{"id":"https://openalex.org/W4282919254","doi":"https://doi.org/10.1109/tpami.2022.3180392","title":"Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection","display_name":"Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4282919254","doi":"https://doi.org/10.1109/tpami.2022.3180392","pmid":"https://pubmed.ncbi.nlm.nih.gov/35679384"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3180392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3180392","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/A5100693026","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-4996-7365"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiang Li","raw_affiliation_strings":["PCA Lab, College of Computer Science, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-4996-7365","affiliations":[{"raw_affiliation_string":"PCA Lab, College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012770465","display_name":"Chengqi Lv","orcid":"https://orcid.org/0000-0002-6356-3922"},"institutions":[{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengqi Lv","raw_affiliation_strings":["Shanghai AI Laboratory, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6356-3922","affiliations":[{"raw_affiliation_string":"Shanghai AI Laboratory, Shanghai, China","institution_ids":["https://openalex.org/I4391012619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101827340","display_name":"Wenhai Wang","orcid":"https://orcid.org/0000-0002-2418-3134"},"institutions":[{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhai Wang","raw_affiliation_strings":["Shanghai AI Laboratory, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-2418-3134","affiliations":[{"raw_affiliation_string":"Shanghai AI Laboratory, Shanghai, China","institution_ids":["https://openalex.org/I4391012619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438659","display_name":"Gang Li","orcid":"https://orcid.org/0000-0001-9956-7653"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-9956-7653","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100604251","display_name":"Lingfeng Yang","orcid":"https://orcid.org/0000-0002-2725-8947"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfeng Yang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2725-8947","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100726984","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0003-4800-832X"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["PCA Lab, College of Computer Science, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-4800-832X","affiliations":[{"raw_affiliation_string":"PCA Lab, College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100693026"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":20.8157,"has_fulltext":false,"cited_by_count":218,"citation_normalized_percentile":{"value":0.99717236,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"45","issue":"3","first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9991999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9979000091552734,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6727622747421265},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6608019471168518},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5829100608825684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5604032278060913},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5090801119804382},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4787006676197052},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.44186681509017944},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4416826665401459},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38304728269577026},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11349648237228394}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6727622747421265},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6608019471168518},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5829100608825684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5604032278060913},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5090801119804382},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4787006676197052},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.44186681509017944},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4416826665401459},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38304728269577026},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11349648237228394}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3180392","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3180392","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:35679384","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35679384","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":0.550000011920929,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W2031454541","https://openalex.org/W2081827563","https://openalex.org/W2102605133","https://openalex.org/W2163605009","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2601564443","https://openalex.org/W2798542761","https://openalex.org/W2883780447","https://openalex.org/W2886904239","https://openalex.org/W2920326761","https://openalex.org/W2962677013","https://openalex.org/W2962766617","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963402592","https://openalex.org/W2963857746","https://openalex.org/W2964241181","https://openalex.org/W2966926453","https://openalex.org/W2970575838","https://openalex.org/W2981958729","https://openalex.org/W2982770724","https://openalex.org/W2984006054","https://openalex.org/W2985405845","https://openalex.org/W2986357608","https://openalex.org/W2995000130","https://openalex.org/W3014595575","https://openalex.org/W3034896527","https://openalex.org/W3035323039","https://openalex.org/W3035376925","https://openalex.org/W3035396860","https://openalex.org/W3035478146","https://openalex.org/W3035524459","https://openalex.org/W3039009902","https://openalex.org/W3042930119","https://openalex.org/W3096166514","https://openalex.org/W3102710196","https://openalex.org/W3106250896","https://openalex.org/W3108849448","https://openalex.org/W3109381875","https://openalex.org/W3131097401","https://openalex.org/W3136022415","https://openalex.org/W3172087149","https://openalex.org/W3176187859","https://openalex.org/W3180134609","https://openalex.org/W4288325606","https://openalex.org/W4293584584","https://openalex.org/W4298168895","https://openalex.org/W6620707391","https://openalex.org/W6638667902","https://openalex.org/W6639102338","https://openalex.org/W6684191040","https://openalex.org/W6730903564","https://openalex.org/W6750227808","https://openalex.org/W6750697433","https://openalex.org/W6753767121","https://openalex.org/W6760424586","https://openalex.org/W6761662064","https://openalex.org/W6764322716","https://openalex.org/W6767109091","https://openalex.org/W6767133135","https://openalex.org/W6770930591","https://openalex.org/W6771252513","https://openalex.org/W6771837744","https://openalex.org/W6779586474","https://openalex.org/W6779990023","https://openalex.org/W6780959388","https://openalex.org/W6782744377","https://openalex.org/W6785808489","https://openalex.org/W6790696370","https://openalex.org/W6910687937"],"related_works":["https://openalex.org/W3192357901","https://openalex.org/W3036286480","https://openalex.org/W2387360586","https://openalex.org/W4287027631","https://openalex.org/W4237171675","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W2962677013"],"abstract_inverted_index":{"Object":[0],"detection":[1,50,104],"is":[2,62,72,85,236,299],"a":[3,188,192,287],"fundamental":[4,116],"computer":[5],"vision":[6],"task":[7],"that":[8,250],"simultaneously":[9],"predicts":[10],"the":[11,16,69,94,100,110,113,132,136,146,155,178,182,207,219,225,238,259,268,275],"category":[12],"and":[13,38,54,68,121,139,143,153,157,201,222,280,290,305],"localization":[14,55],"of":[15,18,96,112,135,198,240,270],"targets":[17],"interest.":[19],"Recently":[20],"one-stage":[21],"(also":[22],"termed":[23,293],"\"dense\")":[24],"detectors":[25,46,84],"have":[26],"gained":[27],"much":[28],"attention":[29],"over":[30],"two-stage":[31],"ones":[32],"due":[33],"to":[34,41,86,92,102,186,194,258],"their":[35],"simple":[36],"pipeline":[37],"friendly":[39],"application":[40],"end":[42],"devices.":[43],"Dense":[44],"object":[45,49],"basically":[47],"formulate":[48],"as":[51],"dense":[52,83],"classification":[53,61,101,120,140],"(i.e.,":[56],"bounding":[57],"box":[58,70,199],"regression).":[59],"The":[60,215],"usually":[63],"optimized":[64],"by":[65],"Focal":[66,241,247,252],"Loss":[67,248,253],"location":[71],"commonly":[73],"learned":[74],"under":[75,295],"Dirac":[76,148],"delta":[77,149],"distribution.":[78],"A":[79],"recent":[80],"trend":[81],"for":[82,151,160,172,210,262],"introduce":[87],"an":[88],"individual":[89],"prediction":[90,184],"branch":[91],"estimate":[93],"quality":[95,118,137,162,179,213],"localization,":[97,152],"which":[98,235,298],"facilitates":[99],"improve":[103],"performance.":[105],"This":[106],"paper":[107],"delves":[108],"into":[109,181],"representations":[111,171,217],"above":[114],"three":[115],"elements:":[117],"estimation,":[119],"localization.":[122],"Three":[123],"problems":[124],"are":[125],"discovered":[126],"in":[127,228,278],"existing":[128],"practices,":[129],"including":[130],"(1)":[131],"inconsistent":[133],"usage":[134],"estimation":[138,180],"between":[141],"training":[142,279],"inference,":[144],"(2)":[145],"inflexible":[147],"distribution":[150,197,208,227],"(3)":[154],"deficient":[156],"implicit":[158],"guidance":[159],"accurate":[161],"estimation.":[163,214],"To":[164],"address":[165],"these":[166,173],"problems,":[167],"we":[168,176,285],"design":[169],"new":[170],"elements.":[174],"Specifically,":[175],"merge":[177],"class":[183],"vector":[185,193,209],"form":[187,257],"joint":[189],"representation,":[190],"use":[191],"represent":[195],"arbitrary":[196],"locations,":[200],"extract":[202],"discriminant":[203],"feature":[204],"descriptors":[205],"from":[206,254],"more":[211],"reliable":[212],"improved":[216],"eliminate":[218],"inconsistency":[220],"risk":[221],"accurately":[223],"depict":[224],"flexible":[226],"real":[229],"data,":[230],"but":[231],"contain":[232],"continuous":[233,260],"labels,":[234],"beyond":[237],"scope":[239],"Loss.":[242],"We":[243],"then":[244],"propose":[245],"Generalized":[246],"(GFocal)":[249],"generalizes":[251],"its":[255],"discrete":[256],"version":[261],"successful":[263],"optimization.":[264],"Extensive":[265],"experiments":[266],"demonstrate":[267],"effectiveness":[269],"our":[271],"method,":[272],"without":[273],"sacrificing":[274],"efficiency":[276],"both":[277],"inference.":[281],"Based":[282],"on":[283],"GFocal,":[284],"construct":[286],"considerably":[288],"fast":[289],"lightweight":[291],"detector":[292],"NanoDet":[294],"mobile":[296],"settings,":[297],"1.8":[300],"AP":[301],"higher,":[302],"2x":[303],"faster":[304],"6x":[306],"smaller":[307],"than":[308],"scaled":[309],"YoloV4-Tiny.":[310]},"counts_by_year":[{"year":2026,"cited_by_count":13},{"year":2025,"cited_by_count":105},{"year":2024,"cited_by_count":68},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-13T08:25:38.343686","created_date":"2025-10-10T00:00:00"}
