{"updated":"2025-01-19T07:45:51.783707+00:00","links":{},"created":"2025-01-19T01:45:34.049289+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241034","sets":["1164:2592:11452:11789"]},"path":["11789"],"owner":"44499","recid":"241034","title":["畳み込みとアテンションに基づく画像セグメンテーションおよび復元"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-19"},"_buckets":{"deposit":"2459fb30-050d-443e-9570-dbc30dc0caf3"},"_deposit":{"id":"241034","pid":{"type":"depid","value":"241034","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"畳み込みとアテンションに基づく画像セグメンテーションおよび復元","author_link":["662919","662918"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"畳み込みとアテンションに基づく画像セグメンテーションおよび復元"},{"subitem_title":"Semantic Segmentation and Image Restoration Based on Convolution and Attention","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"招待講演","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-11-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"島根大学学術研究院理工学系"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Shimane University, Interdisciplinary Faculty of Science and Engineering","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/241034/files/IPSJ-AL24200018.pdf","label":"IPSJ-AL24200018.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AL24200018.pdf","filesize":[{"value":"837.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"9"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"52142835-bd82-426d-bf92-4c1fdca797d8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"白井, 匡人"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masato, Shirai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN1009593X","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8566","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年画像認識のタスクにおいて局所的な特徴抽出に優れた畳み込みや大域的な関係性を捉えることが可能なアテンション機構を用いたモデルがよく利用されている．特にセグメンテーションや画像復元といったタスクにおいては畳み込みとアテンション機構を組み合わせたモデルが多数提案されている．一方で畳み込みとアテンション機構のどちらかのみを用いた手法が高い精度を示すこともあり，それぞれの有効性は明らかになっていない．本論文では，セグメンテーションおよび画像復元のタスクにおける畳み込みとアテンション機構の働きを調査することで精度の向上に役立つモデル構造を明らかにする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, convolutional layer models, which excel in local feature extraction, and attention models, which can capture global relationships, have been used frequently in image recognition tasks. While models combining convolutional layers and attention have been proposed for tasks such as image segmentation and image restoration, the effectiveness of each mechanism has not been clarified because there is little difference in accuracy between the convolutional layer model and a single model. In this paper, we investigate the function of convolutional layers and attention in segmentation and image restoration tasks to identify model structures that can improve accuracy.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"3","bibliographic_titles":[{"bibliographic_title":"研究報告アルゴリズム（AL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"18","bibliographicVolumeNumber":"2024-AL-200"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":241034}