{"id":"https://openalex.org/W4304098616","doi":"https://doi.org/10.1145/3503161.3547863","title":"CrossNet: Boosting Crowd Counting with Localization","display_name":"CrossNet: Boosting Crowd Counting with Localization","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304098616","doi":"https://doi.org/10.1145/3503161.3547863"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547863","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th 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/A5101787171","display_name":"Ji Zhang","orcid":"https://orcid.org/0000-0001-6141-239X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ji Zhang","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058898461","display_name":"Zhi-Qi Cheng","orcid":"https://orcid.org/0000-0002-1720-2085"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi-Qi Cheng","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011680564","display_name":"Xiao Wu","orcid":"https://orcid.org/0000-0002-8322-8558"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wu","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100318291","display_name":"Wei Li","orcid":"https://orcid.org/0000-0002-8278-1765"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045727713","display_name":"Jian-Jun Qiao","orcid":"https://orcid.org/0000-0003-4282-0149"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Jun Qiao","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101787171"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":1.0138,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.84185122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6436","last_page":"6444"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.7553744912147522},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6456742882728577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6172677278518677},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5889371633529663},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.557267963886261},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4678497910499573},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4176156520843506},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.347156822681427},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11587902903556824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7553744912147522},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6456742882728577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6172677278518677},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5889371633529663},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.557267963886261},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4678497910499573},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4176156520843506},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.347156822681427},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11587902903556824},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547863","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G491161220","display_name":null,"funder_award_id":"1772436,2001400","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W607748843","https://openalex.org/W639708223","https://openalex.org/W1910776219","https://openalex.org/W2463631526","https://openalex.org/W2613718673","https://openalex.org/W2745597836","https://openalex.org/W2886443245","https://openalex.org/W2949285970","https://openalex.org/W3007175960","https://openalex.org/W3010021361","https://openalex.org/W3014641072","https://openalex.org/W3015977732","https://openalex.org/W3027606690","https://openalex.org/W3034785991","https://openalex.org/W3035193053","https://openalex.org/W3045455261","https://openalex.org/W3110700482","https://openalex.org/W3113251869","https://openalex.org/W3119860245","https://openalex.org/W3126263946","https://openalex.org/W3127995199","https://openalex.org/W3170328766","https://openalex.org/W3174519905","https://openalex.org/W3175657805","https://openalex.org/W3175725565","https://openalex.org/W3176047859","https://openalex.org/W3176458063","https://openalex.org/W3177167987","https://openalex.org/W3190723141","https://openalex.org/W3192782181","https://openalex.org/W3203845557","https://openalex.org/W3207203470","https://openalex.org/W4289709982"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2378211422","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2571255492"],"abstract_inverted_index":{"Generating":[0],"high-quality":[1,107],"density":[2,48,69,108,132,162],"maps":[3,70,109],"is":[4,12,59,80,103,121,154],"a":[5,53,76],"crucial":[6],"step":[7],"in":[8,47,198],"crowd":[9,37,62,161,199,202],"counting.":[10],"It":[11],"obvious":[13],"that":[14,189],"exploiting":[15],"the":[16,20,25,30,84,90,96,131,134,140,145,160,190,194],"head":[17],"location":[18,46,66,146],"of":[19,32,87,92,133,144],"people":[21],"can":[22,128],"naturally":[23],"highlight":[24],"crowded":[26],"area":[27],"and":[28,173,201],"eliminate":[29],"interference":[31],"background":[33],"noise.":[34],"However,":[35],"existing":[36],"counting":[38,135,200],"methods":[39],"are":[40],"still":[41],"tricky":[42],"to":[43,82,105,123,137,156,159],"reasonably":[44],"use":[45,130],"generation.":[49],"In":[50],"this":[51],"paper,":[52],"novel":[54],"location-guided":[55],"framework":[56],"named":[57],"CrossNet":[58],"proposed":[60,81,191],"for":[61],"counting,":[63],"which":[64,127,164],"integrates":[65],"supervision":[67,93],"into":[68],"through":[71],"dual-branch":[72],"joint":[73],"training.":[74],"First,":[75],"new":[77],"branching":[78],"network":[79],"localize":[83],"potential":[85],"positions":[86],"pedestrians.":[88],"With":[89],"help":[91],"induced":[94],"from":[95],"localization":[97,125],"branch,":[98],"Location":[99],"Enhancement":[100],"(LE)":[101],"module":[102,120],"designed":[104],"obtain":[106],"by":[110],"positioning":[111],"foreground":[112],"regions.":[113],"Second,":[114],"Adaptive":[115],"Density":[116,149],"Awareness":[117,150],"Attention":[118],"(ADAA)":[119],"engaged":[122],"enhance":[124],"accuracy,":[126],"efficiently":[129],"branch":[136],"adaptively":[138],"capture":[139],"error-prone":[141],"dense":[142],"areas":[143,177],"maps.":[147],"Finally,":[148],"Localization":[151],"(DAL)":[152],"loss":[153],"offered":[155],"allocate":[157],"attention":[158],"levels,":[163],"delivers":[165],"more":[166],"focus":[167],"on":[168,176,184],"regions":[169],"with":[170,178],"high":[171],"densities":[172],"less":[174],"concentration":[175],"low":[179],"densities.":[180],"Extensive":[181],"experiments":[182],"conducted":[183],"four":[185],"benchmark":[186],"datasets":[187],"demonstrate":[188],"method":[192],"outperforms":[193],"state-of-the-art":[195],"approaches":[196],"both":[197],"localization.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
