| Item type |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2024-06-13 |
| 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 |
Layer-Wise External Attention by Well-Localized Attention Map for Efficient Deep Anomaly Detection |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Nakanishi, Keiichi
Shiroma, Ryo
Hayakawa, Tokihisa
Katafuchi, Ryoya
徳永, 旭将
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The external attention mechanism offers a promising approach to enhance image anomaly detection (Hayakawa et al., in: IMPROVE, pp. 100-–110, 2023). Nevertheless, the effectiveness of this method is contingent upon the judicious selection of an intermediate layer with external attention. In this study, we performed a comprehensive series of experiments to clarify the mechanisms through which external attention improves detection performance. We assessed the performance of the LEA-Net (Hayakawa et al., in: IMPROVE, pp. 100–110, 2023), which implements layer-wise external attention, using MVTec AD and Plant Village datasets. The detection performances of the LEA-Net were compared with that of the baseline model under different anomaly maps generated by three unsupervised approaches. In addition, we investigated the relationship between the detection performance of LEA-Net and the selection of an attention point, which means an intermediate layer where external attention is applied. The findings reveal that the synergy between the dataset and the generated anomaly map influenced the effectiveness of the LEA-Net. For poorly localized anomaly maps, the selection of the attention point becomes a pivotal factor in determining detection efficiency. At shallow attention points, a well-localized attention map successfully notably improves the detection performance. For deeper attention points, the overall intensity of the attention map is essential; this intensity can be substantially amplified by layer-wise external attention, even for a low-intensity anomaly map. Overall, the results suggest that for layer-wise external attention, the positional attributes of anomalies hold greater significance than the overall intensity or visual appearance of the anomaly map. |
|
言語 |
en |
| 書誌情報 |
en : SN Computer Science
巻 5,
号 5,
p. 592,
発行日 2024-05-28
|
| 出版社 |
|
|
出版者 |
Springer |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1007/s42979-024-02912-3 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2661-8907 |
| 著作権関連情報 |
|
|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright (c) The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Anomaly detection |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Visual inspection AI |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Deep learning |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Visual attention mechanism |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Self-attention · MVTec AD |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Plant science |
| 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 |
|
|
|
https://hyokadb02.jimu.kyutech.ac.jp/html/100000804_ja.html |
| 論文ID（連携） |
|
|
|
10430362 |
| 連携ID |
|
|
|
12358 |
10430362.pdf (4.4 MB)
