{"id":"https://openalex.org/W4403791559","doi":"https://doi.org/10.1145/3664647.3681491","title":"MDDR: Multi-modal Dual-Attention aggregation for Depression Recognition","display_name":"MDDR: Multi-modal Dual-Attention aggregation for Depression Recognition","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791559","doi":"https://doi.org/10.1145/3664647.3681491"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681491","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681491","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/A5107790191","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0001-8536-2948"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["National University of Defense Technology, ChangSha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069681054","display_name":"En Zhu","orcid":"https://orcid.org/0000-0003-2305-7555"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"En Zhu","raw_affiliation_strings":["National University of Defense Technology, ChangSha, China"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100359210","display_name":"Juan Chen","orcid":"https://orcid.org/0009-0008-0675-9591"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Chen","raw_affiliation_strings":["University of Chinese Academy of Sciences, BeiJing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, BeiJing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013240205","display_name":"YunPeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"YunPeng Li","raw_affiliation_strings":["Nanjing Industria Tenebris Information Technology Co., Ltd, NanJing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Industria Tenebris Information Technology Co., Ltd, NanJing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107790191"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":3.1719,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.92175535,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"321","last_page":"329"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.996399998664856,"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":0.996399998664856,"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/T12488","display_name":"Mental Health via Writing","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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":0.970300018787384,"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/modal","display_name":"Modal","score":0.8093125820159912},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.7537178993225098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5887089967727661},{"id":"https://openalex.org/keywords/depression","display_name":"Depression (economics)","score":0.5515822172164917},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35862743854522705},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.15967470407485962},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.04807737469673157}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.8093125820159912},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7537178993225098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5887089967727661},{"id":"https://openalex.org/C2776867660","wikidata":"https://www.wikidata.org/wiki/Q1814941","display_name":"Depression (economics)","level":2,"score":0.5515822172164917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35862743854522705},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.15967470407485962},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.04807737469673157},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681491","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681491","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":35,"referenced_works":["https://openalex.org/W250071017","https://openalex.org/W1493037916","https://openalex.org/W2021396128","https://openalex.org/W2023687307","https://openalex.org/W2169670431","https://openalex.org/W2185887892","https://openalex.org/W2194775991","https://openalex.org/W2570412145","https://openalex.org/W2745497104","https://openalex.org/W2752782242","https://openalex.org/W2760537051","https://openalex.org/W2800833042","https://openalex.org/W2807126412","https://openalex.org/W2889847952","https://openalex.org/W2937336808","https://openalex.org/W2957148965","https://openalex.org/W3003850414","https://openalex.org/W3004404036","https://openalex.org/W3015892324","https://openalex.org/W3016138882","https://openalex.org/W3094644839","https://openalex.org/W3122081138","https://openalex.org/W3155791660","https://openalex.org/W3169801598","https://openalex.org/W3201200719","https://openalex.org/W4200536610","https://openalex.org/W4213368610","https://openalex.org/W4287693875","https://openalex.org/W4376226279","https://openalex.org/W4385815492","https://openalex.org/W4385881926","https://openalex.org/W4386453735","https://openalex.org/W4386813249","https://openalex.org/W6661649765","https://openalex.org/W6817659160"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Automated":[0],"diagnosis":[1],"of":[2,24,28,109,119,132],"depression":[3,169,182],"is":[4,102,142],"crucial":[5],"for":[6,68,112,136,154,168],"early":[7],"detection":[8],"and":[9,78,87,91,95,180],"timely":[10],"intervention.":[11],"Previous":[12],"research":[13],"has":[14],"largely":[15],"concentrated":[16],"on":[17,145,177],"visual":[18],"information,":[19],"often":[20],"neglecting":[21],"the":[22,46,84,92,107,126,130,133,150,178],"value":[23],"leveraging":[25],"a":[26,116,161],"variety":[27],"data":[29],"types.":[30],"Although":[31],"some":[32],"studies":[33],"have":[34],"attempted":[35],"to":[36,104,148],"employ":[37],"multiple":[38],"modalities,":[39],"they":[40],"typically":[41],"fall":[42],"short":[43],"in":[44],"investigating":[45],"complex":[47],"dynamics":[48],"between":[49],"features":[50,77,111,121,135,153],"from":[51],"various":[52],"modalities":[53],"over":[54,122],"time.":[55,123],"To":[56],"address":[57],"this":[58],"challenge,":[59],"we":[60,159],"present":[61],"an":[62],"innovative":[63],"Multi-modal":[64],"Dual-Attention":[65],"aggregation":[66,82,141],"architecture":[67],"Depression":[69],"Recognition":[70],"(MDDR).":[71],"This":[72],"framework":[73],"leverages":[74],"multi-modal":[75,110],"pre-trained":[76],"introduces":[79],"two":[80],"attention":[81],"mechanisms:":[83],"Feature":[85],"Alignment":[86],"Aggregation":[88,96],"(FAA)":[89],"module":[90,101,128],"Sequence":[93],"Encoding":[94],"(SEA)":[97],"module.":[98],"The":[99],"FAA":[100],"designed":[103,167],"dynamically":[105],"evaluate":[106],"relevance":[108],"each":[113,137],"instance,":[114],"facilitating":[115],"dynamic":[117],"integration":[118],"these":[120],"Following":[124],"this,":[125],"SEA":[127],"determines":[129],"importance":[131],"amalgamated":[134],"frame,":[138],"ensuring":[139],"that":[140],"conducted":[143],"based":[144],"their":[146],"significance,":[147],"extract":[149],"most":[151],"relevant":[152],"accurately":[155],"diagnosing":[156],"depression.":[157],"Moreover,":[158],"propose":[160],"unique":[162],"loss":[163],"calculation":[164],"method":[165],"specifically":[166],"assessment,":[170],"named":[171],"DR":[172],"Loss.":[173],"Our":[174],"approach,":[175],"evaluated":[176],"AVEC2013":[179],"AVEC2014":[181],"audiovisual":[183],"datasets,":[184],"achieves":[185],"unparalleled":[186],"performance.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5}],"updated_date":"2026-03-01T08:55:55.761014","created_date":"2025-10-10T00:00:00"}
