{"id":"https://openalex.org/W4415193053","doi":"https://doi.org/10.1109/iccv51701.2025.01977","title":"Text-Guided Visual Prompt DINO for Generic Segmentation","display_name":"Text-Guided Visual Prompt DINO for Generic Segmentation","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4415193053","doi":"https://doi.org/10.1109/iccv51701.2025.01977"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.01977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.06146","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026629452","display_name":"Yuchen Guan","orcid":"https://orcid.org/0009-0007-0700-5006"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Guan","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102160084","display_name":"Chong Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chong Sun","raw_affiliation_strings":["Tencent Inc.,WeChat AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc.,WeChat AI","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086125857","display_name":"Canmiao Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Canmiao Fu","raw_affiliation_strings":["Tencent Inc.,WeChat AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc.,WeChat AI","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102030796","display_name":"Zhipeng Huang","orcid":"https://orcid.org/0000-0003-2028-0508"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Huang","raw_affiliation_strings":["Tencent Inc.,WeChat AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc.,WeChat AI","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109125095","display_name":"Chun Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun Yuan","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100369793","display_name":"Chen Li","orcid":"https://orcid.org/0000-0002-0079-3106"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Li","raw_affiliation_strings":["Tencent Inc.,WeChat AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc.,WeChat AI","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24369872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"21288","last_page":"21298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9013000130653381,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9013000130653381,"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/scalability","display_name":"Scalability","score":0.5817999839782715},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5533000230789185},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5134999752044678},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4893999993801117},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47189998626708984},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4569000005722046},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4221000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8324999809265137},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5533000230789185},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40459999442100525},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4032000005245209},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.01977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.01977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.06146","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.06146","pdf_url":"https://arxiv.org/pdf/2508.06146","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.06146","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.06146","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.06146","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.06146","pdf_url":"https://arxiv.org/pdf/2508.06146","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6732593181","display_name":null,"funder_award_id":"62106149","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,9,165],"multimodal":[3,160],"vision":[4],"models":[5],"have":[6],"highlighted":[7],"limitations":[8],"late-stage":[10],"feature":[11],"fusion":[12,49],"and":[13,55,87,162],"suboptimal":[14],"query":[15,75],"selection":[16,76],"for":[17,77,158],"hybrid":[18],"prompts":[19,54],"open-world":[20,141,166],"segmentation,":[21],"alongside":[22],"constraints":[23],"from":[24],"caption-derived":[25],"vocabularies.":[26],"To":[27],"address":[28],"these":[29],"challenges,":[30],"we":[31,45,72,97],"propose":[32],"Prompt-DINO,":[33],"a":[34,99,119,155],"text-guided":[35],"visual":[36,88],"Prompt":[37],"DINO":[38],"framework":[39],"featuring":[40],"three":[41],"key":[42],"innovations.":[43],"First,":[44],"introduce":[46],"an":[47],"early":[48],"mechanism":[50],"that":[51,135],"unifies":[52],"text/visual":[53],"backbone":[56],"features":[57],"at":[58,171],"the":[59,82,105],"initial":[60],"encoding":[61],"stage,":[62],"enabling":[63],"deeper":[64],"cross-modal":[65],"interactions":[66],"to":[67,92,129],"resolve":[68],"semantic":[69,147],"ambiguities.":[70],"Second,":[71],"design":[73],"order-aligned":[74],"DETR-based":[78],"architectures,":[79],"explicitly":[80],"optimizing":[81],"structural":[83],"alignment":[84],"between":[85],"text":[86],"queries":[89],"during":[90],"decoding":[91],"enhance":[93],"semantic-spatial":[94],"consistency.":[95],"Third,":[96],"develop":[98],"generative":[100],"data":[101,163],"engine":[102],"powered":[103],"by":[104,126],"Recognize":[106],"Anything":[107],"via":[108],"Prompting":[109],"(RAP)":[110],"model,":[111],"which":[112],"synthesizes":[113],"0.5B":[114],"diverse":[115],"training":[116],"instances":[117],"through":[118],"dual-path":[120],"cross-verification":[121],"pipeline,":[122],"reducing":[123],"label":[124],"noise":[125],"80.5%":[127],"compared":[128],"conventional":[130],"approaches.":[131],"Extensive":[132],"experiments":[133],"demonstrate":[134],"Prompt-DINO":[136],"achieves":[137],"state-of-the-art":[138],"performance":[139],"on":[140],"detection":[142,161],"benchmarks":[143],"while":[144],"significantly":[145],"expanding":[146],"coverage":[148],"beyond":[149],"fixed-vocabulary":[150],"constraints.":[151],"Our":[152],"work":[153],"establishes":[154],"new":[156],"paradigm":[157],"scalable":[159],"generation":[164],"scenarios.":[167],"Data&Code":[168],"are":[169],"available":[170],"https://github.com/WeChatCV/WeVisionOne.":[172]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-15T00:00:00"}