{"id":"https://openalex.org/W4416251730","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227195","title":"Towards Open-World Video Segmentation via Iterative Automatic Prompting","display_name":"Towards Open-World Video Segmentation via Iterative Automatic Prompting","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251730","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227195"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5071266785","display_name":"Liangzhi Li","orcid":"https://orcid.org/0000-0002-8879-5957"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangzhi Li","raw_affiliation_strings":["Qufu Normal University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,China","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671307","display_name":"Xinru Li","orcid":"https://orcid.org/0000-0001-6859-2311"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinru Li","raw_affiliation_strings":["Qufu Normal University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,China","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101618975","display_name":"Zhouqiang Jiang","orcid":"https://orcid.org/0009-0002-1304-4179"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhouqiang Jiang","raw_affiliation_strings":["Osaka University,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022865501","display_name":"Xingfu Cheng","orcid":"https://orcid.org/0009-0008-1937-3727"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingfu Cheng","raw_affiliation_strings":["Qufu Normal University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,China","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037709945","display_name":"Zhaofeng Niu","orcid":"https://orcid.org/0000-0001-9894-4584"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaofeng Niu","raw_affiliation_strings":["Qufu Normal University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,China","institution_ids":["https://openalex.org/I202126657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100412545","display_name":"Bowen Wang","orcid":"https://orcid.org/0000-0002-2911-5595"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Bowen Wang","raw_affiliation_strings":["Osaka University,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013763732","display_name":"Guangshun Li","orcid":"https://orcid.org/0000-0001-6147-0637"},"institutions":[{"id":"https://openalex.org/I202126657","display_name":"Qufu Normal University","ror":"https://ror.org/03ceheh96","country_code":"CN","type":"education","lineage":["https://openalex.org/I202126657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangshun Li","raw_affiliation_strings":["Qufu Normal University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qufu Normal University,China","institution_ids":["https://openalex.org/I202126657"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":0.39598922,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9075999855995178,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9075999855995178,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.04540000110864639,"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":0.010700000450015068,"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/segmentation","display_name":"Segmentation","score":0.7549999952316284},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5392000079154968},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4887999892234802},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4821999967098236},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.3598000109195709},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.35499998927116394},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3361999988555908},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.3305000066757202}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8127999901771545},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7549999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7192999720573425},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6396999955177307},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4887999892234802},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4821999967098236},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3190999925136566},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3156000077724457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":27,"referenced_works":["https://openalex.org/W1560354729","https://openalex.org/W2009874829","https://openalex.org/W2011953904","https://openalex.org/W2470139095","https://openalex.org/W2564998703","https://openalex.org/W2798441772","https://openalex.org/W2963253279","https://openalex.org/W2963732700","https://openalex.org/W2964218467","https://openalex.org/W3158120491","https://openalex.org/W3194038966","https://openalex.org/W4312396403","https://openalex.org/W4312510454","https://openalex.org/W4312547276","https://openalex.org/W4313171270","https://openalex.org/W4313189668","https://openalex.org/W4319300361","https://openalex.org/W4386076112","https://openalex.org/W4390872599","https://openalex.org/W4390873795","https://openalex.org/W4390874575","https://openalex.org/W4390874670","https://openalex.org/W4392435044","https://openalex.org/W4400677507","https://openalex.org/W4401375179","https://openalex.org/W4404713242","https://openalex.org/W4413917265"],"related_works":[],"abstract_inverted_index":{"Large-scale":[0],"pre-trained":[1],"visual":[2,40],"foundation":[3,41,142,163],"models,":[4,143],"such":[5],"as":[6],"the":[7,44,116,138,147,158],"Segment":[8],"Anything":[9],"Model":[10],"2":[11],"(SAM2),":[12],"demonstrate":[13],"strong":[14],"performance":[15],"in":[16,102,122],"video":[17,58,103,154],"segmentation.":[18,59,104],"However,":[19],"they":[20],"require":[21],"multiple":[22,127],"iterations":[23],"of":[24,140,149],"sophisticated":[25],"manual":[26],"prompts":[27],"to":[28,67,70,94,165],"achieve":[29],"satisfactory":[30],"results.":[31],"This":[32],"paper":[33],"introduces":[34],"a":[35,79,91],"method":[36],"that":[37,108],"integrates":[38],"existing":[39,111],"models":[42,151,164],"without":[43],"need":[45],"for":[46,56,152,160],"additional":[47],"training,":[48],"named":[49],"IAP-SAM2,":[50],"which":[51],"enables":[52],"iterative":[53,81],"automatic":[54,62],"prompting":[55,63],"open-world":[57,112],"An":[60],"innovative":[61],"mechanism":[64,93],"is":[65],"designed":[66],"allow":[68],"SAM2":[69],"segment":[71],"target":[72],"object":[73,96,130],"on":[74,86,115],"videos.":[75],"Additionally,":[76],"we":[77],"propose":[78],"multi-round":[80],"prompt":[82],"generation":[83],"strategy":[84],"based":[85],"feature":[87],"similarity,":[88],"along":[89],"with":[90,126],"voting":[92],"refine":[95],"segmentation":[97,113,135,155],"and":[98,118,129,156],"address":[99,166],"occlusion":[100],"issues":[101],"Experimental":[105],"results":[106],"show":[107],"IAP-SAM2":[109],"outperforms":[110],"approaches":[114],"DAVIS":[117],"LVOS":[119],"datasets,":[120],"particularly":[121],"handling":[123],"complex":[124],"videos":[125],"targets":[128],"occlusions,":[131],"while":[132],"maintaining":[133],"robust":[134],"performance.":[136],"In":[137],"era":[139],"emerging":[141],"this":[144],"work":[145],"unlocks":[146],"potential":[148],"these":[150],"automated":[153],"expands":[157],"pathway":[159],"leveraging":[161],"combined":[162],"real-world":[167],"challenges.":[168]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}