{"id":"https://openalex.org/W2910308495","doi":"https://doi.org/10.1109/iros.2018.8593617","title":"Predicting Part Affordances of Objects Using Two-Stream Fully Convolutional Network with Multimodal Inputs","display_name":"Predicting Part Affordances of Objects Using Two-Stream Fully Convolutional Network with Multimodal Inputs","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2910308495","doi":"https://doi.org/10.1109/iros.2018.8593617","mag":"2910308495"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2018.8593617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2018.8593617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5026860496","display_name":"Krishneel Chaudhary","orcid":"https://orcid.org/0000-0001-6173-9800"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Krishneel Chaudhary","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101836795","display_name":"Kei Okada","orcid":"https://orcid.org/0000-0001-6606-6692"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kei Okada","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047218430","display_name":"Masayuki Inaba","orcid":"https://orcid.org/0000-0003-1273-1567"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masayuki Inaba","raw_affiliation_strings":["Graduate School of Information Science and Technology, The University of Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100340509","display_name":"Xiangyu Chen","orcid":"https://orcid.org/0000-0002-0711-0275"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyu Chen","raw_affiliation_strings":["Key Laboratory of Machine Perception, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026860496"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":1.4715,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83848862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3096","last_page":"3101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9940999746322632,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/affordance","display_name":"Affordance","score":0.9531640410423279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7972065806388855},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7191590666770935},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6943166255950928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.647892415523529},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6467827558517456},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.643810510635376},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6158563494682312},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5889493823051453},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49315351247787476},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4479469954967499},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3404652774333954},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.20505687594413757},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08637562394142151}],"concepts":[{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.9531640410423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7972065806388855},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7191590666770935},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6943166255950928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.647892415523529},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6467827558517456},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.643810510635376},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6158563494682312},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5889493823051453},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49315351247787476},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4479469954967499},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3404652774333954},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.20505687594413757},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08637562394142151},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros.2018.8593617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2018.8593617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1524405667","https://openalex.org/W1533861849","https://openalex.org/W1565402342","https://openalex.org/W1580568969","https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1933657216","https://openalex.org/W1994922096","https://openalex.org/W2011301426","https://openalex.org/W2016053056","https://openalex.org/W2075274454","https://openalex.org/W2295343990","https://openalex.org/W2355686263","https://openalex.org/W2412782625","https://openalex.org/W2528967817","https://openalex.org/W2561523096","https://openalex.org/W2741998915","https://openalex.org/W2932269910","https://openalex.org/W2962835968","https://openalex.org/W2963956866","https://openalex.org/W6631943919","https://openalex.org/W6633727509","https://openalex.org/W6634616096","https://openalex.org/W6637373629","https://openalex.org/W6705907440","https://openalex.org/W6715287400"],"related_works":["https://openalex.org/W1972718289","https://openalex.org/W2368824897","https://openalex.org/W1508050556","https://openalex.org/W1910862367","https://openalex.org/W2379365082","https://openalex.org/W2370747590","https://openalex.org/W2030109976","https://openalex.org/W2369260257","https://openalex.org/W2389120450","https://openalex.org/W55249799"],"abstract_inverted_index":{"For":[0],"a":[1],"robot":[2],"to":[3,9,20,106],"manipulate":[4],"an":[5,53],"object,":[6],"it":[7,105],"has":[8],"understand":[10],"the":[11,14,21,32,39,46,73,87,90,98,114,125,128,136,140],"functions":[12],"and":[13,42,82,100,110,131],"actions":[15],"that":[16],"can":[17],"be":[18],"subjected":[19],"object.":[22,33],"This":[23],"set":[24],"of":[25,31,45,113,124,139],"information":[26],"is":[27,70,119,133],"known":[28],"as":[29],"affordance":[30,54],"Affordances":[34],"are":[35],"generally":[36],"defined":[37],"by":[38],"geometrical":[40,111],"structures":[41],"physical":[43],"properties":[44,112],"objects.":[47,115,141],"In":[48,86],"this":[49],"paper,":[50],"we":[51],"present":[52],"detection":[55],"network":[56,77,91,126],"(ADNet)":[57],"for":[58,134],"detecting":[59],"object":[60],"affordances":[61,138],"using":[62,127],"multimodal":[63,117],"input":[64],"i.e.,":[65],"RGB-D":[66],"data.":[67],"The":[68,116],"method":[69],"based":[71],"on":[72],"state-of-the-art":[74],"fully":[75],"convolutional":[76],"with":[78],"two":[79],"encoding":[80,118],"streams":[81],"one":[83],"decoding":[84],"stream.":[85],"presented":[88],"formulation,":[89],"learns":[92],"powerful":[93],"discriminative":[94],"features":[95],"independently":[96],"from":[97],"RGB":[99],"depth":[101],"images,":[102],"which":[103],"enables":[104],"abstract":[107],"rich":[108],"photometrical":[109],"combined":[120],"at":[121],"multiple":[122],"stages":[123],"late-fusion":[129],"strategy":[130],"used":[132],"predicting":[135],"potential":[137]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}