{"id":"https://openalex.org/W3035414481","doi":"https://doi.org/10.1109/icme46284.2020.9102748","title":"Weakly-Supervised Plate And Food Region Segmentation","display_name":"Weakly-Supervised Plate And Food Region Segmentation","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3035414481","doi":"https://doi.org/10.1109/icme46284.2020.9102748","mag":"3035414481"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","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/A5051127663","display_name":"Wataru Shimoda","orcid":"https://orcid.org/0000-0001-6238-9697"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Wataru Shimoda","raw_affiliation_strings":["The University of Electro Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Electro Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054600485","display_name":"\u202aKeiji Yanai\u202c","orcid":"https://orcid.org/0000-0002-0431-183X"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keiji Yanai","raw_affiliation_strings":["The University of Electro Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Electro Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051127663"],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":1.8698,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.8755685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9968000054359436,"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/segmentation","display_name":"Segmentation","score":0.7714892625808716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7596139311790466},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7340244054794312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6653181910514832},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6356227397918701},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6102180480957031},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5835116505622864},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5410230755805969},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5157618522644043},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49241650104522705},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4733338952064514},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4616360366344452},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35357338190078735},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16767573356628418}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7714892625808716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7596139311790466},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7340244054794312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6653181910514832},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6356227397918701},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6102180480957031},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5835116505622864},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5410230755805969},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5157618522644043},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49241650104522705},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4733338952064514},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4616360366344452},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35357338190078735},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16767573356628418}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme46284.2020.9102748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1849277567","https://openalex.org/W1964856489","https://openalex.org/W2021543368","https://openalex.org/W2026845913","https://openalex.org/W2055527244","https://openalex.org/W2057352599","https://openalex.org/W2071650006","https://openalex.org/W2087006489","https://openalex.org/W2091013587","https://openalex.org/W2104019579","https://openalex.org/W2109562068","https://openalex.org/W2124351162","https://openalex.org/W2133515615","https://openalex.org/W2161185676","https://openalex.org/W2161236525","https://openalex.org/W2163969215","https://openalex.org/W2168356304","https://openalex.org/W2206370378","https://openalex.org/W2295107390","https://openalex.org/W2503388974","https://openalex.org/W2519610629","https://openalex.org/W2552414813","https://openalex.org/W2776207810","https://openalex.org/W2952793010","https://openalex.org/W2962758679","https://openalex.org/W2962851944","https://openalex.org/W2963263347","https://openalex.org/W2966316879","https://openalex.org/W2972610293","https://openalex.org/W2991083560","https://openalex.org/W6664275923","https://openalex.org/W6672251000","https://openalex.org/W6683607639","https://openalex.org/W6685133223","https://openalex.org/W6726497184"],"related_works":["https://openalex.org/W4287226069","https://openalex.org/W4313854567","https://openalex.org/W3159311316","https://openalex.org/W4287199417","https://openalex.org/W4308789000","https://openalex.org/W2127194945","https://openalex.org/W1986943276","https://openalex.org/W3034239110","https://openalex.org/W2897195263","https://openalex.org/W2489489317"],"abstract_inverted_index":{"In":[0,140],"this":[1,109],"paper,":[2,110],"we":[3,35,111,114,124,142],"propose":[4],"a":[5,41,46,65,78,176],"novel":[6],"method":[7,169],"to":[8],"infer":[9],"plate":[10,21,75,87,117,138],"regions":[11,72,85,98,118],"of":[12,26,39,58,63,92,102,105,132,146,156],"food":[13,29,42,66,71,84,97,134,172],"images":[14],"without":[15,119],"any":[16,120],"pixel-wise":[17,121],"annotation.":[18],"We":[19],"synthesize":[20],"segmentation":[22,135,168,181],"masks":[23],"using":[24,136],"difference":[25,94],"visualization":[27,61,101],"in":[28,108,170],"image":[30],"classifiers.":[31],"To":[32],"be":[33],"concrete,":[34],"use":[36],"two":[37,103],"types":[38],"classifiers:":[40],"category":[43,67,80],"classifier":[44,68,81],"and":[45,123,152,174],"food/non-food":[47,79],"classifier.":[48],"Using":[49],"the":[50,59,93,96,106,126,130,137,144,147,154,157,171],"Class":[51],"Activation":[52],"Mapping":[53],"(CAM)":[54],"which":[55],"is":[56],"one":[57],"basic":[60],"techniques":[62],"CNNs,":[64],"can":[69,82,115],"highlight":[70,83],"containing":[73],"no":[74],"regions,":[76],"while":[77],"including":[86],"regions.":[88],"By":[89],"taking":[90],"advantage":[91],"between":[95],"estimated":[99],"by":[100,150],"kinds":[104],"classifiers,":[107],"demonstrate":[112],"that":[113],"estimate":[116],"annotation,":[122],"proposed":[125,148,161],"approach":[127,149],"for":[128],"boosting":[129],"accuracy":[131,155],"weakly-supervised":[133,158,167,180],"segmentation.":[139,159],"experiments,":[141],"show":[143],"effectiveness":[145],"evaluating":[151],"comparing":[153],"The":[160],"approaches":[162],"certainly":[163],"improved":[164],"an":[165],"image-level":[166],"domain":[173],"outperformed":[175],"well-known":[177],"bounding":[178],"box-level":[179],"method.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
