{"id":"https://openalex.org/W3199179967","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533447","title":"Do We Really Reduce Bias for Scene Graph Generation?","display_name":"Do We Really Reduce Bias for Scene Graph Generation?","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199179967","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533447","mag":"3199179967"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5001730791","display_name":"Haiyan Gao","orcid":"https://orcid.org/0000-0002-8332-1592"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyan Gao","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069884358","display_name":"Xin Tian","orcid":"https://orcid.org/0000-0003-3984-5505"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tian","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029104204","display_name":"Yi Ji","orcid":"https://orcid.org/0000-0001-6965-4158"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Ji","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106406655","display_name":"Ying Li","orcid":"https://orcid.org/0009-0004-1669-1878"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Li","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058448163","display_name":"Chunping Liu","orcid":"https://orcid.org/0009-0008-1495-5138"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunping Liu","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning 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/T11714","display_name":"Multimodal Machine Learning 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":0.9991999864578247,"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":0.9980000257492065,"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":0.7070743441581726},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.6386215686798096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6335906982421875},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5892781019210815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5209982991218567},{"id":"https://openalex.org/keywords/scene-graph","display_name":"Scene graph","score":0.4949731230735779},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4589463472366333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37678098678588867},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2428922951221466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18235129117965698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7070743441581726},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.6386215686798096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6335906982421875},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5892781019210815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5209982991218567},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.4949731230735779},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4589463472366333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37678098678588867},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2428922951221466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18235129117965698},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"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:10.1109/ijcnn52387.2021.9533447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4700821500","display_name":null,"funder_award_id":"61972059,61773272,61602332","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327518","display_name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2025668003","https://openalex.org/W2036167211","https://openalex.org/W2159564241","https://openalex.org/W2277195237","https://openalex.org/W2479423890","https://openalex.org/W2560730294","https://openalex.org/W2579549467","https://openalex.org/W2613718673","https://openalex.org/W2629538667","https://openalex.org/W2890531016","https://openalex.org/W2950096400","https://openalex.org/W2955124656","https://openalex.org/W2962779575","https://openalex.org/W2962785943","https://openalex.org/W2963101956","https://openalex.org/W2963314968","https://openalex.org/W2963403868","https://openalex.org/W2963521239","https://openalex.org/W2963536419","https://openalex.org/W2963683498","https://openalex.org/W2963938081","https://openalex.org/W2966683369","https://openalex.org/W2970355596","https://openalex.org/W2983256121","https://openalex.org/W3034268345","https://openalex.org/W3034538190","https://openalex.org/W3035017890","https://openalex.org/W3041062282","https://openalex.org/W3108864070","https://openalex.org/W3110575265","https://openalex.org/W3115747660","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6739901393","https://openalex.org/W6739930931","https://openalex.org/W6752083267","https://openalex.org/W6754778999","https://openalex.org/W6764756247","https://openalex.org/W6771968570","https://openalex.org/W6787884402"],"related_works":["https://openalex.org/W2807251790","https://openalex.org/W2737719445","https://openalex.org/W2799406489","https://openalex.org/W2961085424","https://openalex.org/W4239098401","https://openalex.org/W2049792449","https://openalex.org/W2898210368","https://openalex.org/W2382480268","https://openalex.org/W4392007279","https://openalex.org/W4387129494"],"abstract_inverted_index":{"For":[0],"a":[1,9,21,125],"given":[2],"image,":[3],"the":[4,26,34,44,85,119,134,137,168,173],"corresponding":[5,138],"scene":[6],"graph":[7,81],"is":[8],"kind":[10],"of":[11,39,72,130],"structural":[12],"expression":[13],"which":[14,153],"benefits":[15],"to":[16,88,141,150],"high-level":[17],"tasks.":[18],"To":[19],"generate":[20],"meaningful":[22],"and":[23,47,64,79,94,111,159,177],"useful":[24],"one,":[25],"existing":[27],"models":[28,51,110],"pay":[29],"more":[30,114],"attention":[31],"on":[32,162],"reducing":[33],"bias":[35,46,49,107],"from":[36,50,108],"long-tail":[37],"distribution":[38],"dataset.":[40],"However,":[41],"they":[42],"overlook":[43],"unimodal":[45,106],"evaluation":[48,155],"themselves.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"construct":[57],"an":[58],"unbiased":[59],"solution":[60],"called":[61],"Balanced":[62],"Label":[63],"Vision":[65],"for":[66,92],"Multilabel":[67],"Classification":[68],"(BLVMC).":[69],"BLVMC":[70,170],"consists":[71],"two":[73,175],"modules,":[74],"label-vision":[75],"grounding":[76],"module":[77,104,145],"(LVGM)":[78],"no":[80],"constraint":[82],"(NGC).":[83],"Specially,":[84],"LVGM":[86],"aims":[87],"be":[89],"in":[90],"equilibrium":[91],"label":[93,101],"vision":[95],"by":[96],"introducing":[97],"visual":[98],"information":[99],"into":[100],"branch.":[102],"This":[103,144],"reduces":[105],"previous":[109,179],"makes":[112],"them":[113],"stable.":[115],"The":[116,157],"NGC":[117,135,139],"views":[118],"Scene":[120],"Graph":[121],"Generation":[122],"(SGG)":[123],"as":[124],"multilabel":[126],"classification":[127],"task":[128],"instead":[129],"multiclass":[131],"classification.":[132],"Besides,":[133],"uses":[136],"mR@K":[140],"evaluate":[142],"models.":[143,181],"allows":[146],"each":[147],"subject-object":[148],"pair":[149],"retain":[151],"multi-predicates,":[152],"relieves":[154],"bias.":[156],"quantitative":[158],"qualitative":[160],"experiments":[161],"Visual":[163],"Genome":[164],"(VG)":[165],"dataset":[166],"demonstrate":[167],"proposed":[169],"effectively":[171],"eliminates":[172],"above":[174],"biases":[176],"outperforms":[178],"state-of-the-art":[180]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
