arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.
By Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu
The paper introduces MUSTARD, a multimodal spatiotemporal anomaly detection framework for industrial products that enhances large language models with fine‑grained motion awareness. It uses optical‑flow‑guided keyframe selection and augmented modalities to capture both local motion patterns and global dynamic evolution, enabling detection and reasoning of dynamics‑based defects with only a few normal reference samples. Experiments show that MUSTARD outperforms existing methods in identifying spatiotemporal anomalies and offers interpretable explanations.
By S-Wei Chen, Siangyu Yang, Yi-Chen Jan, Chun-Yi Lee, Shao-Yuan Lo
arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
By Xudong Mou, Rui Wang, Tiejun Wang, Zexin Wu, Fangda Guo, Jie Sun, Shiru Chen, Penghao Zhang, Tiezi Zhang, Tianyu Wo, Hao Peng, Chunming Hu, Xudong Liu, Renyu Yang
The paper introduces MUSTARD, a multimodal spatiotemporal anomaly detection framework that enhances large language models with fine‑grained motion awareness for industrial defect detection. It uses optical‑flow‑guided keyframe selection and augmented modalities to capture local motion patterns and global dynamic evolution, enabling detection of dynamics‑based anomalies without model updates. Experiments show that MUSTARD outperforms existing methods in identifying spatiotemporal defects and offers interpretable explanations.
The paper introduces a multimodal anomaly detection framework that uses cross‑modal reconstruction of heterogeneous time‑series sensor data to detect faults in industrial systems. By learning to reconstruct each modality from the others, the method leverages complementary information across sensing channels without requiring explicit temporal alignment or identical sampling rates. An adaptive test‑time thresholding mechanism further improves robustness to distribution shifts caused by changing operating conditions, as demonstrated by strong fault detection performance in three industrial case studies, especially under out‑of‑distribution regimes.
By Magnus Munk Jensen, Dorte Hammersh{\o}i, Rafa{\l} Wi\'sniewski, Olga Fink
arXiv:2609.13940v1 Announce Type: new
Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...
By Abigail Langbridge, Fearghal O'Donncha, James T Rayfield, Bradley Eck