乐胖代购免代理版

{"id":"https://openalex.org/W4392943508","doi":"https://doi.org/10.1109/icmla58977.2023.00013","title":"AReID: Rethinking Re-Identification and Occlusions for Multi-Object Tracking","display_name":"AReID: Rethinking Re-Identification and Occlusions for Multi-Object Tracking","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4392943508","doi":"https://doi.org/10.1109/icmla58977.2023.00013"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla58977.2023.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5091228807","display_name":"Rodolfo Quispe","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]},{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["BR","US"],"is_corresponding":false,"raw_author_name":"Rodolfo Quispe","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, Brazil","Microsoft Corp.,Redmond,USA,98052-6399"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]},{"raw_affiliation_string":"Microsoft Corp.,Redmond,USA,98052-6399","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051469345","display_name":"Cuiling Lan","orcid":"https://orcid.org/0000-0001-9145-9957"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuiling Lan","raw_affiliation_strings":["Microsoft Research Asia,Beijing,China,100080"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia,Beijing,China,100080","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101734732","display_name":"Zhizheng Zhang","orcid":"https://orcid.org/0000-0002-5360-7565"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhizheng Zhang","raw_affiliation_strings":["Microsoft Research Asia,Beijing,China,100080"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia,Beijing,China,100080","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065725754","display_name":"H\u00e9lio Pedrini","orcid":"https://orcid.org/0000-0003-0125-630X"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Helio Pedrini","raw_affiliation_strings":["Institute of Computing, University of Campinas,Campinas,Brazil,13083-852"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas,Campinas,Brazil,13083-852","institution_ids":["https://openalex.org/I181391015"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":67},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"37"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8059,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8059,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.733,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6692,"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/identification","display_name":"Identification","score":0.6724153},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5686128}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6724153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6478898},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.59614086},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5686128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5314903},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4977155},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.44080657},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06144318},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla58977.2023.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.48,"display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":28,"referenced_works":["https://openalex.org/W2291627510","https://openalex.org/W2565639579","https://openalex.org/W2739491435","https://openalex.org/W2952122856","https://openalex.org/W2963857746","https://openalex.org/W2981393651","https://openalex.org/W3012922853","https://openalex.org/W3084173793","https://openalex.org/W3094000868","https://openalex.org/W3106763294","https://openalex.org/W3119686997","https://openalex.org/W3145269263","https://openalex.org/W3165926952","https://openalex.org/W3167949052","https://openalex.org/W3171162369","https://openalex.org/W3174542323","https://openalex.org/W3184439416","https://openalex.org/W4206580383","https://openalex.org/W4206711703","https://openalex.org/W4286904999","https://openalex.org/W4295331127","https://openalex.org/W4302599069","https://openalex.org/W4306705649","https://openalex.org/W4311688136","https://openalex.org/W4312473433","https://openalex.org/W4312789460","https://openalex.org/W4319866011","https://openalex.org/W4385575022"],"related_works":["https://openalex.org/W4389065903","https://openalex.org/W3135795035","https://openalex.org/W2811496562","https://openalex.org/W2789220062","https://openalex.org/W2588268827","https://openalex.org/W2385949326","https://openalex.org/W2158788032","https://openalex.org/W2094665863","https://openalex.org/W2071984725","https://openalex.org/W1966005655"],"abstract_inverted_index":{"Re-Identification":[0,17,41,94,104],"has":[1,79],"been":[2,80],"widely":[3],"leverage":[4],"by":[5,51,102],"tracking-by-detection":[6],"Multi-Object":[7,125],"Tracking":[8,126],"methods":[9,138],"to":[10,69,98,119,123],"enhance":[11],"the":[12,25,29,33,48,52,58,63,140],"matching":[13],"step.":[14],"During":[15],"training,":[16],"is":[18,38,113],"usually":[19],"learned":[20],"as":[21,32],"a":[22],"sub-task":[23],"considering":[24],"projected":[26],"centroid":[27],"of":[28,47,55,62,93,142],"bounding":[30],"boxes":[31],"embedding":[34,67],"tensor.":[35],"This":[36],"design":[37],"problematic":[39],"for":[40],"feature":[42],"learning":[43],"and":[44,75,115],"leveraging":[45],"because":[46],"noise":[49],"introduced":[50],"big":[53],"amount":[54],"occlusions.":[56],"Specifically,":[57],"occlusion":[59],"between":[60],"objects":[61,71],"target":[64],"class":[65],"causes":[66],"tensors":[68],"represent":[70],"with":[72,145],"wrong":[73],"IDs":[74],"this":[76,86,100],"specific":[77],"scenario":[78],"overlooked":[81],"in":[82,132],"previous":[83],"literature.":[84],"In":[85],"work":[87],"we":[88],"introduce":[89],"an":[90],"Adaptive":[91],"use":[92],"features":[95,105],"that":[96,128],"aims":[97],"tackle":[99],"problem":[101],"using":[103,135],"only":[106],"when":[107],"they":[108],"are":[109],"reliable.":[110],"Our":[111],"method":[112,127,144],"generic":[114],"can":[116],"be":[117],"added":[118],"further":[120],"boost":[121],"performance":[122],"any":[124],"uses":[129],"Re-Identification.":[130],"Results":[131],"multiple":[133],"datasets":[134],"various":[136],"base":[137],"demonstrate":[139],"consistency":[141],"our":[143],"state-of-the-art":[146],"results.":[147]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4392943508","counts_by_year":[],"updated_date":"2025-01-02T07:11:15.157737","created_date":"2024-03-20"}