{"id":"https://openalex.org/W7164838123","doi":"https://doi.org/10.1145/3805622.3810879","title":"Sparse Implicit Connectivity Graphs with Scheduled Emotion History Sampling for Multimodal Emotion Recognition in Conversation","display_name":"Sparse Implicit Connectivity Graphs with Scheduled Emotion History Sampling for Multimodal Emotion Recognition in Conversation","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164838123","doi":"https://doi.org/10.1145/3805622.3810879"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810879","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810879","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810879","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138662313","display_name":"Feng Li","orcid":"https://orcid.org/0009-0009-5550-5202"},"institutions":[{"id":"https://openalex.org/I188935350","display_name":"Anhui University of Finance and Economics","ror":"https://ror.org/0152zzg30","country_code":"CN","type":"education","lineage":["https://openalex.org/I188935350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Li","raw_affiliation_strings":["Anhui University of Finance and Economics, Bengbu, China"],"raw_orcid":"https://orcid.org/0009-0009-5550-5202","affiliations":[{"raw_affiliation_string":"Anhui University of Finance and Economics, Bengbu, China","institution_ids":["https://openalex.org/I188935350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138628841","display_name":"Wen Luo","orcid":"https://orcid.org/0009-0008-4640-2140"},"institutions":[{"id":"https://openalex.org/I188935350","display_name":"Anhui University of Finance and Economics","ror":"https://ror.org/0152zzg30","country_code":"CN","type":"education","lineage":["https://openalex.org/I188935350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Luo","raw_affiliation_strings":["Anhui University of Finance and Economics, Bengbu, China"],"raw_orcid":"https://orcid.org/0009-0008-4640-2140","affiliations":[{"raw_affiliation_string":"Anhui University of Finance and Economics, Bengbu, China","institution_ids":["https://openalex.org/I188935350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100382568","display_name":"Bing Wang","orcid":"https://orcid.org/0000-0003-4945-7725"},"institutions":[{"id":"https://openalex.org/I188935350","display_name":"Anhui University of Finance and Economics","ror":"https://ror.org/0152zzg30","country_code":"CN","type":"education","lineage":["https://openalex.org/I188935350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Wang","raw_affiliation_strings":["Anhui University of Finance and Economics, Bengbu, China"],"raw_orcid":"https://orcid.org/0000-0003-4945-7725","affiliations":[{"raw_affiliation_string":"Anhui University of Finance and Economics, Bengbu, China","institution_ids":["https://openalex.org/I188935350"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100717753","display_name":"Yongwei Li","orcid":"https://orcid.org/0000-0001-7799-366X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongwei Li","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7799-366X","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"2352","last_page":"2360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9891999959945679,"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":0.9891999959945679,"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":0.002199999988079071,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0007999999797903001,"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/conversation","display_name":"Conversation","score":0.5852000117301941},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5654000043869019},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.5516999959945679},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4611999988555908},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.45649999380111694},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.43479999899864197},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42669999599456787},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3815000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513999938964844},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.5852000117301941},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5654000043869019},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.5516999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47909998893737793},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41749998927116394},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38679999113082886},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.36169999837875366},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3578000068664551},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810879","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810879","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810879","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810879","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4616566598415375}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1985867508","https://openalex.org/W2146334809","https://openalex.org/W2184126968","https://openalex.org/W2740550900","https://openalex.org/W2767249564","https://openalex.org/W2787581402","https://openalex.org/W2805662932","https://openalex.org/W2891359673","https://openalex.org/W2963686995","https://openalex.org/W2964051877","https://openalex.org/W2964300796","https://openalex.org/W2985882473","https://openalex.org/W3038471032","https://openalex.org/W3173396651","https://openalex.org/W3203741465","https://openalex.org/W4221154966","https://openalex.org/W4285184319","https://openalex.org/W4360930863","https://openalex.org/W4367281761","https://openalex.org/W4372268605","https://openalex.org/W4385571780","https://openalex.org/W4385573848","https://openalex.org/W4387969507","https://openalex.org/W4388874193","https://openalex.org/W4392405713","https://openalex.org/W4393146934","https://openalex.org/W4403792229","https://openalex.org/W4404782393","https://openalex.org/W4409365140","https://openalex.org/W4411113111","https://openalex.org/W4411635439","https://openalex.org/W4411691756","https://openalex.org/W4416133646","https://openalex.org/W7128424965","https://openalex.org/W7131786586"],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Emotion":[1,121],"Recognition":[2],"in":[3,14],"Conversation":[4],"(MERC)":[5],"aims":[6],"to":[7,35,41,105,143,155,184],"infer":[8],"the":[9,145,153,176],"emotion":[10,80,172],"of":[11,149,178],"each":[12],"utterance":[13],"a":[15,66,124,138],"dialogue":[16],"by":[17],"integrating":[18],"textual,":[19],"acoustic,":[20],"and":[21,40,55,70,103,132,147,196],"visual":[22],"cues.":[23],"Existing":[24],"graph-based":[25],"approaches":[26],"can":[27,58,99],"model":[28,154],"inter-utterance":[29],"interactions.":[30],"However,":[31],"they":[32,89],"often":[33],"struggle":[34],"capture":[36],"implicit":[37,140,150],"contextual":[38],"dependencies":[39],"perform":[42],"history-conditioned":[43],"reasoning.":[44],"As":[45],"conversations":[46],"become":[47],"longer,":[48],"implicitly":[49],"constructed":[50],"links":[51],"may":[52],"introduce":[53],"redundancy":[54],"noise,":[56],"which":[57],"be":[59],"further":[60],"amplified":[61],"during":[62,182],"message":[63],"passing.":[64],"Moreover,":[65],"discrepancy":[67],"between":[68],"training":[69,133,183],"inference":[71],"is":[72],"commonly":[73],"observed:":[74],"models":[75],"are":[76],"trained":[77],"with":[78,119],"ground-truth":[79],"histories":[81,180],"as":[82,194],"conditioning":[83],"signals,":[84],"while":[85,161],"at":[86,187],"test":[87],"time":[88],"must":[90],"rely":[91],"on":[92,157,190],"previously":[93],"predicted":[94,179],"histories.":[95],"This":[96],"exposure":[97],"bias":[98],"cause":[100],"distribution":[101],"shift":[102],"lead":[104],"error":[106],"accumulation.":[107],"To":[108],"mitigate":[109],"these":[110],"issues,":[111],"we":[112,136,167],"propose":[113],"SIC-SEH":[114],"(Sparse":[115],"Implicit":[116],"Connectivity":[117],"Graphs":[118],"Scheduled":[120],"History":[122],"Sampling),":[123],"robustness-oriented":[125],"framework":[126],"that":[127],"jointly":[128],"improves":[129],"graph":[130,142],"structure":[131],"strategy.":[134],"Specifically,":[135],"build":[137],"sparse":[139],"connectivity":[141],"constrain":[144],"scale":[146],"quality":[148],"links,":[151],"allowing":[152],"focus":[156],"salient":[158],"historical":[159],"cues":[160],"suppressing":[162],"noisy":[163],"propagation.":[164],"In":[165],"addition,":[166],"adopt":[168],"scheduled":[169],"sampling":[170],"over":[171],"histories,":[173],"progressively":[174],"increasing":[175],"proportion":[177],"used":[181],"enhance":[185],"stability":[186],"inference.":[188],"Experiments":[189],"benchmark":[191],"datasets":[192],"such":[193],"IEMOCAP":[195],"MELD":[197],"demonstrate":[198],"consistent":[199],"improvements.":[200]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-06-16T00:00:00"}
