{"id":"https://openalex.org/W3198585874","doi":"https://doi.org/10.1145/3460426.3463647","title":"Question-Guided Semantic Dual-Graph Visual Reasoning with Novel Answers","display_name":"Question-Guided Semantic Dual-Graph Visual Reasoning with Novel Answers","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3198585874","doi":"https://doi.org/10.1145/3460426.3463647","mag":"3198585874"},"language":"en","primary_location":{"id":"doi:10.1145/3460426.3463647","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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/A5102328626","display_name":"Xinzhe Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinzhe Zhou","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028877572","display_name":"Yadong Mu","orcid":"https://orcid.org/0000-0001-7815-3750"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yadong Mu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102328626"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0961,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39397059,"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":"411","last_page":"419"},"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":0.9940999746322632,"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":0.9940000176429749,"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/question-answering","display_name":"Question answering","score":0.7772077322006226},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.763287365436554},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6058632731437683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5536078214645386},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5355818867683411},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5178549885749817},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5084503889083862},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4708138406276703},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44831353425979614},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43605419993400574},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.42831605672836304},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3294026851654053},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2528514862060547}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7772077322006226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.763287365436554},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6058632731437683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5536078214645386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5355818867683411},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5178549885749817},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5084503889083862},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4708138406276703},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44831353425979614},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43605419993400574},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.42831605672836304},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3294026851654053},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2528514862060547},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460426.3463647","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1566018662","https://openalex.org/W2038721957","https://openalex.org/W2087739686","https://openalex.org/W2136480620","https://openalex.org/W2400717490","https://openalex.org/W2471094925","https://openalex.org/W2546696630","https://openalex.org/W2560730294","https://openalex.org/W2597425697","https://openalex.org/W2798895485","https://openalex.org/W2905524945","https://openalex.org/W2963398599","https://openalex.org/W2963486920","https://openalex.org/W2964345214","https://openalex.org/W3002557610","https://openalex.org/W3004349648"],"related_works":["https://openalex.org/W2026121273","https://openalex.org/W4206452239","https://openalex.org/W2888228969","https://openalex.org/W2776984690","https://openalex.org/W3111570652","https://openalex.org/W2135033253","https://openalex.org/W3103079199","https://openalex.org/W4287747623","https://openalex.org/W2608030593","https://openalex.org/W2798526799"],"abstract_inverted_index":{"Visual":[0],"Question":[1],"Answering":[2],"(VQA)":[3],"has":[4],"gained":[5],"increasing":[6],"attention":[7],"as":[8,26,121],"being":[9],"the":[10,33,70,84,125,197],"cross-disciplinary":[11],"research":[12],"of":[13,69,199],"computer":[14],"vision":[15],"and":[16,78,128,174,182,188],"natural":[17],"language":[18],"understanding.":[19],"However,":[20],"recent":[21],"advances":[22],"mostly":[23],"treated":[24],"it":[25],"a":[27,43,62,122,152,159],"closed-set":[28],"classification":[29],"problem,":[30],"by":[31,191],"limiting":[32],"possible":[34],"outputs":[35],"to":[36,82,119,169,178],"some":[37,107],"fixed":[38],"frequent":[39],"answers":[40,101,105,132],"available":[41],"in":[42,144,157],"training":[44],"set.":[45],"Although":[46],"effective":[47],"on":[48,61],"benchmark":[49,194],"datasets,":[50],"this":[51,148],"paradigm":[52,139],"is":[53,67,140,167],"inherently":[54],"defective---the":[55],"VQA":[56,90,155],"model":[57],"would":[58],"always":[59],"fail":[60],"question":[63,172],"whose":[64],"correct":[65],"answer":[66,71,184],"out":[68],"set,":[72],"which":[73,158],"severely":[74],"hampers":[75],"its":[76],"generalization":[77],"flexibility.":[79],"To":[80,147],"try":[81],"close":[83],"gap,":[85],"we":[86,150],"explore":[87],"an":[88],"open-set":[89],"setting,":[91],"where":[92],"models":[93],"are":[94,117],"evaluated":[95],"using":[96],"novel":[97,160],"samples":[98],"with":[99],"unseen":[100],"given":[102],"dynamic":[103,130],"candidate":[104,131],"from":[106],"candidate-generation":[108,126],"module.":[109],"For":[110],"experimental":[111],"purposes,":[112],"two":[113,193],"oracle":[114],"candidate-sampling":[115],"strategies":[116],"proposed":[118,201],"serve":[120],"proxy":[123],"for":[124,133],"module":[127,166],"generate":[129],"testing":[134],"samples.":[135],"The":[136],"conventional":[137],"classification-based":[138],"no":[141],"longer":[142],"applicable":[143],"our":[145,200],"setting.":[146],"end,":[149],"design":[151],"matching":[153],"based":[154],"model,":[156],"Single-Source":[161],"Graph":[162],"Convolutional":[163],"Network":[164],"(SSGCN)":[165],"designed":[168],"jointly":[170],"leverage":[171],"guidance":[173],"dual":[175],"semantic":[176],"answer-graphs":[177],"produce":[179],"more":[180],"discriminative":[181],"relevant":[183],"embeddings.":[185],"Extensive":[186],"experiments":[187],"ablation":[189],"studies":[190],"re-purposing":[192],"datasets":[195],"demonstrate":[196],"effectiveness":[198],"model.":[202]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}