arXiv:2606. 09670v1 Announce Type: cross Abstract: Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec.
By Mateo Diaz-Bone, Daniel Caraballo, Florian Scheidegger, Thomas Frick, Mattia Rigotti, Andrea Bartezzaghi, Roy Assaf, Niccolo Avogaro, Yagmur G. Cinar, Brown Ebouky, Filip M. Janicki, Piotr S. Kluska, Cezary Skura, Cristiano Malossi
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.
The paper identifies a problem in multi‑view anomaly detection called cross‑view information leakage, where fusing multiple inspection views can cause normal features to mask anomalies during reconstruction. To address this, the authors propose GLAD, a framework that uses a Global‑Local Attention Driven approach, combining vision foundation model features with two fusion modules: Multi‑view Merging Attention for local, weighted fusion and Object‑Guided Attention for global context aggregation. Experiments on Real‑IAD and MANTA‑Tiny demonstrate that GLAD outperforms existing methods across various metrics, underscoring the importance of restricting information flow to preserve the reconstruction gap.
By Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu, Wen-Huang Cheng, Kai-Lung Hua
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li
Visual segmentation systems encounter objects outside their training distribution during real-world deployment, hindering reliable autonomous systems that depend on scene parsing in the perception sta...