{"id":"https://openalex.org/W7147656088","doi":"https://doi.org/10.1109/cnml68938.2026.11452293","title":"DualAgg: Dual-Branch Spatial Aggregation for Open-Vocabulary Semantic Segmentation","display_name":"DualAgg: Dual-Branch Spatial Aggregation for Open-Vocabulary Semantic Segmentation","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147656088","doi":"https://doi.org/10.1109/cnml68938.2026.11452293"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5112227813","display_name":"Lina Han","orcid":null},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lina Han","raw_affiliation_strings":["Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358"],"affiliations":[{"raw_affiliation_string":"Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109831307","display_name":"Bing Liu","orcid":"https://orcid.org/0000-0003-2029-5187"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358"],"affiliations":[{"raw_affiliation_string":"Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455297","display_name":"Tao Li","orcid":"https://orcid.org/0000-0002-3337-6202"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianping Li","raw_affiliation_strings":["Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358"],"affiliations":[{"raw_affiliation_string":"Shandong Normal University,School of Communication and Electronic Engineering,Jinan,China,250358","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086151689","display_name":"Xini Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101880","display_name":"Institute of Scientific and Technical Information","ror":"https://ror.org/0170b6c09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210101880","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoguang Wang","raw_affiliation_strings":["Shandong Institute of Scientific and Technical Information,Jinan,China,250100"],"affiliations":[{"raw_affiliation_string":"Shandong Institute of Scientific and Technical Information,Jinan,China,250100","institution_ids":["https://openalex.org/I4210101880"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112227813"],"corresponding_institution_ids":["https://openalex.org/I28006308"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.92583665,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"496","last_page":"499"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5806999802589417,"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":0.5806999802589417,"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":0.11620000004768372,"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/T10028","display_name":"Topic Modeling","score":0.09679999947547913,"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/segmentation","display_name":"Segmentation","score":0.6692000031471252},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5231999754905701},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5001000165939331},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.48510000109672546},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.4514000117778778},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.43939998745918274}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468000054359436},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6692000031471252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5952000021934509},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5231999754905701},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.4514000117778778},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.36890000104904175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3082999885082245},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2831999957561493},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.2775999903678894},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.46263596415519714}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W4386075561","https://openalex.org/W4386076397","https://openalex.org/W4402703011","https://openalex.org/W4402715795"],"related_works":[],"abstract_inverted_index":{"Open":[0],"Vocabulary":[1],"Semantic":[2],"Segmentation":[3],"(OVSS)":[4],"aims":[5],"to":[6,29,61],"segment":[7],"categories":[8],"not":[9],"fully":[10],"observed":[11],"during":[12],"training,":[13],"relying":[14],"on":[15,117],"effective":[16],"vision\u2013language":[17],"alignment":[18],"and":[19,33,48,57,86,94,112,130],"semantic":[20,103],"generalization.":[21],"Existing":[22],"methods":[23],"typically":[24],"apply":[25],"early":[26,50],"category":[27,97],"pruning":[28],"reduce":[30],"computational":[31],"cost":[32],"use":[34],"Transformer-based":[35],"encoders":[36],"for":[37,68,128],"global":[38,58,102],"context":[39],"modeling,":[40],"but":[41],"suffer":[42],"from":[43,105],"two":[44],"key":[45],"limitations:":[46],"static":[47],"coarse":[49],"rejection":[51],"may":[52],"prematurely":[53],"discard":[54],"relevant":[55],"categories,":[56],"aggregation":[59,104],"tends":[60],"dilute":[62],"fine-grained":[63],"local":[64,106],"structural":[65,107],"information":[66],"critical":[67],"accurate":[69],"segmentation.":[70],"To":[71],"address":[72],"these":[73],"issues,":[74],"we":[75],"propose":[76],"a":[77],"novel":[78],"framework":[79],"integrating":[80],"Adaptive":[81],"Early":[82],"Category":[83],"Rejection":[84],"(AECR)":[85],"Dual-Branch":[87],"Spatial":[88],"Aggregation":[89],"(DBSA).":[90],"AECR":[91],"performs":[92],"spatial-aware":[93],"uncertainty-guided":[95],"progressive":[96],"filtering,":[98],"while":[99],"DBSA":[100],"decouples":[101],"modeling":[108],"via":[109],"parallel":[110],"branches":[111],"adaptive":[113],"fusion.":[114],"Extensive":[115],"experiments":[116],"multiple":[118],"OVSS":[119],"benchmarks":[120],"demonstrate":[121],"consistent":[122],"improvements":[123],"over":[124],"strong":[125],"baselines,":[126],"particularly":[127],"rare":[129],"unseen":[131],"categories.":[132]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}