The paper introduces Concept-Aware Visual Anomaly Detection (Concept-Aware VAD), a framework that extends Concept Bottleneck Models to visual anomaly detection. It offers dual-branch architecture: a concept branch for human-interpretable descriptions and a visual branch for pixel-level localization, evaluated on three VAD benchmarks with competitive performance. A user study shows higher effectiveness and lower misleading rates compared to state-of-the-art explainers, and concept-level interventions improve unsupervised detection.
arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.
arXiv:2607. 18850v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning.
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
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...
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set.
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.
arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.
Probe‑VAD introduces an ordinal binary‑probing framework that leverages frozen vision‑language models for training‑free video anomaly detection. By querying ten ordered severity thresholds and extracting YES/NO continuation likelihoods, it builds a cumulative severity profile that is converted into a continuous anomaly score with isotonic projection for ordinal consistency. Experiments on public benchmarks show that this simple interface yields superior performance at low computational cost, avoiding the limitations of caption‑based compression or restricted numerical scoring.
arXiv:2608.29783v1 Announce Type: new Abstract: Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature di...
The paper introduces a new task called Quality Anomaly Perception for UGC Image Enhancement (UEAP) and presents the first benchmark dataset, UEAP-4k, featuring fine‑grained annotations of anomaly categories, locations, and severity levels in real‑world user‑generated content. It proposes the Difference‑Fusion Anomaly Perception Method (DFAP‑UGC), which fuses explicit differences between enhanced images and their references using dense spatial querying, regional verification, and quality‑aware ranking to robustly identify localized anomalies. A Locality‑Aware Dynamic Task Prioritization (LADTP) training strategy is also introduced to enable efficient end‑to‑end learning without multi‑stage overhead, and experiments demonstrate that DFAP‑UGC outperforms adapted classical baselines.
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data.