{"id":"https://openalex.org/W4417113773","doi":"https://doi.org/10.1145/3769748.3773350","title":"Revisiting Intelligent Settlement and Nutritional Estimation of Small-bowl Dishes via Deep Learning","display_name":"Revisiting Intelligent Settlement and Nutritional Estimation of Small-bowl Dishes via Deep Learning","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W4417113773","doi":"https://doi.org/10.1145/3769748.3773350"},"language":null,"primary_location":{"id":"doi:10.1145/3769748.3773350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769748.3773350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769748.3773350","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3769748.3773350","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaonan Fan","orcid":"https://orcid.org/0009-0004-6749-3855"},"institutions":[{"id":"https://openalex.org/I88372448","display_name":"Dalian Polytechnic University","ror":"https://ror.org/00c7x4a95","country_code":"CN","type":"education","lineage":["https://openalex.org/I88372448"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaonan Fan","raw_affiliation_strings":["Dalian Polytechnic University, DaLian, China"],"raw_orcid":"https://orcid.org/0009-0004-6749-3855","affiliations":[{"raw_affiliation_string":"Dalian Polytechnic University, DaLian, China","institution_ids":["https://openalex.org/I88372448"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiyu Zou","orcid":"https://orcid.org/0009-0009-0587-582X"},"institutions":[{"id":"https://openalex.org/I88372448","display_name":"Dalian Polytechnic University","ror":"https://ror.org/00c7x4a95","country_code":"CN","type":"education","lineage":["https://openalex.org/I88372448"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyu Zou","raw_affiliation_strings":["Dalian Polytechnic University, DaLian, China"],"raw_orcid":"https://orcid.org/0009-0009-0587-582X","affiliations":[{"raw_affiliation_string":"Dalian Polytechnic University, DaLian, China","institution_ids":["https://openalex.org/I88372448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110938044","display_name":"Shumin Fan","orcid":"https://orcid.org/0009-0008-5570-3324"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shumin Fan","raw_affiliation_strings":["Dalian Maritime University, DaLian, China"],"raw_orcid":"https://orcid.org/0009-0008-5570-3324","affiliations":[{"raw_affiliation_string":"Dalian Maritime University, DaLian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356012","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-8918-1666"},"institutions":[{"id":"https://openalex.org/I88372448","display_name":"Dalian Polytechnic University","ror":"https://ror.org/00c7x4a95","country_code":"CN","type":"education","lineage":["https://openalex.org/I88372448"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Dalian Polytechnic University, DaLian, China"],"raw_orcid":"https://orcid.org/0000-0002-8918-1666","affiliations":[{"raw_affiliation_string":"Dalian Polytechnic University, DaLian, China","institution_ids":["https://openalex.org/I88372448"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45137205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.8446000218391418,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.8446000218391418,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11259","display_name":"Agriculture Sustainability and Environmental Impact","score":0.024399999529123306,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.02199999988079071,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8133000135421753},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.645799994468689},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5144000053405762},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47119998931884766},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45989999175071716},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44760000705718994},{"id":"https://openalex.org/keywords/settlement","display_name":"Settlement (finance)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40389999747276306},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.31630000472068787}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8133000135421753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.771399974822998},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.645799994468689},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.628000020980835},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47450000047683716},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44760000705718994},{"id":"https://openalex.org/C2777063073","wikidata":"https://www.wikidata.org/wiki/Q1553237","display_name":"Settlement (finance)","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38670000433921814},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2741999924182892},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26109999418258667},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3769748.3773350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769748.3773350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769748.3773350","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3769748.3773350","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769748.3773350","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3769748.3773350","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417113773.pdf","grobid_xml":"https://content.openalex.org/works/W4417113773.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1563919154","https://openalex.org/W1980193494","https://openalex.org/W1982635469","https://openalex.org/W2020617790","https://openalex.org/W2081799410","https://openalex.org/W2084955099","https://openalex.org/W2097117768","https://openalex.org/W2194775991","https://openalex.org/W2530422462","https://openalex.org/W2552501394","https://openalex.org/W2583892095","https://openalex.org/W2752782242","https://openalex.org/W2963091558","https://openalex.org/W3006086117","https://openalex.org/W3011727199","https://openalex.org/W4386914024","https://openalex.org/W4387090472","https://openalex.org/W4387968134","https://openalex.org/W4392904527","https://openalex.org/W4396594902","https://openalex.org/W4402727359","https://openalex.org/W4403549214","https://openalex.org/W4409956224"],"related_works":[],"abstract_inverted_index":{"The":[0],"rise":[1],"of":[2,31,134],"small-bowl":[3,32,46,140],"dining":[4],"necessitates":[5],"automated":[6],"billing":[7,83],"and":[8,19,78,84,92,119],"nutritional":[9,85,110],"analysis":[10],"systems":[11],"for":[12,45,81,96,126,138],"smart":[13],"cafeterias.":[14],"However,":[15],"existing":[16],"food":[17],"datasets":[18],"methods":[20],"fall":[21],"short":[22],"in":[23],"this":[24],"domain":[25],"due":[26],"to":[27,100],"the":[28,40,132],"unique":[29],"challenges":[30],"scenarios.":[33],"To":[34],"address":[35],"this,":[36],"we":[37],"introduce":[38],"SBD-101,":[39],"first":[41],"dedicated":[42],"image":[43],"dataset":[44],"dishes,":[47],"comprising":[48],"1042":[49],"real-world":[50],"images":[51],"across":[52],"44":[53],"categories,":[54],"with":[55],"precise":[56,102],"segmentation":[57,120],"annotations.":[58],"We":[59],"further":[60],"propose":[61],"Intelligent":[62],"Small":[63],"Bowl":[64],"Dish":[65],"Network":[66],"(ISBD-Net),":[67],"a":[68],"deep":[69,136],"learning":[70,137],"framework":[71],"that":[72],"leverages":[73],"multi-modal":[74,135],"fusion":[75],"(RGB,":[76],"depth,":[77],"multi-view":[79],"image)":[80],"intelligent":[82,139],"analysis.":[86],"ISBD-Net":[87],"integrates":[88],"multi-scale":[89],"feature":[90],"extraction":[91],"contour":[93],"anchor":[94],"optimization":[95],"accurate":[97],"instance":[98],"segmentation,":[99],"achieve":[101],"volume":[103],"estimation,":[104],"which":[105],"is":[106],"then":[107],"translated":[108],"into":[109],"content.":[111],"Experiments":[112],"on":[113],"SBD-101":[114],"demonstrate":[115],"ISBD-Net\u2019s":[116],"superior":[117],"detection":[118],"performance,":[121],"providing":[122],"high-quality":[123],"visual":[124],"features":[125],"downstream":[127],"tasks.":[128],"This":[129],"work":[130],"showcases":[131],"potential":[133],"dish":[141],"management.":[142]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-08T00:00:00"}
