{"id":"https://openalex.org/W2746086300","doi":"https://doi.org/10.1109/aipr.2016.8010583","title":"SPARCNN: Spatially related convolutional neural networks","display_name":"SPARCNN: Spatially related convolutional neural networks","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2746086300","doi":"https://doi.org/10.1109/aipr.2016.8010583","mag":"2746086300"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2016.8010583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2016.8010583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1708.07522","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033944727","display_name":"JT Turner","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140062","display_name":"Knexus Research (United States)","ror":"https://ror.org/044a15h91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140062"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J.T. Turner","raw_affiliation_strings":["Kuexus Research Corporation","Knexus Research Corporation; 174 Waterfront St. Suite 310; National Harbor, MD 20745"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kuexus Research Corporation","institution_ids":[]},{"raw_affiliation_string":"Knexus Research Corporation; 174 Waterfront St. Suite 310; National Harbor, MD 20745","institution_ids":["https://openalex.org/I4210140062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006386740","display_name":"Kalyan Moy Gupta","orcid":"https://orcid.org/0000-0003-3311-0030"},"institutions":[{"id":"https://openalex.org/I4210140062","display_name":"Knexus Research (United States)","ror":"https://ror.org/044a15h91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210140062"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kalyan Moy Gupta","raw_affiliation_strings":["Kuexus Research Corporation","Knexus Research Corporation; 174 Waterfront St. Suite 310; National Harbor, MD 20745"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kuexus Research Corporation","institution_ids":[]},{"raw_affiliation_string":"Knexus Research Corporation; 174 Waterfront St. Suite 310; National Harbor, MD 20745","institution_ids":["https://openalex.org/I4210140062"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063806691","display_name":"David W. Aha","orcid":null},"institutions":[{"id":"https://openalex.org/I1288214837","display_name":"United States Naval Research Laboratory","ror":"https://ror.org/04d23a975","country_code":"US","type":"facility","lineage":["https://openalex.org/I1288214837","https://openalex.org/I1330347796","https://openalex.org/I175003984","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Aha","raw_affiliation_strings":["Navy Center for Applied Research in Artificial Intelligence","Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Code 5514, Washington DC 20375"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Navy Center for Applied Research in Artificial Intelligence","institution_ids":[]},{"raw_affiliation_string":"Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Code 5514, Washington DC 20375","institution_ids":["https://openalex.org/I1288214837"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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":0.9988999962806702,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8043862581253052},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7196836471557617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7108480930328369},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6624824404716492},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.6460662484169006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5860928297042847},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5759344100952148},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5333912968635559},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49906492233276367},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4679504930973053},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45323845744132996},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.44441476464271545},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.43355679512023926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36818718910217285},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3306349515914917},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.0669482946395874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043862581253052},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7196836471557617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7108480930328369},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6624824404716492},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.6460662484169006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5860928297042847},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5759344100952148},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5333912968635559},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49906492233276367},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4679504930973053},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45323845744132996},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.44441476464271545},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.43355679512023926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36818718910217285},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3306349515914917},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0669482946395874},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/aipr.2016.8010583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2016.8010583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1708.07522","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.07522","pdf_url":"https://arxiv.org/pdf/1708.07522","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2746086300","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1708.07522.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1708.07522","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1708.07522","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1708.07522","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.07522","pdf_url":"https://arxiv.org/pdf/1708.07522","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2746086300.pdf","grobid_xml":"https://content.openalex.org/works/W2746086300.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W2049750909","https://openalex.org/W2112796928","https://openalex.org/W2151103935","https://openalex.org/W2168356304","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W1846926097","https://openalex.org/W2904487290","https://openalex.org/W2566270501","https://openalex.org/W2793853941","https://openalex.org/W2550475090","https://openalex.org/W3148401955","https://openalex.org/W2974281242","https://openalex.org/W3085023111","https://openalex.org/W3105979354","https://openalex.org/W2592414189","https://openalex.org/W2295107390","https://openalex.org/W3162929401","https://openalex.org/W3124303138","https://openalex.org/W4225696660","https://openalex.org/W2752714822","https://openalex.org/W2463993963","https://openalex.org/W2611781169","https://openalex.org/W2781459369","https://openalex.org/W2972314911","https://openalex.org/W3093510833"],"abstract_inverted_index":{"The":[0],"ability":[1],"to":[2,17,39,59,126,151],"accurately":[3],"detect":[4],"and":[5,35,42,93,107,186],"classify":[6],"objects":[7,52,185],"at":[8,82],"varying":[9],"pixel":[10],"sizes":[11],"in":[12,53,175],"cluttered":[13,41],"scenes":[14],"is":[15,179],"crucial":[16],"many":[18],"Navy":[19],"applications.":[20],"However,":[21],"detection":[22,44,62,92],"performance":[23,168],"of":[24,79,112,134,170],"existing":[25,74],"state-of-the-art":[26,129],"approaches":[27],"such":[28,40],"as":[29],"convolutional":[30],"neural":[31],"networks":[32],"(CNNs)":[33],"degrade":[34],"suffer":[36],"when":[37,149,172],"applied":[38],"multi-object":[43],"tasks.":[45],"We":[46,89],"conjecture":[47],"that":[48,66,105,142,156],"spatial":[49,114,160],"relationships":[50],"between":[51],"an":[54,64],"image":[55,188],"could":[56],"be":[57],"exploited":[58],"significantly":[60],"improve":[61],"accuracy,":[63],"approach":[65],"had":[67],"not":[68,158],"yet":[69],"been":[70],"considered":[71],"by":[72,147,181],"any":[73],"techniques":[75],"(to":[76],"the":[77,83,85,137,176],"best":[78],"our":[80],"knowledge)":[81],"time":[84],"research":[86],"was":[87],"conducted.":[88],"introduce":[90],"a":[91,109,152,166],"classification":[94,145],"technique":[95,155],"called":[96],"Spatially":[97],"Related":[98],"Detection":[99],"with":[100,128],"Convolutional":[101],"Neural":[102],"Networks":[103],"(SPARCNN)":[104],"learns":[106],"exploits":[108],"probabilistic":[110],"representation":[111],"inter-object":[113],"configurations":[115],"within":[116],"images":[117],"from":[118],"training":[119],"sets":[120],"for":[121],"more":[122],"effective":[123],"region":[124,153],"proposals":[125],"use":[127],"CNNs.":[130],"Our":[131],"empirical":[132],"evaluation":[133],"SPARCNN":[135],"on":[136],"VOC":[138],"2007":[139],"dataset":[140],"shows":[141],"it":[143],"increases":[144],"accuracy":[146],"8%":[148],"compared":[150],"proposal":[154],"does":[157],"exploit":[159],"relations.":[161],"More":[162],"importantly,":[163],"we":[164],"obtained":[165],"higher":[167],"boost":[169],"18.8%":[171],"task":[173],"difficulty":[174],"test":[177],"set":[178],"increased":[180,187],"including":[182],"highly":[183],"obscured":[184],"clutter.":[189]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}