{"id":"https://openalex.org/W2981920808","doi":"https://doi.org/10.1145/3343031.3350982","title":"Adaptive Feature Fusion via Graph Neural Network for Person Re-identification","display_name":"Adaptive Feature Fusion via Graph Neural Network for Person Re-identification","publication_year":2019,"publication_date":"2019-10-15","ids":{"openalex":"https://openalex.org/W2981920808","doi":"https://doi.org/10.1145/3343031.3350982","mag":"2981920808"},"language":"en","primary_location":{"id":"doi:10.1145/3343031.3350982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343031.3350982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Multimedia","raw_type":"proceedings-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/A5101533503","display_name":"Yaoyu Li","orcid":"https://orcid.org/0000-0002-7362-7897"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaoyu Li","raw_affiliation_strings":["Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018564842","display_name":"Hantao Yao","orcid":"https://orcid.org/0000-0001-8125-2864"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hantao Yao","raw_affiliation_strings":["Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","Llvision Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"Llvision Technology, Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024879728","display_name":"Ling\u2010Yu Duan","orcid":"https://orcid.org/0000-0002-4491-2023"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyu Duan","raw_affiliation_strings":["Peng Cheng Laboratory &amp; Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory &amp; Peking University, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009236464","display_name":"Hanxing Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanxing Yao","raw_affiliation_strings":["Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","Llvision Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"Llvision Technology, Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022636178","display_name":"Changsheng Xu","orcid":"https://orcid.org/0000-0001-8343-9665"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changsheng Xu","raw_affiliation_strings":["Chinese Academy of Sciences, University of Chinese Academy of Sciences &amp; Peng Cheng Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, University of Chinese Academy of Sciences &amp; Peng Cheng Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101533503"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":1.1135,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.82405086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2115","last_page":"2123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9972000122070312,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9919000267982483,"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/discriminative-model","display_name":"Discriminative model","score":0.7765353918075562},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.776393711566925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6660003662109375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6587987542152405},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5877358317375183},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5846956968307495},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.545985996723175},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.523478627204895},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5028881430625916},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.436714231967926},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11114159226417542}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7765353918075562},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.776393711566925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6660003662109375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6587987542152405},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5877358317375183},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5846956968307495},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.545985996723175},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.523478627204895},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5028881430625916},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.436714231967926},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11114159226417542},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3343031.3350982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3343031.3350982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1982925187","https://openalex.org/W2096733369","https://openalex.org/W2101581379","https://openalex.org/W2108598243","https://openalex.org/W2116341502","https://openalex.org/W2149991777","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2209877316","https://openalex.org/W2467139031","https://openalex.org/W2468907370","https://openalex.org/W2510970676","https://openalex.org/W2584637367","https://openalex.org/W2585635281","https://openalex.org/W2597507805","https://openalex.org/W2598634450","https://openalex.org/W2606377603","https://openalex.org/W2736410039","https://openalex.org/W2738406610","https://openalex.org/W2768610172","https://openalex.org/W2772363116","https://openalex.org/W2779003141","https://openalex.org/W2788012242","https://openalex.org/W2795758732","https://openalex.org/W2798458055","https://openalex.org/W2798874329","https://openalex.org/W2883638665","https://openalex.org/W2896888563","https://openalex.org/W2962706983","https://openalex.org/W2962926870","https://openalex.org/W2963076818","https://openalex.org/W2963322158","https://openalex.org/W2963438548","https://openalex.org/W2963775347","https://openalex.org/W2964113829","https://openalex.org/W2964130064","https://openalex.org/W2964163358","https://openalex.org/W2964289004","https://openalex.org/W2964304299","https://openalex.org/W3099206234","https://openalex.org/W3100506510"],"related_works":["https://openalex.org/W4254879869","https://openalex.org/W3022576529","https://openalex.org/W2628526247","https://openalex.org/W3119773509","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Person":[0],"Re-identification":[1],"(ReID)":[2],"targets":[3],"to":[4,22,81],"identify":[5],"a":[6,19,31,38,55,116,182],"probe":[7],"person":[8,200],"appeared":[9],"under":[10],"multiple":[11],"camera":[12],"views.":[13],"Existing":[14],"methods":[15],"focus":[16],"on":[17,104,120,174,197],"proposing":[18],"robust":[20,183],"model":[21,212],"capture":[23],"the":[24,44,67,70,74,83,86,95,105,109,121,130,138,145,154,175,179,189,210],"discriminative":[25,185],"information.":[26],"However,":[27],"they":[28],"all":[29],"generate":[30],"representation":[32,89,186],"by":[33,108,143,168],"mining":[34],"useful":[35],"clues":[36],"from":[37,191],"given":[39,75],"single":[40],"image,":[41,76,94],"and":[42,77,148,162,184,206],"ignore":[43],"intercommunication":[45],"with":[46],"other":[47],"images.":[48,194],"To":[49],"address":[50],"this":[51],"issue,":[52],"we":[53],"propose":[54],"novel":[56],"network":[57],"named":[58],"Feature-Fusing":[59],"Graph":[60],"Neural":[61],"Network":[62],"(FFGNN),":[63],"which":[64,126,187],"fully":[65],"utilizes":[66],"relationships":[68],"among":[69],"nearest":[71,101],"neighbors":[72],"of":[73,85,132,137],"allows":[78],"message":[79,160],"propagation":[80,161],"update":[82],"feature":[84,106,131,164],"node":[87,128],"during":[88],"learning.":[90],"Given":[91],"an":[92,133],"anchor":[93],"FFGNN":[96,158,180],"firstly":[97],"obtains":[98],"its":[99,192],"Top-K":[100],"images":[102],"based":[103,119],"generated":[107],"trained":[110],"Feature-Extracting":[111],"Network(FEN).":[112],"We":[113],"then":[114],"construct":[115],"graph":[117,139,156,171],"G":[118,140],"obtained":[122,142],"K+1":[123],"images,":[124],"in":[125],"each":[127],"represents":[129],"image.":[134],"The":[135],"edge":[136],"is":[141],"combing":[144],"visual":[146],"similarity":[147,150],"Jaccard":[149],"between":[151,166],"nodes.":[152],"Within":[153],"constructed":[155],"G,":[157],"conducts":[159],"adaptive":[163],"fusion":[165],"nodes":[167],"iteratively":[169],"performing":[170],"convolutional":[172],"operation":[173],"input":[176],"features.":[177],"Finally,":[178],"outputs":[181],"contains":[188],"information":[190],"similar":[193],"Extensive":[195],"experiments":[196],"three":[198],"public":[199],"ReID":[201],"datasets":[202],"including":[203],"Market-1501,":[204],"DukeMTMC-ReID,":[205],"CUHK03":[207],"demonstrate":[208],"that":[209],"proposed":[211],"can":[213],"achieve":[214],"significant":[215],"improvement":[216],"against":[217],"state-of-the-art":[218],"methods.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
