{"id":"https://openalex.org/W7139028017","doi":"https://doi.org/10.1109/globecom59602.2025.11432820","title":"Generalized User-Oriented Image Semantic Coding Empowered by Large Vision-Language Model","display_name":"Generalized User-Oriented Image Semantic Coding Empowered by Large Vision-Language Model","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7139028017","doi":"https://doi.org/10.1109/globecom59602.2025.11432820"},"language":null,"primary_location":{"id":"doi:10.1109/globecom59602.2025.11432820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","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/A5123532754","display_name":"Sin-Yu Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Sin-Yu Huang","raw_affiliation_strings":["The University of British Columbia,Department of Electrical and Computer Engineering,Vancouver,Canada"],"affiliations":[{"raw_affiliation_string":"The University of British Columbia,Department of Electrical and Computer Engineering,Vancouver,Canada","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107233232","display_name":"Vincent W.S. Wong","orcid":null},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Vincent W.S. Wong","raw_affiliation_strings":["The University of British Columbia,Department of Electrical and Computer Engineering,Vancouver,Canada"],"affiliations":[{"raw_affiliation_string":"The University of British Columbia,Department of Electrical and Computer Engineering,Vancouver,Canada","institution_ids":["https://openalex.org/I141945490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5123532754"],"corresponding_institution_ids":["https://openalex.org/I141945490"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88162441,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6346","last_page":"6351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.6855000257492065,"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"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.6855000257492065,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.04390000179409981,"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":0.02979999966919422,"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/coding","display_name":"Coding (social sciences)","score":0.58160001039505},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.571399986743927},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.567799985408783},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5020999908447266},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4778999984264374},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4357999861240387},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3716999888420105},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.36390000581741333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7946000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6200000047683716},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.58160001039505},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.571399986743927},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5020999908447266},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4778999984264374},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41780000925064087},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29319998621940613},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.26600000262260437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom59602.2025.11432820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4654660224914551,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1933349210","https://openalex.org/W2879390606","https://openalex.org/W4313350220","https://openalex.org/W4313455370","https://openalex.org/W4396240934","https://openalex.org/W4396982329","https://openalex.org/W4406858359","https://openalex.org/W4408145276","https://openalex.org/W4414165894","https://openalex.org/W4414539638","https://openalex.org/W7133193597"],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"communication":[1],"has":[2],"shown":[3],"outstanding":[4],"performance":[5],"in":[6,12,27,74,201],"preserving":[7],"the":[8,53,88,102,109,123,149,152,156,161,190,195,204],"overall":[9],"source":[10,103],"information":[11],"wireless":[13],"transmission.":[14],"For":[15],"semantically":[16],"rich":[17],"content":[18],"such":[19],"as":[20],"images,":[21],"human":[22],"users":[23],"are":[24,39,106],"often":[25],"interested":[26],"specific":[28,43],"regions":[29],"depending":[30],"on":[31,42,118,183],"their":[32],"intent.":[33,96],"Moreover,":[34],"recent":[35],"semantic":[36,72,83,199],"coding":[37,84,200],"models":[38],"mostly":[40],"trained":[41],"datasets.":[44],"However,":[45],"real-world":[46],"applications":[47],"may":[48],"involve":[49],"images":[50],"out":[51],"of":[52,55,203],"distribution":[54],"training":[56],"dataset,":[57],"which":[58,105,134,165],"makes":[59],"generalization":[60,124],"a":[61,79,91,136,170],"crucial":[62],"but":[63],"largely":[64],"unexplored":[65],"problem.":[66],"To":[67,121,147],"incorporate":[68],"user\u2019s":[69,110,157],"intent":[70],"into":[71,142],"coding,":[73],"this":[75],"paper,":[76],"we":[77,126,159],"propose":[78],"generalized":[80],"user-oriented":[81],"image":[82,104,116,130,154,198],"(UO-ISC)":[85],"framework,":[86],"where":[87],"user":[89],"provides":[90],"text":[92],"query":[93],"indicating":[94],"its":[95],"The":[97,112],"transmitter":[98],"extracts":[99],"features":[100],"from":[101],"relevant":[107],"to":[108],"query.":[111],"receiver":[113],"reconstructs":[114],"an":[115],"based":[117],"those":[119],"features.":[120],"enhance":[122],"ability,":[125],"integrate":[127],"contrastive":[128],"language":[129],"pre-training":[131],"(CLIP)":[132],"model,":[133],"is":[135,166],"pretrained":[137,171],"large":[138,172,174],"vision-language":[139],"model":[140],"(VLM),":[141],"our":[143],"proposed":[144,191],"UO-ISC":[145,192],"framework.":[146],"evaluate":[148],"relevance":[150,163],"between":[151],"reconstructed":[153],"and":[155],"query,":[158],"introduce":[160],"user-intent":[162],"loss,":[164],"computed":[167],"by":[168],"using":[169],"VLM,":[173],"language-and-vision":[175],"assistant":[176],"(LLaVA)":[177],"model.":[178],"When":[179],"performing":[180],"zero-shot":[181],"inference":[182],"unseen":[184],"objects,":[185],"simulation":[186],"results":[187],"show":[188],"that":[189],"framework":[193],"outperforms":[194],"state-of-the-art":[196],"query-aware":[197],"terms":[202],"answer":[205],"match":[206],"rate.":[207]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}
