{"id":"https://openalex.org/W2530144925","doi":"https://doi.org/10.1145/2983563.2983567","title":"Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking","display_name":"Multimodal and Crossmodal Representation Learning from Textual and Visual Features with Bidirectional Deep Neural Networks for Video Hyperlinking","publication_year":2016,"publication_date":"2016-10-11","ids":{"openalex":"https://openalex.org/W2530144925","doi":"https://doi.org/10.1145/2983563.2983567","mag":"2530144925"},"language":"en","primary_location":{"id":"doi:10.1145/2983563.2983567","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983563.2983567","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion","raw_type":"proceedings-article"},"type":"preprint","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/A5053127458","display_name":"Vedran Vukoti\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I28221208","display_name":"Institut National des Sciences Appliqu\u00e9es de Rennes","ror":"https://ror.org/04xaa4j22","country_code":"FR","type":"education","lineage":["https://openalex.org/I28221208"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Vedran Vukoti\u0107","raw_affiliation_strings":["INSA Rennes, Rennes, France"],"affiliations":[{"raw_affiliation_string":"INSA Rennes, Rennes, France","institution_ids":["https://openalex.org/I28221208"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073795682","display_name":"Christian Raymond","orcid":"https://orcid.org/0000-0002-0963-9367"},"institutions":[{"id":"https://openalex.org/I28221208","display_name":"Institut National des Sciences Appliqu\u00e9es de Rennes","ror":"https://ror.org/04xaa4j22","country_code":"FR","type":"education","lineage":["https://openalex.org/I28221208"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Christian Raymond","raw_affiliation_strings":["INSA Rennes, Rennes, France"],"affiliations":[{"raw_affiliation_string":"INSA Rennes, Rennes, France","institution_ids":["https://openalex.org/I28221208"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008190208","display_name":"Guillaume Gravier","orcid":"https://orcid.org/0000-0002-2266-5682"},"institutions":[{"id":"https://openalex.org/I56067802","display_name":"Universit\u00e9 de Rennes","ror":"https://ror.org/015m7wh34","country_code":"FR","type":"education","lineage":["https://openalex.org/I56067802"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Guillaume Gravier","raw_affiliation_strings":["CNRS, Rennes, France"],"affiliations":[{"raw_affiliation_string":"CNRS, Rennes, France","institution_ids":["https://openalex.org/I56067802","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053127458"],"corresponding_institution_ids":["https://openalex.org/I28221208"],"apc_list":null,"apc_paid":null,"fwci":3.17646529,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.95167863,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"37","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","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/T11714","display_name":"Multimodal Machine Learning Applications","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/T11439","display_name":"Video Analysis and Summarization","score":0.9959999918937683,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9945999979972839,"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/crossmodal","display_name":"Crossmodal","score":0.8922521471977234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7403474450111389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7340849041938782},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7219253182411194},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6610137820243835},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4745122790336609},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44328573346138},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34684693813323975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3307337760925293},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3262389302253723},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.16852566599845886}],"concepts":[{"id":"https://openalex.org/C60115397","wikidata":"https://www.wikidata.org/wiki/Q5188732","display_name":"Crossmodal","level":4,"score":0.8922521471977234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7403474450111389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7340849041938782},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7219253182411194},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6610137820243835},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4745122790336609},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44328573346138},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34684693813323975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3307337760925293},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3262389302253723},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.16852566599845886},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2983563.2983567","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983563.2983567","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1821938769","https://openalex.org/W1964073652","https://openalex.org/W2028502081","https://openalex.org/W2035607533","https://openalex.org/W2062118960","https://openalex.org/W2100495367","https://openalex.org/W2102765684","https://openalex.org/W2117539524","https://openalex.org/W2142988183","https://openalex.org/W2184188583","https://openalex.org/W2255887839","https://openalex.org/W2288303121","https://openalex.org/W2295512169","https://openalex.org/W2400006804","https://openalex.org/W2414857070","https://openalex.org/W2557865186","https://openalex.org/W2604272474","https://openalex.org/W2618530766","https://openalex.org/W2915649242","https://openalex.org/W2949547296","https://openalex.org/W2950133940","https://openalex.org/W2952529932","https://openalex.org/W2962835968"],"related_works":["https://openalex.org/W4240440807","https://openalex.org/W953566696","https://openalex.org/W2010220987","https://openalex.org/W2010927954","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Video":[0],"hyperlinking":[1],"represents":[2],"a":[3,173],"classical":[4,68,206],"example":[5],"of":[6,17,106,111,134,154,168,194,235,248,265],"multimodal":[7,49,65,142,169,175,215,221],"problems.":[8],"Common":[9],"approaches":[10],"to":[11,27,62,74,164,205,211],"such":[12,108],"problems":[13],"are":[14,101,124,203,209],"early":[15,46],"fusion":[16,47,143],"the":[18,28,132,152,192],"initial":[19],"modalities":[20],"and":[21,44,93,98,114,144,171,208,241,244],"crossmodal":[22,42,76,145],"translation":[23,43],"from":[24],"one":[25],"modality":[26],"other.":[29],"Recently,":[30],"deep":[31,35,56,120,159,200,225,259],"neural":[32,57,122,160,201,261],"networks,":[33,58,262],"especially":[34],"autoencoders,":[36,69],"have":[37,59],"proven":[38,61],"promising":[39],"both":[40,90],"for":[41,45,91,94,103,126,141],"via":[48],"embedding.":[50],"A":[51],"particular":[52],"architecture,":[53],"bidirectional":[54,198],"symmetrical":[55,199],"been":[60],"yield":[63,172,212],"improved":[64,214],"embeddings":[66,216,222],"over":[67],"while":[70,118],"also":[71],"being":[72],"able":[73],"perform":[75],"translation.":[77],"In":[78,177],"this":[79,178],"work,":[80,179],"we":[81,138,180],"focus":[82],"firstly":[83],"at":[84,233,267],"evaluating":[85],"good":[86],"single-modal":[87,149,189],"continuous":[88],"representations":[89],"textual":[92],"visual":[95,116,129,135,239],"information.":[96],"Word2Vec":[97],"paragraph":[99],"vectors":[100],"evaluated":[102,125],"representing":[104],"collections":[105],"words,":[107],"as":[109],"parts":[110],"automatic":[112,242,252],"transcripts":[113,243,253],"multiple":[115],"concepts,":[117],"different":[119,148,185,188],"convolutional":[121,260],"networks":[123,161,202],"directly":[127],"embedding":[128,238,251],"information,":[130],"avoiding":[131],"creation":[133],"concepts.":[136],"Secondly,":[137],"evaluate":[139],"methods":[140],"translation,":[146],"with":[147,187,227,254,257],"pairs,":[150],"in":[151,184,231],"task":[153],"video":[155],"hyperlinking.":[156],"Bidirectional":[157],"(symmetrical)":[158],"were":[162],"shown":[163,210],"successfully":[165],"tackle":[166],"downsides":[167],"autoencoders":[170,207,226],"superior":[174],"representation.":[176],"extensively":[181],"tests":[182],"them":[183],"settings,":[186],"representations,":[190],"within":[191],"context":[193],"video-hyperlinking.":[195],"Our":[196],"novel":[197],"compared":[204],"significantly":[213,218],"that":[217],"(alpha=0.0001)":[219],"outperform":[220],"obtained":[223,256],"by":[224],"an":[228,245],"absolute":[229,246],"improvement":[230,247],"precision":[232,266],"10":[234],"14.1%":[236],"when":[237,250],"concepts":[240],"4.3%":[249],"features":[255],"very":[258],"yielding":[263],"80%":[264],"10.":[268]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
