://openalex.org/W2997136715","https://openalex.org/W3014225712","https://openalex.org/W3035014997","https://openalex.org/W3035017890","https://openalex.org/W3047999543","https://openalex.org/W3049440217","https://openalex.org/W3093061104","https://openalex.org/W3094219444","https://openalex.org/W3101631402","https://openalex.org/W3118608800","https://openalex.org/W3132408010","https://openalex.org/W3154360660","https://openalex.org/W3163842339","https://openalex.org/W3189076762","https://openalex.org/W3195324332","https://openalex.org/W3195393327","https://openalex.org/W3202576436","https://openalex.org/W4235505822","https://openalex.org/W4297775537","https://openalex.org/W6631190155","https://openalex.org/W6678470764","https://openalex.org/W6688533166","https://openalex.org/W6735013348","https://openalex.org/W6736057607","https://openalex.org/W6737664043","https://openalex.org/W6742288159","https://openalex.org/W6743428213","https://openalex.org/W6743661861","https://openalex.org/W6747943641","https://openalex.org/W6761836715","https://openalex.org/W6762718338","https://openalex.org/W6783596713","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"Successful":[0],"image":[1,180,250],"classification":[2],"requires":[3],"a":[4,22,27,42,55,61,126],"discriminative":[5],"representation":[6],"learning":[7,17],"model":[8,202],"for":[9,101,119,179,249],"images.":[10],"To":[11],"approach":[12],"this":[13,98,170],"idea,":[14],"deep":[15],"metric":[16],"(DML),":[18],"serving":[19],"as":[20,48,116],"building":[21],"basic":[23],"feature":[24,82],"space":[25,83],"with":[26,41,145,176,186],"pre-defined":[28],"metric,":[29],"has":[30],"demonstrated":[31],"compelling":[32],"performance":[33],"over":[34,228],"the":[35,49,66,69,74,81,95,102,108,120,146,187,207,244],"years.":[36],"DML":[37],"is":[38,174,214,220],"often":[39],"implemented":[40],"carefully":[43],"crafted":[44],"loss":[45],"function,":[46],"such":[47],"representative":[50],"triplet":[51,121,166,211],"loss,":[52],"which":[53,154,199],"encourages":[54],"positive":[56],"sample":[57],"to":[58,65,106,184],"be":[59,91,159],"by":[60],"fixed":[62],"margin":[63],"closer":[64],"anchor":[67],"than":[68],"negative.":[70],"Despite":[71],"its":[72,226],"efficacy,":[73],"negative":[75,88,128,140,143],"samples":[76,89],"are":[77],"treated":[78],"uniformly,":[79],"rendering":[80],"less":[84],"informative":[85],"since":[86],"different":[87,93],"can":[90,158],"largely":[92],"from":[94,153],"anchor.":[96],"In":[97,182],"work,":[99],"we,":[100],"first":[103],"time,":[104],"propose":[105],"exploit":[107],"semantic":[109,177],"information":[110],"inherent":[111],"in":[112],"discrete":[113],"class":[114,156],"labels":[115],"an":[117,149,193],"aid":[118],"loss.":[122,212],"Specifically,":[123],"we":[124,191,223],"build":[125],"bi-level":[127],"sampling":[129],"strategy,":[130,171],"<italic":[131,135],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[132,136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">i</i>":[133],".":[134],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">e</i>":[137],".,":[138],"strong":[139],"and":[141,164,204,222,231,236],"weak":[142],"sampling,":[144],"guidance":[147],"of":[148,209,246],"external":[150],"knowledge":[151,232],"source,":[152],"rich":[155],"semantics":[157,248],"extracted.":[160],"With":[161],"several":[162],"fine-grained":[163],"complementary":[165],"losses":[167],"based":[168],"on":[169,240],"our":[172,218],"method":[173,219],"enhanced":[175],"awareness":[178],"classification.":[181,251],"addition,":[183],"coordinate":[185],"complicated":[188],"training":[189],"dynamics,":[190],"devise":[192],"ad-hoc":[194],"Semantic":[195],"Relation":[196],"Weighting":[197],"module,":[198],"consistently":[200],"inspects":[201],"states":[203],"dynamically":[205],"adjusts":[206],"importance":[208],"each":[210],"It":[213],"worth":[215],"noting":[216],"that":[217],"plug-and-play,":[221],"thus":[224],"test":[225],"validity":[227],"various":[229],"backbones":[230],"sources.":[233],"Both":[234],"qualitative":[235],"quantitative":[237],"experimental":[238],"results":[239],"benchmark":[241],"datasets":[242],"demonstrate":[243],"effectiveness":[245],"employing":[247]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2025-10-10T00:00:00"}