{"id":"https://openalex.org/W3089601163","doi":"https://doi.org/10.1109/icip40778.2020.9191170","title":"Visual Relationship Classification With Negative-Sample Mining","display_name":"Visual Relationship Classification With Negative-Sample Mining","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3089601163","doi":"https://doi.org/10.1109/icip40778.2020.9191170","mag":"3089601163"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9191170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5072424222","display_name":"Roberto de Moura Estev\u00e3o Filho","orcid":"https://orcid.org/0000-0002-3502-4892"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Roberto de M. Estevao Filho","raw_affiliation_strings":["Electrical Engineering Program, Federal University of Rio de Janeiro COPPE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program, Federal University of Rio de Janeiro COPPE","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027382066","display_name":"Jos\u00e9 Gabriel R. C. Gomes","orcid":"https://orcid.org/0000-0003-3242-023X"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jose Gabriel R.C. Gomes","raw_affiliation_strings":["Electrical Engineering Program, Federal University of Rio de Janeiro COPPE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program, Federal University of Rio de Janeiro COPPE","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021370218","display_name":"Leonardo de O. Nunes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leonardo de O. Nunes","raw_affiliation_strings":["Microsoft Advanced Technology Labs, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Advanced Technology Labs, Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2251","last_page":"2255"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.998199999332428,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9980000257492065,"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":0.7930984497070312},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6933719515800476},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6714326739311218},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5923925638198853},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5740121603012085},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5375359654426575},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.517688512802124},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4755760729312897},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.44902878999710083},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.43791669607162476},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43083101511001587},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.42663300037384033},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4217953383922577},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4136897325515747},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4019124507904053},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3838442862033844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08640998601913452}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7930984497070312},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6933719515800476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6714326739311218},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5923925638198853},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5740121603012085},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5375359654426575},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.517688512802124},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4755760729312897},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.44902878999710083},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.43791669607162476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43083101511001587},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42663300037384033},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4217953383922577},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4136897325515747},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4019124507904053},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3838442862033844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08640998601913452},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9191170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W639708223","https://openalex.org/W1614298861","https://openalex.org/W1799366690","https://openalex.org/W2017313218","https://openalex.org/W2077069816","https://openalex.org/W2108598243","https://openalex.org/W2117287331","https://openalex.org/W2117539524","https://openalex.org/W2160254296","https://openalex.org/W2194775991","https://openalex.org/W2277195237","https://openalex.org/W2479423890","https://openalex.org/W2506483933","https://openalex.org/W2591644541","https://openalex.org/W2605736949","https://openalex.org/W2607855566","https://openalex.org/W2740962769","https://openalex.org/W2754191212","https://openalex.org/W2777602943","https://openalex.org/W2789177853","https://openalex.org/W2899505139","https://openalex.org/W2949474740","https://openalex.org/W2950577311","https://openalex.org/W2962737704","https://openalex.org/W2963097937","https://openalex.org/W2963150697","https://openalex.org/W2963184176","https://openalex.org/W2963341628","https://openalex.org/W2963623257","https://openalex.org/W2963650529","https://openalex.org/W2963683498","https://openalex.org/W3121480429","https://openalex.org/W6604254268","https://openalex.org/W6620707391","https://openalex.org/W6636510571","https://openalex.org/W6638444622","https://openalex.org/W6734384760","https://openalex.org/W6753441378","https://openalex.org/W6788995615"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W2113666009","https://openalex.org/W2145649715","https://openalex.org/W3195209712","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"the":[3,65,72],"application":[4],"of":[5,35],"a":[6,11,28,58,93,104],"visual":[7],"relationship":[8],"classifier":[9],"as":[10,37],"standalone":[12],"system":[13],"that":[14,31],"is":[15],"meant":[16],"to":[17,42,81,109],"be":[18],"used":[19],"with":[20,57],"external":[21],"detectors.":[22],"Through":[23],"these":[24],"lens,":[25],"we":[26],"propose":[27],"training":[29,67],"scheme":[30],"uses":[32],"unannotated":[33],"pairs":[34,98],"objects":[36],"negative":[38],"samples":[39],"in":[40,53,89],"order":[41],"improve":[43,77],"precision.":[44],"The":[45],"proposed":[46,66],"network":[47],"architecture":[48,70],"incorporates":[49],"common":[50],"techniques":[51],"presented":[52],"related":[54],"state-of-the-art":[55],"solutions":[56],"novel":[59],"positional":[60],"encoding":[61],"scheme.":[62],"We":[63],"evaluate":[64],"method":[68,102],"and":[69,76],"on":[71],"Open":[73],"Images":[74],"dataset":[75],"mAP":[78],"from":[79,107],"34.6%":[80],"78.2%":[82],"when":[83],"considering":[84],"all":[85],"possible":[86],"object":[87],"pairings":[88],"each":[90],"image.":[91],"For":[92],"case":[94],"where":[95],"only":[96],"ground-truth":[97],"are":[99],"considered,":[100],"our":[101],"presents":[103],"small":[105],"decrease,":[106],"91.0%":[108],"88.8%":[110],"mAP.":[111]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
