{"id":"https://openalex.org/W7160933673","doi":"https://doi.org/10.48550/arxiv.2605.08723","title":"EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing","display_name":"EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160933673","doi":"https://doi.org/10.48550/arxiv.2605.08723"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08723","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.08723","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135933553","display_name":"Huilai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Huilai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135911905","display_name":"Xiaomeng Di","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di, Xiaomeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009499465","display_name":"Ying Xing","orcid":"https://orcid.org/0000-0003-2807-1911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135989875","display_name":"Yonghao Dang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Yonghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135983857","display_name":"Yiming Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135924604","display_name":"Jianqin Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Jianqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.32899999618530273,"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":0.32899999618530273,"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":0.14429999887943268,"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":0.13130000233650208,"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/parsing","display_name":"Parsing","score":0.7598999738693237},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5425999760627747},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.532800018787384},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4975000023841858},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3330000042915344},{"id":"https://openalex.org/keywords/video-retrieval","display_name":"Video retrieval","score":0.3165000081062317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8564000129699707},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7598999738693237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6697999835014343},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.532800018787384},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.460099995136261},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C2983174267","wikidata":"https://www.wikidata.org/wiki/Q3775098","display_name":"Video retrieval","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.28780001401901245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26600000262260437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08723","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.08723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08723","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7663758993148804}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Weakly":[0],"supervised":[1],"Audio-Visual":[2],"Video":[3],"Parsing":[4],"(AVVP)":[5],"aims":[6],"to":[7,49,126,135,146,162],"recognize":[8],"and":[9,14,56,86,94,114,165,187],"temporally":[10],"localize":[11],"audio,":[12],"visual,":[13],"audio-visual":[15,51],"events":[16],"in":[17,91,152,184],"videos":[18],"using":[19],"only":[20],"coarse-grained":[21],"labels.":[22],"Faced":[23],"with":[24,154],"the":[25,111,115,132,170],"challenging":[26],"task":[27],"settings,":[28],"existing":[29],"research":[30],"advances":[31],"along":[32],"two":[33],"main":[34],"paths:":[35],"pre-training":[36,128],"pseudo-label":[37,112,133,186],"generators":[38],"for":[39,109,173],"fine-grained":[40],"cross-modal":[41,166],"semantic":[42],"guidance,":[43],"or":[44],"refining":[45],"AVVP":[46,116,188],"model":[47],"architectures":[48],"enhance":[50],"fusion.":[52,156],"However,":[53],"since":[54],"audio":[55],"visual":[57],"signals":[58],"are":[59],"typically":[60],"unaligned,":[61],"achieving":[62],"accurate":[63],"video":[64,96],"parsing":[65,97],"fundamentally":[66],"relies":[67],"on":[68],"precise":[69],"perception":[70],"of":[71,149],"uni-modal":[72,88,107,138,150,164],"events.":[73,139,174],"Yet":[74],"these":[75],"multi-modal":[76,81,155],"focused":[77],"strategies":[78],"excessively":[79],"emphasize":[80],"fusion":[82],"while":[83],"inadequately":[84],"guiding":[85],"preserving":[87],"semantics,":[89],"resulting":[90],"noisy":[92],"pseudo-labels":[93],"sub-optimal":[95],"performance.":[98,189],"This":[99],"paper":[100],"proposes":[101],"a":[102,121,143],"novel":[103],"framework":[104],"that":[105,178],"enhances":[106],"representations":[108],"both":[110,163,185],"generator":[113,134],"model.":[117],"Specifically,":[118],"we":[119],"introduce":[120],"similarity-based":[122],"label":[123],"migration":[124],"approach":[125],"annotate":[127],"data,":[129],"thereby":[130],"enabling":[131],"better":[136],"understand":[137],"We":[140],"also":[141],"employ":[142],"soft-constrained":[144],"manner":[145],"refine":[147],"modeling":[148],"features":[151],"parallel":[153],"These":[157],"designs":[158],"enable":[159],"coordinated":[160],"attention":[161],"representations,":[167],"thus":[168],"boosting":[169],"localization":[171],"performance":[172],"Extensive":[175],"experiments":[176],"show":[177],"our":[179],"method":[180],"outperforms":[181],"state-of-the-art":[182],"methods":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}