{"id":"https://openalex.org/W3101432304","doi":"https://doi.org/10.1109/tpami.2022.3152495","title":"Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping","display_name":"Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W3101432304","doi":"https://doi.org/10.1109/tpami.2022.3152495","mag":"3101432304","pmid":"https://pubmed.ncbi.nlm.nih.gov/35180077"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3152495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3152495","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101694146","display_name":"Xinyi Xu","orcid":"https://orcid.org/0000-0002-4776-2809"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyi Xu","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-4776-2809","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100656202","display_name":"Zhengyang Wang","orcid":"https://orcid.org/0000-0002-5146-2884"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengyang Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-5146-2884","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015874725","display_name":"Cheng Deng","orcid":"https://orcid.org/0000-0003-2620-3247"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Deng","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2620-3247","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051474344","display_name":"Hao Yuan","orcid":"https://orcid.org/0000-0001-5270-2782"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Yuan","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-5270-2782","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052278550","display_name":"Shuiwang Ji","orcid":"https://orcid.org/0000-0002-4205-4563"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuiwang Ji","raw_affiliation_strings":["Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-4205-4563","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101694146"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.8367,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.8615587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"45","issue":"1","first_page":"1189","last_page":"1200"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9993000030517578,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9921000003814697,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9907000064849854,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9577552080154419},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8320870399475098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6818684935569763},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.681617259979248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5449203252792358},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48069149255752563},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4643775522708893},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4367187023162842},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41820862889289856},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08213630318641663}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9577552080154419},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8320870399475098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6818684935569763},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.681617259979248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5449203252792358},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48069149255752563},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4643775522708893},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4367187023162842},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41820862889289856},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08213630318641663},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3152495","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3152495","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:35180077","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35180077","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1797268635","https://openalex.org/W1849277567","https://openalex.org/W1983364832","https://openalex.org/W2024165284","https://openalex.org/W2096733369","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2124509324","https://openalex.org/W2135442311","https://openalex.org/W2138011018","https://openalex.org/W2138621090","https://openalex.org/W2157364932","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2470673105","https://openalex.org/W2549858646","https://openalex.org/W2555897561","https://openalex.org/W2745461083","https://openalex.org/W2752782242","https://openalex.org/W2885563408","https://openalex.org/W2899771611","https://openalex.org/W2904479140","https://openalex.org/W2910639395","https://openalex.org/W2945976633","https://openalex.org/W2948077755","https://openalex.org/W2948303601","https://openalex.org/W2962723992","https://openalex.org/W2962869940","https://openalex.org/W2963026686","https://openalex.org/W2963091558","https://openalex.org/W2963113119","https://openalex.org/W2963350250","https://openalex.org/W2963403868","https://openalex.org/W2963420686","https://openalex.org/W2963988212","https://openalex.org/W2964121744","https://openalex.org/W2964271799","https://openalex.org/W2982392870","https://openalex.org/W2982763192","https://openalex.org/W2998623274","https://openalex.org/W3001467249","https://openalex.org/W3004063239","https://openalex.org/W3004081568","https://openalex.org/W3006297807","https://openalex.org/W3007490808","https://openalex.org/W3034090344","https://openalex.org/W3035459623","https://openalex.org/W3092083846","https://openalex.org/W3099206234","https://openalex.org/W3106336533","https://openalex.org/W3110370715","https://openalex.org/W4311833010","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6730323794","https://openalex.org/W6739901393","https://openalex.org/W6756040250","https://openalex.org/W6758240700","https://openalex.org/W6774481907","https://openalex.org/W6788147755"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4385957992","https://openalex.org/W3129898729","https://openalex.org/W4200173597","https://openalex.org/W4310880831","https://openalex.org/W3006943036","https://openalex.org/W4299487748"],"abstract_inverted_index":{"Grouping":[0],"has":[1],"been":[2],"commonly":[3],"used":[4,117],"in":[5,121],"deep":[6],"metric":[7,33],"learning":[8,34,184],"for":[9,49],"computing":[10],"diverse":[11,186],"features.":[12,115],"To":[13],"improve":[14],"the":[15,42,65,85,96,144,164,168,179,183],"performance":[16],"and":[17,23,57,88,151,160,187],"interpretability,":[18],"we":[19],"propose":[20],"an":[21],"improved":[22],"interpretable":[24],"grouping":[25,107],"method":[26,38,73,108,126,146,181],"to":[27,111,128,135,167],"be":[28,93],"integrated":[29],"flexibly":[30],"with":[31,45,64],"any":[32],"framework.":[35],"Specifically,":[36],"our":[37,72,105,125,137,171,173],"is":[39,54,74,109],"based":[40],"on":[41],"attention":[43,82],"mechanism":[44],"a":[46,119],"learnable":[47,86],"query":[48,53,87],"each":[50,89],"group.":[51],"The":[52,81],"fully":[55],"trainable":[56],"can":[58,92],"capture":[59],"group-specific":[60],"information":[61],"when":[62],"combined":[63],"diversity":[66],"loss.":[67],"An":[68],"appealing":[69],"property":[70],"of":[71,98,114,170,185],"that":[75,99,104,143,178],"it":[76],"naturally":[77],"lends":[78],"itself":[79],"interpretability.":[80],"scores":[83],"between":[84],"spatial":[90,112],"position":[91],"interpreted":[94],"as":[95,118],"importance":[97],"position.":[100],"We":[101,131],"formally":[102],"show":[103],"proposed":[106,145,180],"invariant":[110],"permutations":[113],"When":[116],"module":[120],"convolutional":[122],"neural":[123],"networks,":[124],"leads":[127],"translational":[129],"invariance.":[130],"conduct":[132],"comprehensive":[133],"experiments":[134],"evaluate":[136],"method.":[138],"Our":[139],"quantitative":[140],"results":[141,175],"indicate":[142],"outperforms":[147],"prior":[148],"methods":[149],"consistently":[150],"significantly":[152],"across":[153,191],"different":[154],"datasets,":[155],"evaluation":[156],"metrics,":[157],"base":[158],"models,":[159],"loss":[161],"functions.":[162],"For":[163],"first":[165],"time":[166],"best":[169],"knowledge,":[172],"interpretation":[174],"clearly":[176],"demonstrate":[177],"enables":[182],"stable":[188],"semantic":[189],"features":[190],"groups.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}