{"id":"https://openalex.org/W3158534061","doi":"https://doi.org/10.1145/3444685.3446312","title":"Relationship graph learning network for visual relationship detection","display_name":"Relationship graph learning network for visual relationship detection","publication_year":2021,"publication_date":"2021-03-07","ids":{"openalex":"https://openalex.org/W3158534061","doi":"https://doi.org/10.1145/3444685.3446312","mag":"3158534061"},"language":"en","primary_location":{"id":"doi:10.1145/3444685.3446312","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","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/A5115602129","display_name":"Yanan Li","orcid":"https://orcid.org/0000-0003-2886-1585"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanan Li","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050817770","display_name":"Jun Yu","orcid":"https://orcid.org/0000-0003-1922-7283"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yu","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074672983","display_name":"Yibing Zhan","orcid":"https://orcid.org/0000-0003-3180-0484"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibing Zhan","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100456847","display_name":"Zhi Chen","orcid":"https://orcid.org/0000-0003-2911-5234"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Chen","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5115602129"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46447712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","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/T11714","display_name":"Multimodal Machine Learning Applications","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9986000061035156,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983000159263611,"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/subnetwork","display_name":"Subnetwork","score":0.8214669227600098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6426008939743042},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6111376881599426},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5036823153495789},{"id":"https://openalex.org/keywords/spatial-relationship","display_name":"Spatial relationship","score":0.5025343894958496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5007872581481934},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4392753839492798},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.432054728269577},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4242880642414093},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42370089888572693},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4147430658340454},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35808998346328735},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3058493733406067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1793988049030304}],"concepts":[{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.8214669227600098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6426008939743042},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6111376881599426},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5036823153495789},{"id":"https://openalex.org/C2986522900","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relationship","level":2,"score":0.5025343894958496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5007872581481934},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4392753839492798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.432054728269577},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4242880642414093},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42370089888572693},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4147430658340454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35808998346328735},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3058493733406067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1793988049030304},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3444685.3446312","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1931639407","https://openalex.org/W2029257328","https://openalex.org/W2108598243","https://openalex.org/W2250539671","https://openalex.org/W2277195237","https://openalex.org/W2479423890","https://openalex.org/W2579549467","https://openalex.org/W2591644541","https://openalex.org/W2605736949","https://openalex.org/W2613718673","https://openalex.org/W2788537604","https://openalex.org/W2883170015","https://openalex.org/W2886970679","https://openalex.org/W2897457738","https://openalex.org/W2955988340","https://openalex.org/W2962737704","https://openalex.org/W2962785943","https://openalex.org/W2962845008","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2963403868","https://openalex.org/W2963514444","https://openalex.org/W2963536419","https://openalex.org/W2963650529","https://openalex.org/W2963683498","https://openalex.org/W2963938081","https://openalex.org/W2964080601","https://openalex.org/W2980248933","https://openalex.org/W2981319334","https://openalex.org/W2987123286","https://openalex.org/W2998660469","https://openalex.org/W3019723613","https://openalex.org/W3034538190","https://openalex.org/W3035017890","https://openalex.org/W3045725402","https://openalex.org/W3054755450","https://openalex.org/W3101487523"],"related_works":["https://openalex.org/W2060724872","https://openalex.org/W2082094785","https://openalex.org/W2202198356","https://openalex.org/W3087203342","https://openalex.org/W2377184161","https://openalex.org/W228984114","https://openalex.org/W4226360758","https://openalex.org/W2151093953","https://openalex.org/W2090026684","https://openalex.org/W2907567977"],"abstract_inverted_index":{"Visual":[0],"relationship":[1,39,60,74,93,96,100,119,152],"detection":[2,40],"aims":[3],"to":[4,65,117,155],"predict":[5],"the":[6,17,44,49,67,104,138,173,176,184,187],"relationships":[7,25,53,71,130],"between":[8,19,26],"detected":[9],"object":[10,83,160],"pairs.":[11],"It":[12],"is":[13],"well":[14],"believed":[15],"that":[16],"correlations":[18,45,50,68,128,141],"image":[20,79],"components":[21],"(i.e.,":[22],"objects":[23,80,90],"and":[24,48,85,134,137,145,175],"objects)":[27],"are":[28,106],"significant":[29],"considerations":[30],"when":[31],"predicting":[32],"objects'":[33,52,70],"relationships.":[34,164],"However,":[35],"most":[36],"current":[37],"visual":[38,73,133],"methods":[41],"only":[42],"exploited":[43],"among":[46,51,69,129],"objects,":[47],"remained":[54],"underexplored.":[55],"This":[56],"paper":[57],"proposes":[58],"a":[59,92,99,151],"graph":[61,114,124],"learning":[62],"network":[63],"(RGLN)":[64],"explore":[66],"for":[72],"detection.":[75],"Specifically,":[76],"RGLN":[77,111,149],"obtains":[78],"using":[81],"an":[82],"detector,":[84],"then,":[86],"every":[87],"pair":[88],"of":[89,159,189],"constitutes":[91],"proposal.":[94],"All":[95],"proposals":[97,105],"construct":[98],"graph,":[101],"in":[102,121],"which":[103,122],"treated":[107],"as":[108],"nodes.":[109],"Accordingly,":[110],"designs":[112],"bi-stream":[113],"attention":[115,125],"subnetworks":[116],"detect":[118],"proposals,":[120],"one":[123],"subnetwork":[126,154],"analyzes":[127,140],"based":[131,142],"on":[132,143,169],"spatial":[135,146],"information,":[136],"other":[139],"semantic":[144],"information.":[147],"Besides,":[148],"exploits":[150],"selection":[153],"ignore":[156],"redundant":[157],"information":[158],"pairs":[161],"with":[162,183],"no":[163],"We":[165],"conduct":[166],"extensive":[167],"experiments":[168],"two":[170],"public":[171],"datasets:":[172],"VRD":[174],"VG":[177],"datasets.":[178],"The":[179],"experimental":[180],"results":[181],"compared":[182],"state-of-the-art":[185],"demonstrate":[186],"competitiveness":[188],"RGLN.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}