{"id":"https://openalex.org/W4379928393","doi":"https://doi.org/10.1145/3587819.3590989","title":"Multimodal Cascaded Framework with Metric Learning Robust to Missing Modalities for Person Classification","display_name":"Multimodal Cascaded Framework with Metric Learning Robust to Missing Modalities for Person Classification","publication_year":2023,"publication_date":"2023-06-07","ids":{"openalex":"https://openalex.org/W4379928393","doi":"https://doi.org/10.1145/3587819.3590989"},"language":"en","primary_location":{"id":"doi:10.1145/3587819.3590989","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587819.3590989","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 14th ACM Multimedia Systems Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3587819.3590989","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007962043","display_name":"Vijay John","orcid":"https://orcid.org/0000-0002-9553-0906"},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]},{"id":"https://openalex.org/I4210110163","display_name":"Nippon Soken (Japan)","ror":"https://ror.org/01yk36x23","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210110163"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Vijay John","raw_affiliation_strings":["RIKEN, Seika-cho, Japan"],"affiliations":[{"raw_affiliation_string":"RIKEN, Seika-cho, Japan","institution_ids":["https://openalex.org/I4210110163","https://openalex.org/I4210110652"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027960360","display_name":"Yasutomo Kawanishi","orcid":"https://orcid.org/0000-0002-3799-4550"},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]},{"id":"https://openalex.org/I4210110163","display_name":"Nippon Soken (Japan)","ror":"https://ror.org/01yk36x23","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210110163"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasutomo Kawanishi","raw_affiliation_strings":["RIKEN, Seika-cho, Japan"],"affiliations":[{"raw_affiliation_string":"RIKEN, Seika-cho, Japan","institution_ids":["https://openalex.org/I4210110163","https://openalex.org/I4210110652"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007962043"],"corresponding_institution_ids":["https://openalex.org/I4210110163","https://openalex.org/I4210110652"],"apc_list":null,"apc_paid":null,"fwci":0.4919,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64070888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"257","last_page":"265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9968000054359436,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9968000054359436,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9775999784469604,"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/modalities","display_name":"Modalities","score":0.7768728137016296},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6849135160446167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6659632325172424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.620825469493866},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44516345858573914},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.43935805559158325},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35563328862190247},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06714701652526855}],"concepts":[{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.7768728137016296},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6849135160446167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6659632325172424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.620825469493866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44516345858573914},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.43935805559158325},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35563328862190247},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06714701652526855},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587819.3590989","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587819.3590989","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 14th ACM Multimedia Systems Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3587819.3590989","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3587819.3590989","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 14th ACM Multimedia Systems Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2017034896","https://openalex.org/W2140477882","https://openalex.org/W2523267570","https://openalex.org/W2770960269","https://openalex.org/W2809410879","https://openalex.org/W2936134684","https://openalex.org/W2962931510","https://openalex.org/W3008785076","https://openalex.org/W3012721484","https://openalex.org/W3097616280","https://openalex.org/W3097741049","https://openalex.org/W3114214226","https://openalex.org/W3175825020","https://openalex.org/W4313164293"],"related_works":["https://openalex.org/W73545470","https://openalex.org/W4224266612","https://openalex.org/W2383394264","https://openalex.org/W4320153225","https://openalex.org/W4293261942","https://openalex.org/W3125968744","https://openalex.org/W2167701463","https://openalex.org/W2110287964","https://openalex.org/W4307407935","https://openalex.org/W649759291"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2,61],"the":[3,52,62,69,74,79,87,105,109,112,121,132,143,160,163,168,187,191,209,213,234,238],"missing":[4,19,63,122,137,222],"modality":[5,18,64,123,138,156],"problem":[6,65],"in":[7,54,78],"multimodal":[8,14,27,71,76,91],"person":[9,24,106,216],"classification,":[10],"where":[11,37],"an":[12,201,225],"incomplete":[13,75],"input":[15],"with":[16,30],"one":[17],"is":[20,35,157,184],"classified":[21],"into":[22,98],"predefined":[23],"classes.":[25],"A":[26,196],"cascaded":[28,59,113,161],"framework":[29,60,114,183,211],"three":[31],"deep":[32],"learning":[33,110],"models":[34],"proposed,":[36],"model":[38,53],"parameters,":[39],"outputs,":[40],"and":[41,89,96,126,190,200,230,240],"latent":[42,84,101,118,127,134,146,164,170,176],"space":[43,81,102,171,177],"learnt":[44,175],"at":[45],"a":[46,55,83,99,155,173,179],"given":[47],"step":[48],"are":[49,93,148,204],"transferred":[50],"to":[51,103,130,150],"subsequent":[56],"step.":[57],"The":[58,136,181],"by,":[66],"firstly,":[67],"generating":[68],"complete":[70],"data":[72,77,147],"from":[73],"feature":[80],"via":[82],"space.":[85],"Subsequently,":[86],"generated":[88],"original":[90],"features":[92],"effectively":[94],"merged":[95],"embedded":[97],"final":[100,169],"estimate":[104],"label.":[107],"During":[108],"phase,":[111],"uses":[115],"two":[116],"novel":[117],"loss":[119,129,140,166],"functions,":[120],"joint":[124,139],"loss,":[125],"prior":[128,165],"learn":[131],"different":[133],"spaces.":[135],"ensures":[141],"that":[142,208],"similar":[144],"class":[145],"close":[149],"each":[151],"other,":[152],"even":[153,218],"if":[154],"missing.":[158],"In":[159],"framework,":[162],"learns":[167],"using":[172],"previously":[174],"as":[178],"prior.":[180],"proposed":[182,210],"validated":[185],"on":[186,237],"audio-visible":[188],"RAVDESS":[189,239],"visible-thermal":[192],"Speaking":[193,241],"Faces":[194,242],"datasets.":[195,243],"detailed":[197],"comparative":[198],"analysis":[199,203],"ablation":[202],"performed,":[205],"which":[206],"demonstrate":[207],"enhances":[212],"robustness":[214],"of":[215,221,227],"classification":[217],"under":[219],"conditions":[220],"modalities,":[223],"reporting":[224],"average":[226],"21.75%":[228],"increase":[229,232],"25.73%":[231],"over":[233],"baseline":[235],"algorithms":[236]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}
