{"id":"https://openalex.org/W3033754661","doi":"https://doi.org/10.1145/3372278.3390684","title":"Image Synthesis from Locally Related Texts","display_name":"Image Synthesis from Locally Related Texts","publication_year":2020,"publication_date":"2020-06-02","ids":{"openalex":"https://openalex.org/W3033754661","doi":"https://doi.org/10.1145/3372278.3390684","mag":"3033754661"},"language":"en","primary_location":{"id":"doi:10.1145/3372278.3390684","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372278.3390684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 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/A5046827942","display_name":"Tianrui Niu","orcid":"https://orcid.org/0000-0001-7743-3822"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianrui Niu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013283012","display_name":"Fangxiang Feng","orcid":"https://orcid.org/0000-0002-4798-4233"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangxiang Feng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101682544","display_name":"Lingxuan Li","orcid":"https://orcid.org/0000-0002-6839-0100"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingxuan Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351304","display_name":"Xiaojie Wang","orcid":"https://orcid.org/0000-0003-0314-8951"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046827942"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.1724,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.80965584,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"145","last_page":"153"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9969000220298767,"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":0.988099992275238,"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":0.757933497428894},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6221652030944824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5891767740249634},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.5579690337181091},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5084801912307739},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.48557040095329285},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48499223589897156},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47180119156837463},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.45999276638031006},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.45706382393836975},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4378964900970459},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43472328782081604},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.4152146577835083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3729018568992615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757933497428894},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6221652030944824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5891767740249634},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.5579690337181091},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5084801912307739},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.48557040095329285},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48499223589897156},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47180119156837463},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.45999276638031006},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.45706382393836975},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4378964900970459},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43472328782081604},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.4152146577835083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3729018568992615},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372278.3390684","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372278.3390684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5439072564","display_name":null,"funder_award_id":"61906018","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2131774270","https://openalex.org/W2793546384","https://openalex.org/W2892259963","https://openalex.org/W2962845008","https://openalex.org/W2963184176","https://openalex.org/W2963373786","https://openalex.org/W2963966654","https://openalex.org/W2963981733","https://openalex.org/W2964024144","https://openalex.org/W2964216930","https://openalex.org/W2965289598","https://openalex.org/W2965833116","https://openalex.org/W3099849198","https://openalex.org/W4245551996"],"related_works":["https://openalex.org/W4210416330","https://openalex.org/W3088136942","https://openalex.org/W2949362007","https://openalex.org/W2775506363","https://openalex.org/W4290852288","https://openalex.org/W4388893791","https://openalex.org/W4283207562","https://openalex.org/W2963177403","https://openalex.org/W2330246314","https://openalex.org/W2949522393"],"abstract_inverted_index":{"Text-to-image":[0],"synthesis":[1],"refers":[2],"to":[3,26,86,120],"generating":[4,14],"photo-realistic":[5],"images":[6,15,88],"from":[7],"text":[8,24],"descriptions.":[9],"Recent":[10],"works":[11],"focus":[12],"on":[13,90,179],"with":[16,156],"complex":[17,78],"scenes":[18],"and":[19,45,53,108,136,141,165,190],"multiple":[20],"objects.":[21],"However,":[22],"the":[23,30,36,43,103,173,185,188,191],"inputs":[25,175],"these":[27,82],"models":[28],"are":[29,55,111],"only":[31],"captions":[32],"that":[33],"always":[34],"describe":[35],"most":[37],"apparent":[38],"object":[39],"or":[40,72,99],"feature":[41],"of":[42,60,95,102,187],"image":[44,70,97,134],"detailed":[46],"information":[47],"(e.g.":[48],"visual":[49],"attributes)":[50],"for":[51],"regions":[52,98],"objects":[54,100],"often":[56],"missing.":[57],"Quantitative":[58],"evaluation":[59,127],"generation":[61],"performances":[62],"is":[63,131],"still":[64],"an":[65],"unsolved":[66],"problem,":[67],"where":[68],"traditional":[69],"classification-":[71],"retrieval-based":[73],"metrics":[74],"fail":[75],"at":[76],"evaluating":[77],"images.":[79],"To":[80],"address":[81],"problems,":[83],"we":[84],"propose":[85],"generate":[87],"conditioned":[89],"locally-related":[91,114],"texts,":[92,115],"i.e.,":[93],"descriptions":[94],"local":[96],"instead":[101],"whole":[104],"image.":[105],"Specifically,":[106],"questions":[107,149],"answers":[109],"(QAs)":[110],"chosen":[112],"as":[113,124],"which":[116],"makes":[117],"it":[118],"possible":[119],"use":[121],"VQA":[122,146,167,182],"accuracy":[123],"a":[125,145],"new":[126,174],"metric.":[128,194],"The":[129],"intuition":[130],"simple:":[132],"higher":[133],"quality":[135],"image-text":[137],"consistency":[138],"(both":[139],"globally":[140],"locally)":[142],"can":[143],"help":[144,171],"model":[147,155,189],"answer":[148],"more":[150],"correctly.":[151],"We":[152],"purposed":[153],"VQA-GAN":[154],"three":[157],"key":[158],"modules:":[159],"hierarchical":[160],"QA":[161],"encoder,":[162],"QA-conditional":[163],"GAN":[164],"external":[166],"loss.":[168],"These":[169],"modules":[170],"leverage":[172],"effectively.":[176],"Thorough":[177],"experiments":[178],"two":[180],"public":[181],"datasets":[183],"demonstrate":[184],"effectiveness":[186],"newly":[192],"proposed":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}