{"id":"https://openalex.org/W3001833697","doi":"https://doi.org/10.1109/sccc49216.2019.8966436","title":"DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection","display_name":"DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3001833697","doi":"https://doi.org/10.1109/sccc49216.2019.8966436","mag":"3001833697"},"language":"en","primary_location":{"id":"doi:10.1109/sccc49216.2019.8966436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sccc49216.2019.8966436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 38th International Conference of the Chilean Computer Science Society (SCCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003450858","display_name":"Fabian Souto Herrera","orcid":null},"institutions":[{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Fabian Souto Herrera","raw_affiliation_strings":["Universidad de Chile, Santiago, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Chile, Santiago, Chile","institution_ids":["https://openalex.org/I69737025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027890236","display_name":"Jos\u00e9 M. Saavedra","orcid":"https://orcid.org/0000-0002-9644-5164"},"institutions":[{"id":"https://openalex.org/I4210095293","display_name":"Institute for Diagnostic Imaging and Research of the Skin and Soft Tissues","ror":"https://ror.org/00trkmr27","country_code":"CL","type":"other","lineage":["https://openalex.org/I4210095293"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Jose M. Saavedra","raw_affiliation_strings":["Computer Vision Group Orand S.A., Santiago, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision Group Orand S.A., Santiago, Chile","institution_ids":["https://openalex.org/I4210095293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40797425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"abs 1902 9843","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9976000189781189,"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":0.9976000189781189,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9941999912261963,"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":0.9929999709129333,"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/computer-science","display_name":"Computer science","score":0.7028185725212097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6217248439788818},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6171764731407166},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6017841696739197},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5896733999252319},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5633941292762756},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5483232140541077},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5423195958137512},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46736857295036316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4590228497982025},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4497414529323578},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4381171762943268},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.42773962020874023},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40101781487464905},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18587803840637207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16817250847816467},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07291600108146667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7028185725212097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6217248439788818},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6171764731407166},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6017841696739197},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5896733999252319},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5633941292762756},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5483232140541077},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5423195958137512},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46736857295036316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4590228497982025},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4497414529323578},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4381171762943268},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.42773962020874023},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40101781487464905},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18587803840637207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16817250847816467},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07291600108146667},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sccc49216.2019.8966436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sccc49216.2019.8966436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 38th International Conference of the Chilean Computer Science Society (SCCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6700000166893005,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W1980972548","https://openalex.org/W2031489346","https://openalex.org/W2194775991","https://openalex.org/W2396419813","https://openalex.org/W2565639579","https://openalex.org/W2574981201","https://openalex.org/W2737691244","https://openalex.org/W2796347433","https://openalex.org/W2884561390","https://openalex.org/W2899771611","https://openalex.org/W2907225497","https://openalex.org/W2949935872","https://openalex.org/W2963351448","https://openalex.org/W2963487083","https://openalex.org/W3099319035","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6687483927","https://openalex.org/W6730903564","https://openalex.org/W6741774873","https://openalex.org/W6750227808","https://openalex.org/W6756040250","https://openalex.org/W6785062762"],"related_works":["https://openalex.org/W2389818373","https://openalex.org/W2220831889","https://openalex.org/W4312683641","https://openalex.org/W3027421045","https://openalex.org/W2576320324","https://openalex.org/W3013312691","https://openalex.org/W2980386803","https://openalex.org/W3215994059","https://openalex.org/W2319823519","https://openalex.org/W4206798987"],"abstract_inverted_index":{"We":[0,23,66],"propose":[1],"modifications":[2],"to":[3,11,91,120],"the":[4,13,18,28,36,45,48,53,56,61,64,71,83,92,110,113],"RetinaNet":[5,118],"architecture":[6,125],"and":[7,51,108],"Focal":[8],"Loss":[9],"[1]":[10,119],"improve":[12,60],"effectiveness":[14],"of":[15,20,55,63,82,98,102,106],"them":[16,40],"in":[17,35],"context":[19],"object":[21],"detection.":[22],"show":[24],"that":[25,75],"through":[26],"normalizing":[27],"embeddings":[29],"generated":[30],"by":[31],"each":[32,80],"receptive":[33],"field":[34],"feature":[37],"map,":[38],"classifying":[39],"using":[41,122],"cosine":[42],"similarity,":[43],"increasing":[44],"loss":[46],"for":[47,79],"foreground":[49],"anchors":[50],"forcing":[52],"dispersion":[54],"classification":[57],"vectors":[58],"will":[59],"performance":[62],"model.":[65],"test":[67],"our":[68,123],"proposal":[69],"with":[70,89,95,117],"FlickrLogos-32":[72],"[2]":[73],"dataset":[74,114],"contains":[76],"40":[77],"examples":[78],"one":[81],"32":[84],"classes,":[85],"achieving":[86],"competitive":[87],"results":[88],"respect":[90],"state-of-the-art":[93],"approaches":[94],"a":[96,100],"precision":[97],"0.975,":[99],"recall":[101],"0.944,":[103],"an":[104],"accuracy":[105],"0.949":[107],"improving":[109],"mAP":[111],"over":[112],"from":[115],"0.657":[116],"0.775":[121],"new":[124],"DLDENet.":[126]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}