{"id":"https://openalex.org/W4415708052","doi":"https://doi.org/10.1109/icme59968.2025.11210205","title":"Multi-Grained Alignment for Visual Grounding","display_name":"Multi-Grained Alignment for Visual Grounding","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708052","doi":"https://doi.org/10.1109/icme59968.2025.11210205"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5100641001","display_name":"Hongbing Li","orcid":"https://orcid.org/0000-0001-5783-7563"},"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":"Hongbing Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100771377","display_name":"Bo Xiao","orcid":"https://orcid.org/0000-0003-3392-3293"},"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":"Bo Xiao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035082722","display_name":"Linyi Yang","orcid":"https://orcid.org/0000-0003-0667-7349"},"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":"Linyi Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458486","display_name":"Xinran Wang","orcid":"https://orcid.org/0000-0003-1120-8248"},"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":"Xinran Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100350262","display_name":"Qi Li","orcid":"https://orcid.org/0000-0003-0679-4385"},"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":"Qi Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Artificial Intelligence,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9556999802589417,"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":0.9556999802589417,"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":0.013899999670684338,"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":0.012199999764561653,"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/limiting","display_name":"Limiting","score":0.5462999939918518},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5238999724388123},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4426000118255615},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4187000095844269},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.38019999861717224},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.37049999833106995},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.36730000376701355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398000121116638},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.586899995803833},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5462999939918518},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4187000095844269},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.38019999861717224},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C2776058522","wikidata":"https://www.wikidata.org/wiki/Q2364768","display_name":"Visual field","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.30730000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210205","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210205","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2489434015","https://openalex.org/W2745461083","https://openalex.org/W2946086442","https://openalex.org/W2962766617","https://openalex.org/W2963109634","https://openalex.org/W2986755220","https://openalex.org/W2987734933","https://openalex.org/W3035574168","https://openalex.org/W3090449556","https://openalex.org/W3096609285","https://openalex.org/W3110435696","https://openalex.org/W3138516171","https://openalex.org/W3139565899","https://openalex.org/W3159619744","https://openalex.org/W3173364567","https://openalex.org/W3184784418","https://openalex.org/W4214490042","https://openalex.org/W4312351586","https://openalex.org/W4313145013","https://openalex.org/W4386071687","https://openalex.org/W4389665280","https://openalex.org/W4392678259","https://openalex.org/W4394625548"],"related_works":[],"abstract_inverted_index":{"Visual":[0],"grounding":[1],"aims":[2],"to":[3,30,64,72],"establish":[4],"fine-grained":[5],"alignment":[6],"between":[7,35],"specific":[8],"regions":[9],"and":[10,69,94,103],"queries.":[11],"Despite":[12],"recent":[13],"success,":[14],"existing":[15],"methods":[16,115],"often":[17,53],"struggle":[18],"with":[19,48],"two":[20],"main":[21],"issues.":[22],"Firstly,":[23],"using":[24],"independently":[25],"pre-trained":[26],"uni-modal":[27],"encoders":[28],"leads":[29],"a":[31,49,80],"significant":[32],"semantic":[33,91],"gap":[34,92],"extracted":[36],"features,":[37],"hindering":[38],"the":[39,60,90],"effective":[40],"interaction":[41],"of":[42],"vision-language":[43],"contexts.":[44],"Secondly,":[45],"attention-based":[46],"approaches":[47],"global":[50],"receptive":[51],"field":[52],"overlook":[54],"local":[55],"information":[56],"within":[57,95],"images,":[58],"limiting":[59],"visual":[61,86],"comprehension":[62],"necessary":[63],"distinguish":[65],"foreground":[66],"from":[67],"background":[68],"consequently":[70],"leading":[71],"localization":[73,99],"ambiguity.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78],"propose":[79],"Multi-Grained":[81],"Alignment":[82],"(MGA)":[83],"framework":[84],"for":[85],"grounding,":[87],"which":[88],"addresses":[89],"across":[93],"modalities":[96],"while":[97],"enhancing":[98],"performance":[100],"through":[101],"region-level":[102],"patch-level":[104],"alignment.":[105],"Extensive":[106],"experimental":[107],"results":[108],"demonstrate":[109],"that":[110],"our":[111],"approach":[112],"outperforms":[113],"state-of-the-art":[114],"on":[116],"three":[117],"widely-used":[118],"benchmarks.":[119],"The":[120],"code":[121],"is":[122],"accessible":[123],"at":[124],"https://github.com/Marloweeee/MGA-ICME.":[125]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-30T00:00:00"}
