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.
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
arXiv:2602.09850v3 Announce Type: replace
Abstract: Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs...
By Peng Chen, Wenqiang Wang, Yunkang Cao, Chengliang Liu, Wei Wang, Mingbo Yang, Li Shen, Wenqi Ren, Xiaochun Cao, Chao Huang
The paper introduces Anomaly‑LR, a defect‑grounded latent reasoning framework for industrial anomaly detection that builds a global understanding of an image and then refines anomaly‑relevant representations directly in visual latent space. It also presents IAD‑LR‑22K, a new instruction dataset with 22,228 image‑question pairs and detailed annotations. Experiments demonstrate that Anomaly‑LR outperforms comparable‑scale methods on multiple IAD benchmarks without needing external references or tools.
By Jaron Yeh, Yen-Wei Chang, Jiang Liu, Shao-Yuan Lo
arXiv:2610.15032v1 Announce Type: cross
Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in whi...
By {\L}ukasz Marcjan, Dorota Wilk-Ko{\l}odziejczyk, Roberto Corizzo
This survey reviews industrial anomaly synthesis (IAS), a field that has been underrepresented in prior studies focused on anomaly detection. It categorizes IAS methods into four paradigms—hand‑crafted, distribution‑hypothesis, generative model, and vision‑language model—while presenting a taxonomy that facilitates systematic comparison. The paper also discusses datasets, benchmarks, evaluation metrics, multimodal advances, and practical deployment considerations, offering guidance for future research.
By Yanshu Wang, Xichen Xu, Jinbao Wang, Chengbin Ma, Xiaoning Lei, Guoyang Xie, Guannan Jiang, Zhichao Lu
arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.
By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao
The paper introduces VisAnomBench, a new benchmark that augments public time‑series datasets with high‑quality natural‑language anomaly explanations generated by large vision‑language models. Using this benchmark, the authors fine‑tune a parameter‑efficient VLM called VisAnomReasoner, which achieves significantly higher precision and F1 scores than existing baselines on VisAnomBench and also generalizes well to the TSB‑AD‑U benchmark.
By Xiaona Zhou, Muntasir Wahed, Tianjiao Yu, Constantin Brif, Ismini Lourentzou
arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.
By Peipei Zhu, Yueqing Niu, Lin Zhu, Guanchong Niu, Yang Yu, Zheng Li
The paper introduces NC‑TFAD, a task‑free continual anomaly detection framework that leverages neural‑collapse geometry to handle non‑stationary data streams in industrial visual inspection. It freezes a pretrained backbone, aligns streaming features to a simplex Equiangular Tight Frame prototype space, and uses synthetic anomaly anchors, inter‑ and intra‑class regularization, and a Focal Neural Collapse Contrastive loss to stabilize representations and enhance normal‑anomaly separability. A normal‑patch‑prototype‑guided localization branch generates calibrated anomaly heatmaps without pixel‑level annotations, and experiments on MVTec AD and VisA demonstrate that NC‑TFAD outperforms existing task‑free continual learning and unified anomaly detection baselines in both image‑level detection and pixel‑level localization.
The paper introduces an adaptive temporal modeling framework for weakly supervised video anomaly detection that addresses the limitations of rigid Multiple Instance Learning approaches. It presents a Temporal Refinement Module using dynamic positional encoding and a learnable class token to capture long‑range dependencies, and an Event Segmentation Module that identifies event boundaries via temporal discontinuity analysis to produce discriminative event‑level representations. An adaptive similarity‑based fusion strategy replaces fixed top‑k heuristics, dynamically integrating snippet‑level and event‑level anomaly scores into video‑level predictions, and the method outperforms state‑of‑the‑art baselines on two benchmarks.
By Changyi Li, Yu Xiao
arXiv:2608.22789v1 Announce Type: new
Abstract: Additive Manufacturing (AM) plays a vital role in the ongoing industrial revolution. However, quality control remains crucial and challenging due to pr...
By Sosmita Paul, Krishna Roy