{"id":"https://openalex.org/W4313032837","doi":"https://doi.org/10.1109/tcsvt.2022.3221658","title":"A Dense-Aware Cross-splitNet for Object Detection and Recognition","display_name":"A Dense-Aware Cross-splitNet for Object Detection and Recognition","publication_year":2022,"publication_date":"2022-11-10","ids":{"openalex":"https://openalex.org/W4313032837","doi":"https://doi.org/10.1109/tcsvt.2022.3221658"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2022.3221658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3221658","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-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/A5101624040","display_name":"Shengye Wang","orcid":"https://orcid.org/0000-0003-3723-6853"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng-Ye Wang","raw_affiliation_strings":["College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001474262","display_name":"Zhong Qu","orcid":"https://orcid.org/0000-0001-7013-4854"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Qu","raw_affiliation_strings":["College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-7013-4854","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083903103","display_name":"Cui\u2010Jin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cui-Jin Li","raw_affiliation_strings":["College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":2.3351,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.90047013,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"33","issue":"5","first_page":"2290","last_page":"2301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9961000084877014,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7524487972259521},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7491938471794128},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7451367378234863},{"id":"https://openalex.org/keywords/subnet","display_name":"Subnet","score":0.6338415145874023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6218647956848145},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5971326231956482},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5715417265892029},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5692800879478455},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.551752507686615},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.48972705006599426},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.45878082513809204},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44289955496788025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15649384260177612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524487972259521},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7491938471794128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7451367378234863},{"id":"https://openalex.org/C21099817","wikidata":"https://www.wikidata.org/wiki/Q7631721","display_name":"Subnet","level":2,"score":0.6338415145874023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6218647956848145},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5971326231956482},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5715417265892029},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5692800879478455},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.551752507686615},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.48972705006599426},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.45878082513809204},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44289955496788025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15649384260177612},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2022.3221658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3221658","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4126981573","display_name":null,"funder_award_id":"62176034","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5718780978","display_name":null,"funder_award_id":"BYJS202114","funder_id":"https://openalex.org/F4320322687","funder_display_name":"Chongqing University of Posts and Telecommunications"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322687","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2092377008","https://openalex.org/W2205506889","https://openalex.org/W2412782625","https://openalex.org/W2565639579","https://openalex.org/W2806070179","https://openalex.org/W2884530474","https://openalex.org/W2886335102","https://openalex.org/W2892761015","https://openalex.org/W2904560314","https://openalex.org/W2924873663","https://openalex.org/W2928165649","https://openalex.org/W2934198733","https://openalex.org/W2950800384","https://openalex.org/W2962721361","https://openalex.org/W2962766617","https://openalex.org/W2963625188","https://openalex.org/W2963927307","https://openalex.org/W2971307267","https://openalex.org/W2982770724","https://openalex.org/W2984009799","https://openalex.org/W2989604896","https://openalex.org/W2997747012","https://openalex.org/W3018757597","https://openalex.org/W3034024180","https://openalex.org/W3046032854","https://openalex.org/W3046565475","https://openalex.org/W3092663126","https://openalex.org/W3102710196","https://openalex.org/W3106250896","https://openalex.org/W3106754126","https://openalex.org/W3109385692","https://openalex.org/W3120822260","https://openalex.org/W3131500599","https://openalex.org/W3138516171","https://openalex.org/W3151841177","https://openalex.org/W3172087149","https://openalex.org/W3172752666","https://openalex.org/W3184439416","https://openalex.org/W3185043317","https://openalex.org/W3194790201","https://openalex.org/W4220773165","https://openalex.org/W4285123781","https://openalex.org/W4307293821","https://openalex.org/W6639102338","https://openalex.org/W6714138976","https://openalex.org/W6760424586","https://openalex.org/W6770715449","https://openalex.org/W6777046832","https://openalex.org/W6779586474","https://openalex.org/W6779679448","https://openalex.org/W6785652829","https://openalex.org/W6794322471","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W2102539527","https://openalex.org/W2131631951","https://openalex.org/W2356206668","https://openalex.org/W2130707537","https://openalex.org/W3200778902","https://openalex.org/W1977409556","https://openalex.org/W2361602549","https://openalex.org/W4362683600","https://openalex.org/W2105155969","https://openalex.org/W4394867575"],"abstract_inverted_index":{"Object":[0],"detection":[1,60,75,220],"and":[2,10,33,76,154,167,187],"recognition":[3,77],"is":[4,89,112],"widely":[5],"used":[6],"in":[7,15,22,46],"various":[8,100,126],"fields":[9],"have":[11,53],"become":[12],"key":[13],"technologies":[14],"computer":[16],"vision.":[17],"The":[18,157,212],"distribution":[19],"of":[20,43,104,123,176,199,217],"objects":[21,32,45],"natural":[23],"images":[24,124],"can":[25],"be":[26],"roughly":[27],"divided":[28],"into":[29,95],"densely":[30,47],"stacked":[31,48],"scattered":[34],"objects.":[35],"Due":[36],"to":[37,114,120,129,144],"the":[38,102,105,108,116,162,188,208,218,226],"incomplete":[39],"attributes":[40],"or":[41,58],"features":[42,122],"some":[44,50],"distributions,":[49],"object":[51,74,131,152,177,219],"detectors":[52],"missed":[54],"local":[55],"area":[56],"details":[57],"low":[59],"accuracy.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65,136],"propose":[66],"Cross-splitNet,":[67],"a":[68,138,173],"novel":[69],"cross-split":[70,110],"method":[71],"for":[72],"dense":[73],"based":[78],"on":[79,161,207,225],"candidate":[80],"box":[81],"generation.":[82],"First,":[83],"an":[84,195],"adaptive":[85],"feature":[86,139,147],"extraction":[87,148],"network":[88,141,180],"constructed.":[90],"Different":[91],"datasets":[92],"are":[93],"input":[94],"convolutional":[96],"neural":[97],"networks":[98,119],"with":[99,125,183,222],"depths,":[101],"generalization":[103],"model.":[106],"Then,":[107],"proposed":[109],"algorithm":[111],"introduced":[113],"guide":[115],"different":[117],"deep":[118],"learn":[121],"densities,":[127],"according":[128],"intermediate":[130],"density":[132],"classification":[133],"results.":[134],"Finally,":[135],"adopt":[137],"pyramid":[140],"(FPN)":[142],"subnet":[143],"perform":[145],"multi-scale":[146],"while":[149],"retaining":[150],"lower-layer":[151],"information":[153],"physical":[155],"characteristics.":[156],"model":[158,193,221],"was":[159,181,229],"trained":[160],"COCO":[163,227],"17,":[164],"VOC":[165,168,209],"12,":[166],"07":[169],"datasets,":[170],"which":[171],"contain":[172],"large":[174],"number":[175],"categories.":[178],"Our":[179],"compared":[182],"several":[184],"two-stage":[185],"detectors,":[186],"results":[189],"show":[190],"that":[191],"our":[192],"achieved":[194],"average":[196,214],"precision":[197,215],"(AP)":[198],"0.819":[200],"at":[201],"22.9":[202],"frames":[203],"per":[204],"second":[205],"(FPS)":[206],"07+12":[210],"dataset.":[211],"mean":[213],"(mAP)":[216],"R50+R2-101":[223],"backbones":[224],"dataset":[228],"increased":[230],"by":[231],"1.9%.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
