{"id":"https://openalex.org/W4308235961","doi":"https://doi.org/10.1109/icip46576.2022.9897563","title":"Context Relation Fusion Model for Visual Question Answering","display_name":"Context Relation Fusion Model for Visual Question Answering","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308235961","doi":"https://doi.org/10.1109/icip46576.2022.9897563"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5100392974","display_name":"Haotian Zhang","orcid":"https://orcid.org/0009-0008-0293-337X"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haotian Zhang","raw_affiliation_strings":["Inner Mongolia University,Computer Science Department","Computer Science Department, Inner Mongolia University"],"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Computer Science Department","institution_ids":["https://openalex.org/I2722730"]},{"raw_affiliation_string":"Computer Science Department, Inner Mongolia University","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100413330","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0001-5639-3999"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wu","raw_affiliation_strings":["Inner Mongolia University,Computer Science Department","Computer Science Department, Inner Mongolia University"],"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Computer Science Department","institution_ids":["https://openalex.org/I2722730"]},{"raw_affiliation_string":"Computer Science Department, Inner Mongolia University","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100392974"],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":null,"apc_paid":null,"fwci":0.5393,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75310138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2112","last_page":"2116"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9986000061035156,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9887999892234802,"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/prior-probability","display_name":"Prior probability","score":0.8253160119056702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7546638250350952},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6999426484107971},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6587794423103333},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.646269679069519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5853245258331299},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5713852643966675},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5593241453170776},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.48091384768486023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.389443576335907},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.15990236401557922},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.08382540941238403},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.07766538858413696}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8253160119056702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7546638250350952},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6999426484107971},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6587794423103333},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.646269679069519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853245258331299},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5713852643966675},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5593241453170776},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.48091384768486023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.389443576335907},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.15990236401557922},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08382540941238403},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.07766538858413696},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1933349210","https://openalex.org/W2560730294","https://openalex.org/W2745461083","https://openalex.org/W2946299408","https://openalex.org/W2955124656","https://openalex.org/W2962884579","https://openalex.org/W2963609017","https://openalex.org/W2963644680","https://openalex.org/W2963800628","https://openalex.org/W2966683369","https://openalex.org/W2970019270","https://openalex.org/W2983256121","https://openalex.org/W2997072136","https://openalex.org/W3015246548","https://openalex.org/W3034287395","https://openalex.org/W3035243232","https://openalex.org/W3035517717","https://openalex.org/W3160728318","https://openalex.org/W3170243808","https://openalex.org/W3177934633","https://openalex.org/W4289421997","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6754733129","https://openalex.org/W6764756247"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3204607391","https://openalex.org/W2964413124","https://openalex.org/W4388937922","https://openalex.org/W3113264705","https://openalex.org/W68335373","https://openalex.org/W4385890381","https://openalex.org/W2402231715"],"abstract_inverted_index":{"Traditional":[0],"VQA":[1,79],"models":[2],"tend":[3],"to":[4,12,40,81,110],"rely":[5],"on":[6,136],"language":[7,27,31,35,43,48,51,85,89,93],"priors":[8,28,52,86],"as":[9],"a":[10,65],"shortcut":[11],"answer":[13],"questions":[14],"and":[15,33,45,91,104,116],"neglect":[16],"visual":[17],"information.":[18],"To":[19],"solve":[20],"this":[21,61],"problem,":[22],"the":[23,42,47,78,98,122,137],"latest":[24],"approaches":[25],"divide":[26],"into":[29,87],"\"good\"":[30,88],"context":[32,44,90],"\"bad\"":[34,92],"bias":[36],"through":[37,121],"global":[38,58],"features":[39,76],"benefit":[41],"suppress":[46],"bias.":[49,94],"However,":[50],"cannot":[53],"be":[54],"meticulously":[55],"divided":[56],"by":[57],"features.":[59],"In":[60],"paper,":[62],"we":[63,96],"propose":[64],"novel":[66],"Context":[67],"Relation":[68,100,106],"Fusion":[69,101,107,125],"Model":[70,102,108,126],"(CRFM),":[71],"which":[72],"produces":[73],"comprehensive":[74],"contextual":[75,114],"forcing":[77],"model":[80],"more":[82],"carefully":[83],"distinguish":[84],"Specifically,":[95],"utilize":[97],"Visual":[99],"(VRFM)":[103],"Question":[105],"(QRFM)":[109],"learn":[111],"local":[112],"critical":[113],"information":[115,119],"then":[117],"perform":[118],"enhancement":[120],"Attended":[123],"Features":[124],"(AFFM).":[127],"Experiments":[128],"show":[129],"that":[130],"our":[131],"CRFM":[132],"achieves":[133],"state-of-the-art":[134],"performance":[135],"VQA-CP":[138],"v2":[139],"dataset.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
