WEKO3
アイテム
Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations
http://hdl.handle.net/10228/0002000992
http://hdl.handle.net/10228/00020009925b8bd422-affe-4516-abd5-be8119f18b7e
| 名前 / ファイル | ライセンス | アクション |
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10435816.pdf (6.1 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||||
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| 公開日 | 2024-11-01 | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
| 資源タイプ | journal article | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations | |||||||||||||||
| 言語 | en | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
| 著者 |
Nakanishi, Keiichi
× Nakanishi, Keiichi
× Katafuchi, Ryoya
× 徳永, 旭将
WEKO
25036
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | Creating pixel-level ground-truth (GT) masks is quite costly for deep learning-based image segmentation. Specialists in areas such as anomaly detection and medical diagnostics face difficulties in producing many GT masks due to limited resources. To reduce this burden, we propose a cost-effective image segmentation framework with point annotations (CoSPA) that performs image segmentation with only point annotations. Point annotations refer to the partial and sparse labeled coordinates in an image. The key idea is to ensure consistency between the predictions for the same coordinates from the two different networks of CoSPA. This new consistency enables the CoSPA to improve segmentation performance and extend to semi-supervised learning. For the MVTec AD dataset, we verified the cost-effectiveness of CoSPA through an anomaly detection task. We demonstrated that the point annotating costs were reduced by 80% compared to creating GT masks. Subsequently, the CoSPA realized by 87% of the mean Intersection over Union (mIoU) achieved using the fully supervised method, DeepLabV3+. Moreover, the mIoU of CoSPA using only 30% of all point annotations defeated that of the unsupervised method, PaDiM. This study offers a new direction for economic anomaly localization. | |||||||||||||||
| 言語 | en | |||||||||||||||
| 備考 | ||||||||||||||||
| 内容記述タイプ | Other | |||||||||||||||
| 内容記述 | 16th IIAI International Congress on Advanced Applied Informatics, IIAI AAI 2024, July 6-12, 2024, Takamatsu, Japan | |||||||||||||||
| 言語 | en | |||||||||||||||
| 書誌情報 |
en : 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) 発行日 2024-10-15 |
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| 出版社 | ||||||||||||||||
| 出版者 | IEEE | |||||||||||||||
| 言語 | en | |||||||||||||||
| DOI | ||||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.1109/IIAI-AAI63651.2024.00019 | |||||||||||||||
| ISBN | ||||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||||
| 関連識別子 | 979-8-3503-7790-3 | |||||||||||||||
| 著作権関連情報 | ||||||||||||||||
| 権利情報 | Copyright (c) 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Deep Learning | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Image Segmentation | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Image Anomaly Detection | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Visual inspection AI | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | Semi-Supervised Learning | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Point Annotation | |||||||||||||||
| 会議記述 | ||||||||||||||||
| 会議名 | IIAI International Congress on Advanced Applied Informatics | |||||||||||||||
| 回次 | 16 | |||||||||||||||
| 開始年 | 2024 | |||||||||||||||
| 開始月 | 07 | |||||||||||||||
| 開始日 | 06 | |||||||||||||||
| 終了年 | 2024 | |||||||||||||||
| 終了月 | 07 | |||||||||||||||
| 終了日 | 12 | |||||||||||||||
| 開催地 | Takamatsu | |||||||||||||||
| 開催国 | JPN | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| 査読の有無 | ||||||||||||||||
| 値 | yes | |||||||||||||||
| 研究者情報 | ||||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000804_ja.html | |||||||||||||||
| 論文ID（連携） | ||||||||||||||||
| 値 | 10435816 | |||||||||||||||
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| 値 | 12356 | |||||||||||||||
