{"id":"https://openalex.org/W4308237199","doi":"https://doi.org/10.1109/icip46576.2022.9897231","title":"Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks","display_name":"Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308237199","doi":"https://doi.org/10.1109/icip46576.2022.9897231"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897231","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5030254193","display_name":"Motonari Kambara","orcid":"https://orcid.org/0000-0002-1991-9119"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Motonari Kambara","raw_affiliation_strings":["Keio University,Japan","Keio University, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033744547","display_name":"Komei Sugiura","orcid":"https://orcid.org/0000-0002-0261-0510"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Komei Sugiura","raw_affiliation_strings":["Keio University,Japan","Keio University, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University,Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030254193"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":0.3598,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67681449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"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":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9973000288009644,"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/T10028","display_name":"Topic Modeling","score":0.9965000152587891,"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/closed-captioning","display_name":"Closed captioning","score":0.9847918152809143},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.761387825012207},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.618730902671814},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5981379747390747},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5564122796058655},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5214875936508179},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.49346959590911865},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4842264652252197},{"id":"https://openalex.org/keywords/service-robot","display_name":"Service robot","score":0.44020792841911316},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.418264776468277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41613703966140747},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.4117850065231323},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.40157780051231384},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36156266927719116},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32158607244491577},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.21408188343048096},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09798863530158997}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.9847918152809143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.761387825012207},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.618730902671814},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5981379747390747},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5564122796058655},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5214875936508179},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.49346959590911865},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4842264652252197},{"id":"https://openalex.org/C2776228582","wikidata":"https://www.wikidata.org/wiki/Q7455797","display_name":"Service robot","level":3,"score":0.44020792841911316},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.418264776468277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41613703966140747},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.4117850065231323},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.40157780051231384},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36156266927719116},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32158607244491577},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.21408188343048096},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09798863530158997},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897231","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1956340063","https://openalex.org/W2016043834","https://openalex.org/W2295848880","https://openalex.org/W2896348597","https://openalex.org/W2952132648","https://openalex.org/W2963177403","https://openalex.org/W2963916161","https://openalex.org/W2968104955","https://openalex.org/W2981851019","https://openalex.org/W2984008963","https://openalex.org/W3035237998","https://openalex.org/W3035635319","https://openalex.org/W3106534186","https://openalex.org/W3119777426","https://openalex.org/W3156855646","https://openalex.org/W3171688991","https://openalex.org/W3174257385","https://openalex.org/W3174902251","https://openalex.org/W3196153606","https://openalex.org/W4288083805","https://openalex.org/W4394659899","https://openalex.org/W6630875275","https://openalex.org/W6678262379","https://openalex.org/W6682631176","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6784761581","https://openalex.org/W6797400868","https://openalex.org/W6802919969","https://openalex.org/W6803537622","https://openalex.org/W6864544085","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W4210416330","https://openalex.org/W3088136942","https://openalex.org/W2949362007","https://openalex.org/W2775506363","https://openalex.org/W4290852288","https://openalex.org/W4388893791","https://openalex.org/W4283207562","https://openalex.org/W2963177403","https://openalex.org/W2330246314","https://openalex.org/W2949522393"],"abstract_inverted_index":{"Domestic":[0],"service":[1,21],"robots":[2,22],"that":[3],"support":[4],"daily":[5],"tasks":[6],"are":[7],"a":[8,40,43,54,96],"promising":[9],"solution":[10],"for":[11,19,59],"elderly":[12],"or":[13],"disabled":[14],"people.":[15],"It":[16],"is":[17,37],"crucial":[18],"domestic":[20],"to":[23,38,71],"explain":[24],"the":[25,48,60,67,73,80,90,93],"collision":[26],"risk":[27],"before":[28],"they":[29],"perform":[30],"actions.":[31],"In":[32],"this":[33],"paper,":[34],"our":[35],"aim":[36],"generate":[39],"caption":[41],"about":[42],"future":[44,61],"event.":[45],"We":[46,85],"propose":[47],"Relational":[49,68],"Future":[50],"Captioning":[51],"Model":[52],"(RFCM),":[53],"crossmodal":[55],"language":[56],"generation":[57],"model":[58],"captioning":[62],"task.":[63],"The":[64],"RFCM":[65,94],"has":[66],"Self-Attention":[69],"Encoder":[70],"extract":[72],"relationships":[74],"between":[75],"events":[76],"more":[77],"effectively":[78],"than":[79],"conventional":[81],"self-attention":[82],"in":[83],"transformers.":[84],"conducted":[86],"comparison":[87],"experiments,":[88],"and":[89],"results":[91],"show":[92],"outperforms":[95],"baseline":[97],"method":[98],"on":[99],"two":[100],"datasets.":[101]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
