{"id":"https://openalex.org/W3086755561","doi":"https://doi.org/10.23919/fusion45008.2020.9190246","title":"Early vs Late Fusion in Multimodal Convolutional Neural Networks","display_name":"Early vs Late Fusion in Multimodal Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3086755561","doi":"https://doi.org/10.23919/fusion45008.2020.9190246","mag":"3086755561"},"language":"en","primary_location":{"id":"doi:10.23919/fusion45008.2020.9190246","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","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/A5013897647","display_name":"Konrad Gadzicki","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Konrad Gadzicki","raw_affiliation_strings":["Cognitive Neuroinformatics University of Bremen, Bremen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cognitive Neuroinformatics University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091307342","display_name":"Razieh Khamsehashari","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Razieh Khamsehashari","raw_affiliation_strings":["Cognitive Neuroinformatics University of Bremen, Bremen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cognitive Neuroinformatics University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066935727","display_name":"Christoph Zetzsche","orcid":null},"institutions":[{"id":"https://openalex.org/I180437899","display_name":"University of Bremen","ror":"https://ror.org/04ers2y35","country_code":"DE","type":"education","lineage":["https://openalex.org/I180437899"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Zetzsche","raw_affiliation_strings":["Cognitive Neuroinformatics University of Bremen, Bremen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cognitive Neuroinformatics University of Bremen, Bremen, Germany","institution_ids":["https://openalex.org/I180437899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013897647"],"corresponding_institution_ids":["https://openalex.org/I180437899"],"apc_list":null,"apc_paid":null,"fwci":6.7705,"has_fulltext":false,"cited_by_count":205,"citation_normalized_percentile":{"value":0.97655836,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.995199978351593,"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":0.8428336381912231},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7544416189193726},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.703997015953064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6665294170379639},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6482658386230469},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6169824600219727},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5299856662750244},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5208138227462769},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4969673454761505},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44742634892463684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3456132709980011}],"concepts":[{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.8428336381912231},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544416189193726},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.703997015953064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6665294170379639},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6482658386230469},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6169824600219727},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5299856662750244},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5208138227462769},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4969673454761505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44742634892463684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3456132709980011},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/fusion45008.2020.9190246","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1572666543","https://openalex.org/W1586730761","https://openalex.org/W1983364832","https://openalex.org/W1985867508","https://openalex.org/W1989085630","https://openalex.org/W2016053056","https://openalex.org/W2077395415","https://openalex.org/W2078046413","https://openalex.org/W2094611250","https://openalex.org/W2108598243","https://openalex.org/W2132020233","https://openalex.org/W2144836231","https://openalex.org/W2156303437","https://openalex.org/W2402144811","https://openalex.org/W2415469094","https://openalex.org/W2477205648","https://openalex.org/W2554408731","https://openalex.org/W2560474170","https://openalex.org/W2613570903","https://openalex.org/W2619383789","https://openalex.org/W2755876276","https://openalex.org/W2761860076","https://openalex.org/W2953384591","https://openalex.org/W2962744348","https://openalex.org/W2963177663","https://openalex.org/W2963188557","https://openalex.org/W2963524571","https://openalex.org/W2964134613","https://openalex.org/W2964214371","https://openalex.org/W2964216549","https://openalex.org/W2971659033","https://openalex.org/W3023340129","https://openalex.org/W3083482336","https://openalex.org/W3103858256","https://openalex.org/W6682864246","https://openalex.org/W6713134421","https://openalex.org/W6716212831","https://openalex.org/W6730018194","https://openalex.org/W6745995786"],"related_works":["https://openalex.org/W2185469136","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039","https://openalex.org/W2726747157","https://openalex.org/W2004817612","https://openalex.org/W2010131506","https://openalex.org/W2145797872"],"abstract_inverted_index":{"Combining":[0],"machine":[1],"learning":[2],"in":[3,35],"neural":[4],"networks":[5],"with":[6],"multimodal":[7],"fusion":[8,20,73,92,119,127],"strategies":[9,21],"offers":[10],"an":[11,79,125],"interesting":[12],"potential":[13],"for":[14,22],"classification":[15],"tasks":[16],"but":[17],"the":[18,36,72,96,107],"optimum":[19],"many":[23],"applications":[24],"have":[25],"yet":[26],"to":[27,83],"be":[28],"determined.":[29],"Here":[30],"we":[31,60],"address":[32],"this":[33],"issue":[34],"context":[37],"of":[38,44,74,103,124],"human":[39],"activity":[40],"recognition,":[41],"making":[42,101],"use":[43,102],"a":[45,53,88,113,121],"state-of-the-art":[46],"convolutional":[47],"network":[48],"architecture":[49],"(Inception":[50],"I3D)":[51],"and":[52,66,86,120],"huge":[54],"dataset":[55],"(NTU":[56],"RGB+D).":[57],"As":[58],"modalities":[59,76],"consider":[61],"RGB":[62],"video,":[63],"optical":[64],"flow,":[65],"skeleton":[67],"data.":[68],"We":[69],"determine":[70],"whether":[71,87],"different":[75,108],"can":[77,94],"provide":[78],"advantage":[80,123],"as":[81],"compared":[82],"uni-modal":[84],"approaches,":[85],"more":[89],"complex":[90],"early":[91,126],"strategy":[93,99],"outperform":[95],"simpler":[97],"late-fusion":[98],"by":[100,117],"statistical":[104],"correlations":[105],"between":[106],"modalities.":[109],"Our":[110],"results":[111],"show":[112],"clear":[114],"performance":[115],"improvement":[116],"multi-modal":[118],"substantial":[122],"strategy.":[128]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":70},{"year":2024,"cited_by_count":52},{"year":2023,"cited_by_count":35},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
