{"created":"2024-06-28T05:52:57.051382+00:00","id":2000846,"links":{},"metadata":{"_buckets":{"deposit":"56e3f219-d600-444f-84ef-fe3c006e26df"},"_deposit":{"created_by":1,"id":"2000846","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"2000846"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:02000846","sets":["8:24"]},"author_link":[],"item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicNumberOfPages":"53","bibliographicPageEnd":"32","bibliographicPageStart":"28","bibliographicVolumeNumber":"53","bibliographic_titles":[{"bibliographic_title":"成形加工","bibliographic_titleLang":"ja"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"視野を共有しない複数台カメラ間の人物追跡などにおいて, 同一人物を判定する人物再同定が重要である. 深層学習を用いた人物再同定は高い性能を達成しており, その損失関数として, 一般的にCross-Entropy LossやTriplet Lossが用いられる. 近年, 人物再同定に対するアプローチとして, その両方の損失関数を線形和で用いる手法が注目されている. しかし, 異なる性質を持つ損失関数を同時に用いる場合, 他方への影響を考慮した重み付き線形和による統合法が必要となる. そこで本研究では, 損失関数の学習効率の違いとそのバランスを考慮し, 学習中に損失の重みを自動調整する手法を提案する.\r\nPerson re-identification is an important component to realize various image recognition systems, e.g., person tracking system by utilizing multiple cameras. Person re-identification based on deep learning has achieved high performance, and cross-entropy loss or triplet loss is generally used as the loss function. In recent years, a linear summation of both loss functions has been attracting attention as an approach to person re-identification. However, when loss functions with different properties are used at the same time, a method of synthesis by weighted linear summation that takes into account the effect of the loss function on the other loss function is necessary. To overcome above problems, in this paper, a method that automatically adjusts the weights of loss functions during learning is proposed.","subitem_description_type":"Abstract"}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_source_id_10":{"attribute_name":"NCID","attribute_value_mlt":[{"subitem_source_identifier":"AN00041650"}]},"item_21_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0285-9831","subitem_source_identifier_type":"PISSN"}]},"item_21_text_63":{"attribute_name":"連携ID","attribute_value_mlt":[{"subitem_text_value":"12341"}]},"item_21_version_type_58":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"清水, 雄哉","creatorNameLang":"ja"}]},{"creatorNames":[{"creatorName":"榎田, 修一"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","filename":"neuro_49.pdf","mimetype":"application/pdf","url":{"url":"https://kyutech.repo.nii.ac.jp/record/2000846/files/neuro_49.pdf"},"version_id":"de9d758d-026d-4f38-bd9c-c4b8f49c249c"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"深層学習","subitem_subject_scheme":"Other"},{"subitem_subject":"人物再同定","subitem_subject_scheme":"Other"},{"subitem_subject":"損失関数","subitem_subject_scheme":"Other"},{"subitem_subject":"cross-entropy loss","subitem_subject_scheme":"Other"},{"subitem_subject":"triplet loss","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"深層学習による人物再同定における損失関数への重み付け手法","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習による人物再同定における損失関数への重み付け手法","subitem_title_language":"ja"}]},"item_type_id":"21","owner":"1","path":["24"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-06-28"},"publish_date":"2024-06-28","publish_status":"0","recid":"2000846","relation_version_is_last":true,"title":["深層学習による人物再同定における損失関数への重み付け手法"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2024-06-28T05:53:01.831662+00:00"}