{"id":"https://openalex.org/W7137893956","doi":"https://doi.org/10.48550/arxiv.2603.14750","title":"Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization","display_name":"Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7137893956","doi":"https://doi.org/10.48550/arxiv.2603.14750"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14750","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14750","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.2603.14750","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006035036","display_name":"Cailing Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Cailing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000873689","display_name":"Zhangbin Li","orcid":"https://orcid.org/0000-0001-5125-8827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhangbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047303316","display_name":"Jinxing Zhou","orcid":"https://orcid.org/0000-0001-6402-7593"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jinxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129669361","display_name":"Wei Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129686898","display_name":"Jingjing Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jingjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129708203","display_name":"Yanghao Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yanghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080759198","display_name":"Zhangling Duan","orcid":"https://orcid.org/0000-0003-3246-8022"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Zhangling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129645911","display_name":"Dan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Dan","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/T10667","display_name":"Emotion and Mood Recognition","score":0.44940000772476196,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.44940000772476196,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.3409000039100647,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07649999856948853,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.7829999923706055},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6998999714851379},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.652400016784668},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6308000087738037},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.5699999928474426},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5514000058174133},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.400299996137619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043000102043152},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7829999923706055},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6998999714851379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6596999764442444},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6308000087738037},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.5699999928474426},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40049999952316284},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3693999946117401},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.27000001072883606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14750","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14750","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.2603.14750","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14750","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":0.7353748679161072}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Point-level":[0],"weakly-supervised":[1],"temporal":[2],"sentiment":[3,17,33,56,81,96,115],"localization":[4],"(P-WTSL)":[5],"aims":[6],"to":[7,54,94,113,128],"detect":[8],"sentiment-relevant":[9],"segments":[10],"in":[11,35],"untrimmed":[12],"multimodal":[13,74],"videos":[14],"using":[15],"timestamp":[16],"annotations,":[18],"which":[19,69],"greatly":[20],"reduces":[21],"the":[22,29,39,63,87,110,121,143,146,164],"costly":[23],"frame-level":[24],"labeling.":[25],"To":[26],"further":[27],"tackle":[28],"challenges":[30],"of":[31,98,148,168],"imprecise":[32],"boundaries":[34],"P-WTSL,":[36],"we":[37,119],"propose":[38,86],"Face-guided":[40,64],"Sentiment":[41,65,89,123],"Boundary":[42],"Enhancement":[43],"Network":[44],"(\\textbf{FSENet}),":[45],"a":[46],"unified":[47],"framework":[48],"that":[49],"leverages":[50],"fine-grained":[51],"facial":[52,71],"features":[53,72],"guide":[55],"localization.":[57],"Specifically,":[58],"our":[59,149,169],"approach":[60,127],"\\textit{first}":[61],"introduces":[62],"Discovery":[66],"(FSD)":[67],"module,":[68],"integrates":[70],"into":[73,133],"interaction":[75],"via":[76,105],"dual-branch":[77],"modeling":[78],"for":[79],"effective":[80],"stimuli":[82],"clues;":[83],"We":[84],"\\textit{then}":[85],"Point-aware":[88],"Semantics":[90],"Contrast":[91],"(PSSC)":[92],"strategy":[93],"discriminate":[95],"semantics":[97],"candidate":[99],"points":[100,104],"(frame-level)":[101],"near":[102],"annotation":[103,173],"contrastive":[106],"learning,":[107],"thereby":[108,162],"enhancing":[109],"model's":[111],"ability":[112,167],"recognize":[114],"boundaries.":[116],"At":[117],"\\textit{last},":[118],"design":[120],"Boundary-aware":[122],"Pseudo-label":[124],"Generation":[125],"(BSPG)":[126],"convert":[129],"sparse":[130],"point":[131],"annotations":[132],"temporally":[134],"smooth":[135],"supervisory":[136],"pseudo-labels.":[137],"Extensive":[138],"experiments":[139],"and":[140,158],"visualizations":[141],"on":[142],"benchmark":[144],"demonstrate":[145],"effectiveness":[147],"framework,":[150],"achieving":[151],"state-of-the-art":[152],"performance":[153],"under":[154],"full":[155],"supervision,":[156,161],"video-level,":[157],"point-level":[159],"weak":[160],"showcasing":[163],"strong":[165],"generalization":[166],"FSENet":[170],"across":[171],"different":[172],"settings.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}