{"id":"https://openalex.org/W4403780792","doi":"https://doi.org/10.1145/3664647.3681232","title":"OpenAVE: Moving towards Open Set Audio-Visual Event Localization","display_name":"OpenAVE: Moving towards Open Set Audio-Visual Event Localization","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403780792","doi":"https://doi.org/10.1145/3664647.3681232"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5080542450","display_name":"Jiale Yu","orcid":"https://orcid.org/0000-0002-9335-0151"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiale Yu","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054557971","display_name":"Baopeng Zhang","orcid":"https://orcid.org/0000-0003-2592-2354"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baopeng Zhang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052217154","display_name":"Teng Zhu","orcid":"https://orcid.org/0000-0002-1754-4878"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu Teng","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jianping Fan","orcid":"https://orcid.org/0000-0003-2693-1172"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Fan","raw_affiliation_strings":["AI Lab at Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Lab at Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080542450"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.7087,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70040829,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"7503","last_page":"7512"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.9904999732971191,"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/computer-science","display_name":"Computer science","score":0.7414579391479492},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6408079862594604},{"id":"https://openalex.org/keywords/audio-visual","display_name":"Audio visual","score":0.6299960613250732},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6196373701095581},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4653388261795044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4000760018825531},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.2652711272239685},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09617200493812561}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7414579391479492},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6408079862594604},{"id":"https://openalex.org/C3017588708","wikidata":"https://www.wikidata.org/wiki/Q758901","display_name":"Audio visual","level":2,"score":0.6299960613250732},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6196373701095581},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4653388261795044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4000760018825531},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.2652711272239685},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09617200493812561},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2593116425","https://openalex.org/W2895752198","https://openalex.org/W2904509905","https://openalex.org/W2931433835","https://openalex.org/W2963149653","https://openalex.org/W2964109005","https://openalex.org/W2973218493","https://openalex.org/W2990113535","https://openalex.org/W2992671401","https://openalex.org/W2997909293","https://openalex.org/W3009436130","https://openalex.org/W3034658206","https://openalex.org/W3035081753","https://openalex.org/W3093287838","https://openalex.org/W3095099350","https://openalex.org/W3106653142","https://openalex.org/W3134961575","https://openalex.org/W3175514052","https://openalex.org/W3176232375","https://openalex.org/W3176709420","https://openalex.org/W3177266689","https://openalex.org/W3188049289","https://openalex.org/W3209983272","https://openalex.org/W4214751560","https://openalex.org/W4312309917","https://openalex.org/W4312317576","https://openalex.org/W4386065620","https://openalex.org/W4386066621","https://openalex.org/W4386075807","https://openalex.org/W4386076365","https://openalex.org/W4386076464","https://openalex.org/W4387968342","https://openalex.org/W4387969017","https://openalex.org/W4387969238","https://openalex.org/W4387969485","https://openalex.org/W6922057760"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Audio-Visual":[0,54,164],"Event":[1,55,165],"(AVE)":[2],"Localization":[3,56],"aims":[4],"to":[5,36,46,83,146],"identify":[6],"and":[7,15,34,58,62,94,130],"classify":[8],"video":[9],"segments":[10],"that":[11,19,156],"are":[12],"both":[13],"audible":[14],"visible,":[16],"a":[17,31,60,139],"field":[18],"has":[20],"seen":[21],"substantial":[22],"progress":[23],"in":[24,40],"recent":[25],"years.":[26],"Existing":[27],"methods":[28],"operate":[29],"under":[30],"closed-set":[32],"assumption":[33],"struggle":[35],"recognize":[37],"unknown":[38,128],"events":[39,129],"open-world":[41],"scenarios.":[42],"To":[43,72,124],"better":[44],"adapt":[45],"real-life":[47],"applications,":[48],"we":[49],"introduce":[50],"the":[51,73,80,98,148,163,168],"Open":[52],"Set":[53],"task":[57],"propose":[59],"novel":[61],"effective":[63],"network":[64],"called":[65],"OpenAVE":[66,157],"based":[67],"on":[68,162],"evidential":[69,91,106],"deep":[70,90,105],"learning.":[71,137],"best":[74],"of":[75,101,150,170],"our":[76,171],"knowledge,":[77],"this":[78,85],"is":[79,144],"first":[81],"effort":[82],"address":[84],"challenge.":[86],"Our":[87],"approach":[88],"encompasses":[89],"AVE":[92,107],"classification":[93,108,111],"event-relevant":[95,133,151],"prediction,":[96],"targeting":[97],"nuanced":[99],"demands":[100],"open-set":[102],"environments.":[103],"The":[104],"manages":[109],"event":[110],"uncertainty":[112],"by":[113],"extracting":[114],"class":[115],"evidence":[116],"from":[117],"segment-specific":[118],"representations":[119],"enriched":[120],"with":[121],"multi-scale":[122],"context.":[123],"effectively":[125],"distinguish":[126],"between":[127],"background":[131],"segments,":[132],"prediction":[134,142],"utilizes":[135],"positive-unlabeled":[136],"Futhermore,":[138],"learnable":[140],"Gaussian-prior":[141],"branch":[143],"adopted":[145],"enhance":[147],"performance":[149],"prediction.":[152],"Experimental":[153],"results":[154],"demonstrate":[155],"significantly":[158],"outperforms":[159],"state-of-the-art":[160],"models":[161],"dataset,":[166],"confirming":[167],"effectiveness":[169],"proposed":[172],"method.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}
