الملخص
Automated quality inspection is essential for ensuring product reliability in manufacturing.While image-based methods effectively capture appearance-related defects, these methods are limited in detecting structural and geometric anomalies, motivating multimodal approaches incorporating 3D information. However, existing methods mainly rely on local patch-level representations, which often lead to unstable reconstruction errors even in normal regions. To address this limitation, we propose GRC-Net, which integrates a global-attention MLP to enforce global representation consistency across patch embeddings with a stable reconstruction module to improve reconstruction stability. The proposed method captures holistic contextual information through a global token and suppresses reconstruction noise by minimizing discrepancies between original and predicted embeddings. Experiments on MVTec 3D-AD and Eyecandies demonstrate that GRC-Net consistently outperforms existing methods at both image and pixel levels. Qualitative results further demonstrate reduced reconstruction errors in normal regions and more distinct reconstruction differences between normal and anomalous regions.
الكلمات المفتاحية
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Jeong, S., Yun, J. P., & Lee, S. J. (2026). GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection. https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection
MLA 9
Jeong, Seyoung, et al. "GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection." https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection.
شيكاغو (المؤلف–التاريخ)
Jeong, Seyoung, Jong Pil Yun, and Sang Jun Lee. 2026. "GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection." https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection.
هارفارد
Jeong, S., Yun, J. P. and Lee, S. J. (2026) 'GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection', Available at: https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection.
فانكوفر
Jeong S, Yun JP, Lee SJ. GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection. https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection
IEEE
S. Jeong, J. P. Yun, and S. J. Lee, "GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection," https://omanscience.com/ar/articles/grc-net-global-representation-consistency-network-for-unsupervised-multimodal-anomaly-detection.