{"id":"https://openalex.org/W4392942457","doi":"https://doi.org/10.1109/bcd57833.2023.10466303","title":"Few-shot FMCG Product Recognition based on Feature Transfer Learning","display_name":"Few-shot FMCG Product Recognition based on Feature Transfer Learning","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4392942457","doi":"https://doi.org/10.1109/bcd57833.2023.10466303"},"language":"en","primary_location":{"id":"doi:10.1109/bcd57833.2023.10466303","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bcd57833.2023.10466303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD)","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/A5112629505","display_name":"Zeng Hua","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hua Zeng","raw_affiliation_strings":["National Tobacco Corporation,Sichuan Company,Chengdu,China","Sichuan Company, National Tobacco Corporation, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"National Tobacco Corporation,Sichuan Company,Chengdu,China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"Sichuan Company, National Tobacco Corporation, Chengdu, China","institution_ids":["https://openalex.org/I2800556661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029121180","display_name":"Tao Long","orcid":"https://orcid.org/0000-0001-6984-6694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao Long","raw_affiliation_strings":["Shenzhen AiMall Tech Ltd.,R&D Center,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen AiMall Tech Ltd.,R&D Center,Shenzhen,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111108121","display_name":"Xuan Li","orcid":"https://orcid.org/0009-0006-8061-2163"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan Li","raw_affiliation_strings":["Shenzhen AiMall Tech Ltd.,R&D Center,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen AiMall Tech Ltd.,R&D Center,Chengdu,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104260721","display_name":"Yonghai Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghai Liu","raw_affiliation_strings":["National Tobacco Corporation,Sichuan Company,Chengdu,China","Sichuan Company, National Tobacco Corporation, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"National Tobacco Corporation,Sichuan Company,Chengdu,China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"Sichuan Company, National Tobacco Corporation, Chengdu, China","institution_ids":["https://openalex.org/I2800556661"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112629505"],"corresponding_institution_ids":["https://openalex.org/I2800556661"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20659261,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"216","last_page":"221"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.8992000222206116,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.8992000222206116,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T13114","display_name":"Image Processing Techniques and Applications","score":0.8815000057220459,"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":0.8787000179290771,"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":0.6548970937728882},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5901440978050232},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.5558249354362488},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5485537648200989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5096690654754639},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4810062348842621},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4292569160461426},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4276985228061676},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.11981186270713806},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10596808791160583}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548970937728882},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5901440978050232},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5558249354362488},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5485537648200989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5096690654754639},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4810062348842621},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4292569160461426},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4276985228061676},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.11981186270713806},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10596808791160583},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bcd57833.2023.10466303","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bcd57833.2023.10466303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD)","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":14,"referenced_works":["https://openalex.org/W2065427498","https://openalex.org/W2136132422","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2889985731","https://openalex.org/W2952638627","https://openalex.org/W2963163009","https://openalex.org/W2963351448","https://openalex.org/W2963901018","https://openalex.org/W2969985801","https://openalex.org/W3012412627","https://openalex.org/W3099319035","https://openalex.org/W4234903585","https://openalex.org/W4307823382"],"related_works":["https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574","https://openalex.org/W2359774528","https://openalex.org/W4298312966","https://openalex.org/W2325697621"],"abstract_inverted_index":{"Fast-moving":[0],"consumer":[1],"goods":[2,37],"(FMCG),":[3],"such":[4],"as":[5,96,133],"beverages,":[6],"ice":[7,178],"cream,":[8],"and":[9,21,30,44,49,56,67,153,177],"tobacco,":[10,175],"have":[11],"to":[12,14,32,59,130,180,197,206],"respond":[13],"market":[15],"demands":[16],"in":[17,73,101,140,162,188],"a":[18,85,93,97,116,127,144,170],"timely":[19],"manner":[20],"carry":[22],"out":[23],"fast":[24],"logistics.":[25],"It":[26],"is":[27,138,160,195],"very":[28],"time-consuming":[29],"labor-intensive":[31],"guarantee":[33],"the":[34,60,82,103,134,182,189,199],"quantity":[35],"of":[36,63,172],"at":[38],"every":[39],"check":[40],"point,":[41],"including":[42],"inbound":[43],"outbound,":[45],"transportation,":[46],"warehousing,":[47],"sorting,":[48],"delivery,":[50],"where":[51],"all":[52],"items":[53],"are":[54,70,89],"checked":[55],"counted.":[57],"Thanks":[58],"rapid":[61],"development":[62],"deep":[64],"learning,":[65],"detection,":[66],"recognition":[68,104,200],"algorithms":[69],"widely":[71],"used":[72],"object":[74],"recognition.":[75,167],"However,":[76],"for":[77,91,165],"some":[78],"new":[79,157],"products":[80],"on":[81,119,149],"market,":[83],"only":[84],"few":[86],"design":[87,152],"photos":[88],"available":[90],"training,":[92],"problem":[94],"known":[95],"few-shot":[98,190],"learning":[99,122],"case,":[100],"which":[102,137],"accuracy":[105,201],"will":[106],"drop":[107],"significantly.":[108],"To":[109],"address":[110],"this":[111,113],"problem,":[112],"paper":[114],"proposes":[115],"method":[117,125,194],"based":[118],"feature":[120,141,145,158],"transfer":[121,146],"(FTL).":[123],"This":[124],"applies":[126],"pre-trained":[128],"model":[129],"extract":[131],"features":[132],"basis":[135],"representation,":[136],"transferred":[139],"space":[142],"through":[143],"model,":[147],"trained":[148],"samples":[150],"with":[151,174],"realistic":[154],"samples.":[155],"The":[156],"representation":[159],"applied":[161],"real":[163],"scenarios":[164],"product":[166],"We":[168],"build":[169],"dataset":[171],"FMCG":[173],"beverage,":[176],"cream":[179],"validate":[181],"proposed":[183,193],"method.":[184],"Experiments":[185],"demonstrated":[186],"that":[187],"setting":[191],"our":[192],"able":[196],"boost":[198],"from":[202],"66%":[203],"(without":[204],"FTL)":[205],"97%":[207],"(with":[208],"FTL).":[209]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}