MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization.
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| Title: | MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization. |
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| Authors: | Guo, Jie1 (AUTHOR), Han, Congyu2 (AUTHOR), Ma, Yujie1 (AUTHOR), Zhang, Chen1 (AUTHOR) zhangchen01@tsinghua.edu.cn |
| Source: | IISE Transactions. Dec2025, Vol. 57 Issue 12, p1391-1406. 16p. |
| Subjects: | Reinforcement learning, Deep reinforcement learning, Image recognition (Computer vision), Recurrent neural networks, Data quality |
| Abstract: | With the rapid development of the digital manufacturing industry, the nature of quality data has transformed from simple univariate or multivariate characteristics to big data comprising multimedia elements such as images and videos. The utilization of image data for automated monitoring and anomaly detection has gained significant attention in recent years, which also poses new and complex challenges. A critical challenge is the substantial demand for sensing and computation resources. When these resources are limited, only a fraction of the image data can be observed and analyzed. Hence, adaptive sampling becomes imperative to select the most informative pixels that effectively capture anomaly information. In this article, we propose a novel recurrent neural network framework named Multi Task-Recurrent Attention Model (MT-RAM) which incorporates adaptive sampling for anomaly classification and localization in partially observable image data. MT-RAM emulates human-like perception by generating a sequence of glimpses to comprehend the image, with the location of each glimpse depending on the information gleaned from previous glimpses. Thorough numerical studies and case studies are conducted to evaluate the performance of MT-RAM in comparison to state-of-the-art adaptive-sampling-based anomaly detection methods. [ABSTRACT FROM AUTHOR] |
| Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188054330 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Jie%22">Guo, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Congyu%22">Han, Congyu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Yujie%22">Ma, Yujie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chen%22">Zhang, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangchen01@tsinghua.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IISE+Transactions%22">IISE Transactions</searchLink>. Dec2025, Vol. 57 Issue 12, p1391-1406. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rapid development of the digital manufacturing industry, the nature of quality data has transformed from simple univariate or multivariate characteristics to big data comprising multimedia elements such as images and videos. The utilization of image data for automated monitoring and anomaly detection has gained significant attention in recent years, which also poses new and complex challenges. A critical challenge is the substantial demand for sensing and computation resources. When these resources are limited, only a fraction of the image data can be observed and analyzed. Hence, adaptive sampling becomes imperative to select the most informative pixels that effectively capture anomaly information. In this article, we propose a novel recurrent neural network framework named Multi Task-Recurrent Attention Model (MT-RAM) which incorporates adaptive sampling for anomaly classification and localization in partially observable image data. MT-RAM emulates human-like perception by generating a sequence of glimpses to comprehend the image, with the location of each glimpse depending on the information gleaned from previous glimpses. Thorough numerical studies and case studies are conducted to evaluate the performance of MT-RAM in comparison to state-of-the-art adaptive-sampling-based anomaly detection methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/24725854.2024.2425292 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1391 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Data quality Type: general Titles: – TitleFull: MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Jie – PersonEntity: Name: NameFull: Han, Congyu – PersonEntity: Name: NameFull: Ma, Yujie – PersonEntity: Name: NameFull: Zhang, Chen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 24725854 Numbering: – Type: volume Value: 57 – Type: issue Value: 12 Titles: – TitleFull: IISE Transactions Type: main |
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