RaViT-AE: Unsupervised Anomaly Detection for Intelligent Cultural Heritage Monitoring Using Region-Attentive ViT Autoencoder
Unsupervised anomaly detection is well known for its ability to effectively identify and discern anomalies in data containing rare anomalies or diverse patterns, leading to broad applications across various research fields. However, this technology has not yet been extensively applied in the field o...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10772234/ |
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