{"id":"https://openalex.org/W3204044682","doi":"https://doi.org/10.5220/0010911000003124","title":"Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification","display_name":"Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3204044682","doi":"https://doi.org/10.5220/0010911000003124","mag":"3204044682"},"language":"en","primary_location":{"id":"doi:10.5220/0010911000003124","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0010911000003124","pdf_url":null,"source":{"id":"https://openalex.org/S4363608825","display_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.5220/0010911000003124","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107234012","display_name":"Muktabh Srivastava","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Muktabh Srivastava","raw_affiliation_strings":["ParallelDots Inc, Gurugram, India, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ParallelDots Inc, Gurugram, India, --- Select a Country ---","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5107234012"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0925,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.29915041,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"659","last_page":"663"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9954000115394592,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7597472667694092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7318564057350159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6611460447311401},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6112428307533264},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5851828455924988},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5244603157043457},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5026490688323975},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4394936263561249},{"id":"https://openalex.org/keywords/problem-statement","display_name":"Problem statement","score":0.4238545894622803},{"id":"https://openalex.org/keywords/statement","display_name":"Statement (logic)","score":0.4159683883190155},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41414201259613037},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38323479890823364},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16642716526985168}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7597472667694092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7318564057350159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6611460447311401},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6112428307533264},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5851828455924988},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5244603157043457},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5026490688323975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4394936263561249},{"id":"https://openalex.org/C202532154","wikidata":"https://www.wikidata.org/wiki/Q4374193","display_name":"Problem statement","level":2,"score":0.4238545894622803},{"id":"https://openalex.org/C2777026412","wikidata":"https://www.wikidata.org/wiki/Q2684591","display_name":"Statement (logic)","level":2,"score":0.4159683883190155},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41414201259613037},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38323479890823364},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16642716526985168},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5220/0010911000003124","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0010911000003124","pdf_url":null,"source":{"id":"https://openalex.org/S4363608825","display_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5220/0010911000003124","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0010911000003124","pdf_url":null,"source":{"id":"https://openalex.org/S4363608825","display_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2127095579","https://openalex.org/W2141584146","https://openalex.org/W2151103935","https://openalex.org/W2549139847","https://openalex.org/W2897842311","https://openalex.org/W2911663386","https://openalex.org/W2963703197","https://openalex.org/W3005680577","https://openalex.org/W3018378048","https://openalex.org/W3035003500","https://openalex.org/W3035160371","https://openalex.org/W3036444709","https://openalex.org/W3171007011"],"related_works":["https://openalex.org/W4230021867","https://openalex.org/W2963732072","https://openalex.org/W121796019","https://openalex.org/W4379033598","https://openalex.org/W1995698356","https://openalex.org/W3128689118","https://openalex.org/W2030474123","https://openalex.org/W3194732531","https://openalex.org/W3027176591","https://openalex.org/W4255583600"],"abstract_inverted_index":{"Retail":[0],"product":[1,13,77,120],"Image":[2],"classification":[3,9,46],"problems":[4],"are":[5],"often":[6],"few":[7],"shot":[8],"problems,":[10],"given":[11],"retail":[12,76,119],"classes":[14],"cannot":[15],"have":[16,34],"the":[17,58,114],"type":[18],"of":[19,112],"variations":[20],"across":[21],"images":[22],"like":[23],"a":[24,65,82],"cat":[25],"or":[26,28],"dog":[27],"tree":[29],"could":[30],"have.":[31],"Previous":[32],"works":[33],"shown":[35],"different":[36],"methods":[37],"to":[38,43,56,104],"finetune":[39],"Convolutional":[40,66],"Neural":[41,67],"Networks":[42],"achieve":[44],"better":[45],"accuracy":[47,109],"on":[48,86],"such":[49],"datasets.":[50],"In":[51],"this":[52],"work,":[53],"we":[54,63],"try":[55],"address":[57],"problem":[59],"statement":[60],":":[61],"Can":[62],"pretrain":[64],"Network":[68],"backbone":[69,117],"which":[70],"yields":[71],"good":[72,91],"enough":[73],"representations":[74,88,106],"for":[75,118],"images,":[78],"so":[79],"that":[80,107],"training":[81,103],"simple":[83],"logistic":[84],"regression":[85],"these":[87],"gives":[89],"us":[90],"classifiers":[92],"?":[93],"We":[94],"use":[95],"contrastive":[96],"learning":[97],"and":[98],"pseudolabel":[99],"based":[100],"noisy":[101],"student":[102],"learn":[105],"get":[108],"in":[110],"order":[111],"finetuning":[113],"entire":[115],"Convnet":[116],"image":[121],"classification.":[122]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}