{"id":"https://openalex.org/W4400527533","doi":"https://doi.org/10.1109/fg59268.2024.10582041","title":"Rank and Sort Loss-Aware Label Assignment with Centroid Prior for Dense Object Detection","display_name":"Rank and Sort Loss-Aware Label Assignment with Centroid Prior for Dense Object Detection","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4400527533","doi":"https://doi.org/10.1109/fg59268.2024.10582041"},"language":"en","primary_location":{"id":"doi:10.1109/fg59268.2024.10582041","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/fg59268.2024.10582041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)","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/A5010022745","display_name":"Shicheng Zu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shicheng Zu","raw_affiliation_strings":["Ericsson Panda Communication Co., Ltd.,Nanjing,China,211106"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ericsson Panda Communication Co., Ltd.,Nanjing,China,211106","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048434881","display_name":"Yucheng Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152119","display_name":"Jiangsu Province Hospital","ror":"https://ror.org/04py1g812","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210152119"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucheng Jin","raw_affiliation_strings":["Jiangsu Province Hospital on Integration of Chinese and Western Medicine,Nanjing,China,210028"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Province Hospital on Integration of Chinese and Western Medicine,Nanjing,China,210028","institution_ids":["https://openalex.org/I4210152119"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13855461,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.880299985408783,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.880299985408783,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.8544999957084656,"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/T11019","display_name":"Image Enhancement Techniques","score":0.830299973487854,"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/sort","display_name":"sort","score":0.7742547988891602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286930680274963},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.7035850286483765},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.639915406703949},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5446817278862},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5332806706428528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4951132833957672},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.44830527901649475},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42449378967285156},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3343682289123535},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25761932134628296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20134875178337097},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.15441098809242249},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.09757742285728455}],"concepts":[{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.7742547988891602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286930680274963},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.7035850286483765},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.639915406703949},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5446817278862},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5332806706428528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4951132833957672},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.44830527901649475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42449378967285156},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3343682289123535},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25761932134628296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20134875178337097},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.15441098809242249},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.09757742285728455}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fg59268.2024.10582041","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/fg59268.2024.10582041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2570343428","https://openalex.org/W2601564443","https://openalex.org/W2925359305","https://openalex.org/W2934198733","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963179609","https://openalex.org/W2963299996","https://openalex.org/W2963351448","https://openalex.org/W2963857746","https://openalex.org/W2963927307","https://openalex.org/W2964054038","https://openalex.org/W2964241181","https://openalex.org/W2969875432","https://openalex.org/W2989604896","https://openalex.org/W3012573144","https://openalex.org/W3035395201","https://openalex.org/W3035396860","https://openalex.org/W3035478146","https://openalex.org/W3035524459","https://openalex.org/W3039009902","https://openalex.org/W3106250896","https://openalex.org/W3107473354","https://openalex.org/W3108849448","https://openalex.org/W3167308647","https://openalex.org/W3171162369","https://openalex.org/W3176081225","https://openalex.org/W3176659256","https://openalex.org/W3187089511","https://openalex.org/W4214489586","https://openalex.org/W4214494681","https://openalex.org/W4225745741","https://openalex.org/W4312789460","https://openalex.org/W4313172897","https://openalex.org/W4375839990","https://openalex.org/W4386076325","https://openalex.org/W6620707391","https://openalex.org/W6750227808","https://openalex.org/W6752745451","https://openalex.org/W6767109091","https://openalex.org/W6779586474","https://openalex.org/W6779990023","https://openalex.org/W6784811298","https://openalex.org/W6796874202","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W2381926679","https://openalex.org/W2095055219","https://openalex.org/W2482527088","https://openalex.org/W2362374543","https://openalex.org/W2116295091","https://openalex.org/W4319430205","https://openalex.org/W3121711078","https://openalex.org/W2085265870","https://openalex.org/W2348196682","https://openalex.org/W2363927067"],"abstract_inverted_index":{"The":[0],"recent":[1,108],"progress":[2],"in":[3,21],"object":[4],"detection":[5,119],"seeks":[6],"to":[7,55,80,88,123,133,139,158,164,174],"design":[8],"more":[9,51,74,89],"effective":[10],"and":[11,34,53,181,207],"dynamic":[12],"label":[13,32,188],"assignment":[14,33,189],"strategies":[15],"that":[16,110],"automatically":[17],"select":[18],"training":[19],"samples":[20],"a":[22,220],"prediction-aware":[23],"manner.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28],"revisit":[29],"the":[30,37,56,66,70,77,82,95,99,107,111,118,125,129,135,143,149,152,159,187,194,199],"loss-aware":[31],"innovatively":[35],"propose":[36],"Rank":[38],"&":[39],"Sort":[40],"(RS)":[41],"Loss-aware":[42],"Label":[43],"Assignment":[44],"with":[45,172,186],"Centroid":[46],"Prior":[47],"(RSLLACP),":[48],"which":[49],"is":[50,73],"noise-robust":[52],"adapted":[54],"semantic":[57],"patterns":[58],"of":[59,65,201],"each":[60],"instance.":[61],"By":[62],"taking":[63,103],"advantage":[64],"instance":[67],"mask":[68],"annotation,":[69],"centroid":[71,96],"prior":[72,97],"appropriate":[75],"than":[76],"geometric":[78],"center":[79],"define":[81],"region":[83],"for":[84],"positive":[85,144,168],"anchors":[86,101,145,169],"due":[87],"informative":[90],"features":[91],"contained":[92],"within.":[93],"Besides,":[94],"prevents":[98],"ambiguous":[100],"from":[102,148],"place.":[104],"Inspired":[105],"by":[106,151,219],"advances":[109],"ranking-based":[112,141],"objective":[113],"functions":[114],"can":[115],"dramatically":[116],"improve":[117],"performance,":[120],"RSLLACP":[121,209],"proposes":[122],"incorporate":[124],"RS":[126],"cost":[127,131],"into":[128],"matching":[130],"matrix":[132],"replace":[134],"classification":[136,153],"cost.":[137],"Thanks":[138],"its":[140,165],"nature,":[142],"are":[146,170],"differentiated":[147],"negatives":[150],"logits":[154],"while":[155],"being":[156],"robust":[157],"foreground-background":[160],"class":[161],"imbalance.":[162],"Due":[163],"sorting":[166,182],"objective,":[167],"prioritized":[171],"respect":[173],"their":[175],"continuous":[176],"localization":[177],"qualities.":[178],"This":[179],"ranking":[180],"nature":[183],"lines":[184],"up":[185],"objective.":[190],"Extensive":[191],"experiments":[192],"on":[193],"MS":[195],"COCO":[196],"dataset":[197],"validate":[198],"effectiveness":[200],"our":[202],"proposed":[203],"RSLLACP.":[204],"Without":[205],"bells":[206],"whistles,":[208],"achieves":[210],"51.9":[211],"AP,":[212],"outperforming":[213],"all":[214],"existing":[215],"state-of-the-art":[216],"one-stage":[217],"detectors":[218],"significant":[221],"margin.":[222]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}