{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02000767","sets":["934:1022:11800:11801"]},"path":["11801"],"owner":"80578","recid":"2000767","title":["SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-01-28"},"_buckets":{"deposit":"909408ad-698c-491d-8bc2-a393730d2b78"},"_deposit":{"id":"2000767","pid":{"type":"depid","value":"2000767","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences","subitem_title_language":"ja"},{"subitem_title":"SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[研究論文] multimedia, food recognition, cooking, recipe data, datasets","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2025-01-28","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Kyoto University"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"Kyoto University"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/2000767/files/IPSJ-TOD1801003.pdf","label":"IPSJ-TOD1801003.pdf"},"date":[{"dateType":"Available","dateValue":"2027-01-28"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOD1801003.pdf","filesize":[{"value":"3.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a7b9687c-7a87-4de1-9699-9936d6894405","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yixin,Zhang"}]},{"creatorNames":[{"creatorName":"Yoko,Yamakata"}]},{"creatorNames":[{"creatorName":"Keishi,Tajima"}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yixin Zhang","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Yoko Yamakata","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Keishi Tajima","creatorNameLang":"en"}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464847","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7799","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Recognizing ingredients in cooking images is a challenging task due to the significant visual changes that ingredients undergo throughout the cooking process. As ingredients are prepared, cooked, and served, their appearances vary greatly between the beginning, intermediate, and finishing stages. Traditional object recognition methods, which assume constant object appearances, struggle with this variability and are often not good at accurately identifying ingredients at different cooking stages. To address this challenge, we propose a stage-aware recognition method specifically designed for dynamically changing ingredients in cooking images. Our approach introduces two techniques: 1. Stage-Wise Model Learning: This technique involves training separate models for each stage of the cooking process. By adapting models to specific stages, we can better capture the distinct visual characteristics of ingredients as their appearances change. 2. Stage-Aware Curriculum Learning: This technique begins training with data from the beginning cooking stages and progressively incorporates data from later stages. This gradual approach helps the model adapt to the evolving appearances of ingredients. Our experimental results, using our published dataset, demonstrate that our stage-aware methods significantly outperform models trained without stage considerations, achieving higher accuracy in ingredient recognition.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.33(2025) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Recognizing ingredients in cooking images is a challenging task due to the significant visual changes that ingredients undergo throughout the cooking process. As ingredients are prepared, cooked, and served, their appearances vary greatly between the beginning, intermediate, and finishing stages. Traditional object recognition methods, which assume constant object appearances, struggle with this variability and are often not good at accurately identifying ingredients at different cooking stages. To address this challenge, we propose a stage-aware recognition method specifically designed for dynamically changing ingredients in cooking images. Our approach introduces two techniques: 1. Stage-Wise Model Learning: This technique involves training separate models for each stage of the cooking process. By adapting models to specific stages, we can better capture the distinct visual characteristics of ingredients as their appearances change. 2. Stage-Aware Curriculum Learning: This technique begins training with data from the beginning cooking stages and progressively incorporates data from later stages. This gradual approach helps the model adapt to the evolving appearances of ingredients. Our experimental results, using our published dataset, demonstrate that our stage-aware methods significantly outperform models trained without stage considerations, achieving higher accuracy in ingredient recognition.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.33(2025) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌データベース（TOD）"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2025-01-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"18"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"id":2000767,"updated":"2025-03-12T08:09:29.715030+00:00","links":{},"created":"2025-02-20T07:22:19.987173+00:00"}