{"id":"https://openalex.org/W4377236329","doi":"https://doi.org/10.1145/3581807.3581814","title":"Improving Pedestrian Attribute Recognition with Dual Adaptive Fusion Attention","display_name":"Improving Pedestrian Attribute Recognition with Dual Adaptive Fusion Attention","publication_year":2022,"publication_date":"2022-11-17","ids":{"openalex":"https://openalex.org/W4377236329","doi":"https://doi.org/10.1145/3581807.3581814"},"language":"en","primary_location":{"id":"doi:10.1145/3581807.3581814","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581807.3581814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","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/A5020957066","display_name":"Wenbiao Xie","orcid":"https://orcid.org/0000-0003-3555-8306"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenbiao Xie","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019431857","display_name":"Chen Zou","orcid":"https://orcid.org/0000-0002-4964-5884"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zou","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018485737","display_name":"Chengui Fu","orcid":"https://orcid.org/0000-0002-5405-9812"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengui Fu","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113144809","display_name":"X. Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomei Xie","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015766535","display_name":"Qiuming Liu","orcid":"https://orcid.org/0000-0001-9844-693X"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiuming Liu","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101897275","display_name":"He Xiao","orcid":"https://orcid.org/0000-0002-8293-1727"},"institutions":[{"id":"https://openalex.org/I4510145","display_name":"Jiangxi University of Science and Technology","ror":"https://ror.org/03q0t9252","country_code":"CN","type":"education","lineage":["https://openalex.org/I4510145"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Xiao","raw_affiliation_strings":["Jiangxi University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Science and Technology, China","institution_ids":["https://openalex.org/I4510145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5020957066"],"corresponding_institution_ids":["https://openalex.org/I4510145"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17352887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7839447259902954},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6689491271972656},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6294583678245544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6138036847114563},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5803251266479492},{"id":"https://openalex.org/keywords/information-fusion","display_name":"Information fusion","score":0.5195682048797607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4992208480834961},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4651055932044983},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.43819326162338257},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43572354316711426},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41671180725097656},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4121038019657135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39739999175071716},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35531753301620483},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3523157835006714},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06869086623191833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839447259902954},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6689491271972656},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6294583678245544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6138036847114563},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5803251266479492},{"id":"https://openalex.org/C2982962833","wikidata":"https://www.wikidata.org/wiki/Q17092450","display_name":"Information fusion","level":2,"score":0.5195682048797607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4992208480834961},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4651055932044983},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.43819326162338257},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43572354316711426},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41671180725097656},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4121038019657135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39739999175071716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35531753301620483},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3523157835006714},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06869086623191833},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581807.3581814","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581807.3581814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3186864891","display_name":null,"funder_award_id":"20202BAB212003","funder_id":"https://openalex.org/F4320322665","funder_display_name":"Natural Science Foundation of Jiangxi Province"}],"funders":[{"id":"https://openalex.org/F4320322665","display_name":"Natural Science Foundation of Jiangxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522973599","https://openalex.org/W1907729166","https://openalex.org/W2058102599","https://openalex.org/W2111025459","https://openalex.org/W2286727787","https://openalex.org/W2410968923","https://openalex.org/W2465503160","https://openalex.org/W2549139847","https://openalex.org/W2565639579","https://openalex.org/W2604463754","https://openalex.org/W2613151562","https://openalex.org/W2896249043","https://openalex.org/W2954148997","https://openalex.org/W2963007689","https://openalex.org/W2963091558","https://openalex.org/W2963156682","https://openalex.org/W2963365374","https://openalex.org/W2963544187","https://openalex.org/W2963790258","https://openalex.org/W2965153332","https://openalex.org/W2981689412","https://openalex.org/W2986999591","https://openalex.org/W3013839707","https://openalex.org/W3018746726","https://openalex.org/W3041491555","https://openalex.org/W3103850820","https://openalex.org/W3110275472","https://openalex.org/W3132834324","https://openalex.org/W3187415662"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039","https://openalex.org/W2726747157","https://openalex.org/W2010131506","https://openalex.org/W3088112989"],"abstract_inverted_index":{"As":[0],"one":[1],"of":[2,6,36,78],"the":[3,68,76,85,114,121,135],"important":[4],"fields":[5],"computer":[7],"vision":[8],"research,":[9],"pedestrian":[10,26],"attribute":[11,43,91],"recognition":[12,44],"has":[13,31,137],"received":[14],"increasing":[15],"attention":[16,72,99],"on":[17,28,113],"researchers":[18],"at":[19],"domestic":[20],"and":[21,41,57,89,118,127],"foreign.":[22],"However,":[23],"obtaining":[24],"long-distance":[25],"information":[27],"actual":[29],"scenes":[30],"problems,":[32],"such":[33],"as":[34],"lack":[35],"information,":[37,104],"incomplete":[38],"feature":[39],"extraction,":[40],"low":[42],"accuracy.":[45],"To":[46],"address":[47],"these":[48],"issues,":[49],"we":[50,96],"proposed":[51],"a":[52],"Dual":[53],"Adaptive":[54],"Fusion":[55],"Attention":[56,59],"Criss-Cross":[58],"Module":[60],"(DAFCC).":[61],"This":[62],"module":[63,73],"contains":[64],"two":[65],"sub-modules:":[66],"First,":[67],"dual":[69],"adaptive":[70],"fusion":[71,84],"automatically":[74],"adjusts":[75],"weights":[77],"attributes":[79],"in":[80],"different":[81,86],"scales,":[82],"then":[83],"scale":[87],"features":[88],"makes":[90],"extraction":[92],"more":[93],"complete.":[94],"Second,":[95],"employ":[97],"criss-cross":[98],"to":[100],"extract":[101],"rich":[102],"contextual":[103],"which":[105],"is":[106],"beneficial":[107],"for":[108],"visual":[109],"understanding.":[110],"By":[111],"training":[112],"public":[115],"PA-100K,":[116],"RAP":[117],"PETA":[119],"datasets,":[120],"mean":[122],"accuracies":[123],"achieved":[124],"81.09%,":[125],"81.44%":[126],"85.94%,":[128],"respectively.":[129],"Extensive":[130],"experimental":[131],"results":[132],"show":[133],"that":[134],"method":[136],"strong":[138],"competitiveness":[139],"among":[140],"many":[141],"current":[142],"classical":[143],"algorithms.":[144]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
