{"id":"https://openalex.org/W4405785112","doi":"https://doi.org/10.1109/iros58592.2024.10802859","title":"Few-shot Transparent Instance Segmentation for Bin Picking","display_name":"Few-shot Transparent Instance Segmentation for Bin Picking","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4405785112","doi":"https://doi.org/10.1109/iros58592.2024.10802859"},"language":"en","primary_location":{"id":"doi:10.1109/iros58592.2024.10802859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024613828","display_name":"Anoop Cherian","orcid":"https://orcid.org/0000-0002-5566-0351"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anoop Cherian","raw_affiliation_strings":["Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062291876","display_name":"Siddarth Jain","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddarth Jain","raw_affiliation_strings":["Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008369672","display_name":"Tim K. Marks","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tim K. Marks","raw_affiliation_strings":["Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs,Cambridge,MA,USA,02139","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5009","last_page":"5016"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.989300012588501,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.989300012588501,"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"}},{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9886999726295471,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9835000038146973,"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/computer-science","display_name":"Computer science","score":0.6435830593109131},{"id":"https://openalex.org/keywords/bin","display_name":"Bin","score":0.6227862238883972},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6127698421478271},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5897853374481201},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5381990075111389},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5254106521606445},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47744375467300415},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1723816692829132},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.16969230771064758}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6435830593109131},{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.6227862238883972},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6127698421478271},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5897853374481201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5381990075111389},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5254106521606445},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47744375467300415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1723816692829132},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.16969230771064758},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros58592.2024.10802859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2175023734","https://openalex.org/W2794739174","https://openalex.org/W2962826264","https://openalex.org/W2963150697","https://openalex.org/W2963797616","https://openalex.org/W2992308087","https://openalex.org/W2993182889","https://openalex.org/W3003686015","https://openalex.org/W3015330711","https://openalex.org/W3034233533","https://openalex.org/W3035679997","https://openalex.org/W3039368862","https://openalex.org/W3089874271","https://openalex.org/W3109733326","https://openalex.org/W3171015922","https://openalex.org/W3171847116","https://openalex.org/W3189898414","https://openalex.org/W3202305750","https://openalex.org/W4200113845","https://openalex.org/W4200492212","https://openalex.org/W4206755693","https://openalex.org/W4214703333","https://openalex.org/W4295046749","https://openalex.org/W4307823382","https://openalex.org/W4312344659","https://openalex.org/W4312580994","https://openalex.org/W4383172033","https://openalex.org/W4387105830","https://openalex.org/W4387790087","https://openalex.org/W4390874575","https://openalex.org/W4393146739","https://openalex.org/W6635206423","https://openalex.org/W6792701392","https://openalex.org/W6801427515","https://openalex.org/W6802704039"],"related_works":["https://openalex.org/W2107701374","https://openalex.org/W1616588898","https://openalex.org/W4395000504","https://openalex.org/W2074502265","https://openalex.org/W4249504934","https://openalex.org/W4214877189","https://openalex.org/W2183416055","https://openalex.org/W2568867011","https://openalex.org/W2773965352","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0,122],"this":[1,32],"paper,":[2],"we":[3,50,155],"consider":[4,51],"the":[5,38,52,109,112,127,138,150,177,185],"problem":[6],"of":[7,11,117,152,164,167,182,184],"segmenting":[8],"multiple":[9],"instances":[10,96],"a":[12,23,55,61,82,105,159],"transparent":[13,69,94],"object":[14,95,120],"from":[15],"RGB":[16],"or":[17],"gray":[18],"scale":[19],"camera":[20],"images":[21,91],"in":[22,54,77,176,202],"robotic":[24],"bin":[25],"picking":[26],"setting.":[27],"Prior":[28],"methods":[29],"for":[30,47,68,87,107],"solving":[31],"task":[33,53],"are":[34,79,130,140],"usually":[35],"built":[36],"on":[37,72,158],"Mask-RCNN":[39,197],"framework,":[40],"but":[41],"they":[42],"require":[43],"large":[44],"annotated":[45,101,208],"datasets":[46],"fine-tuning.":[48],"Instead,":[49],"few-shot":[56,161],"setting":[57],"and":[58,63,103,115,137,170,180],"present":[59,156],"TrInSeg,":[60],"data-efficient":[62],"robust":[64],"instance":[65,146],"segmentation":[66],"method":[67,106],"objects":[70],"based":[71],"Mask-RCNN.":[73],"Our":[74,187],"key":[75],"innovations":[76],"TrInSeg":[78,191],"twofold:":[80],"i)":[81],"novel":[83],"method,":[84,126,154],"dubbed":[85],"TransMixup,":[86],"producing":[88],"new":[89,124,160],"training":[90,209],"using":[92],"synthetic":[93],"created":[97],"by":[98,132,198],"spatially":[99],"transforming":[100],"examples;":[102],"ii)":[104],"scoring":[108,125],"consistency":[110],"between":[111],"predicted":[113],"segments":[114],"rotations":[116],"an":[118,133],"ideal":[119],"template.":[121],"our":[123,153],"spatial":[128],"transformations":[129],"produced":[131],"auxiliary":[134],"neural":[135],"network,":[136],"scores":[139],"then":[141],"used":[142],"to":[143],"filter":[144],"inconsistent":[145],"predictions.":[147],"To":[148],"demonstrate":[149],"effectiveness":[151],"experiments":[157],"dataset":[162],"consisting":[163],"seven":[165],"categories":[166],"non-opaque":[168],"(transparent":[169],"translucent)":[171],"objects,":[172],"each":[173],"category":[174],"varying":[175],"size,":[178],"shape,":[179],"degree":[181],"transparency":[183],"objects.":[186],"results":[188],"show":[189],"that":[190],"achieves":[192],"state-of-the-art":[193],"performance,":[194],"improving":[195],"fine-tuned":[196],"more":[199],"than":[200],"14%":[201],"mIoU,":[203],"while":[204],"requiring":[205],"very":[206],"few":[207],"samples.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
