Jul 2026· IEEE Transactions on Image Processing· Vol 35, pp. 7623-7638· 0 citations· 63 references
Computer ScienceMedicine
Abstract
Visual industrial anomaly detection has evolved from one-class modeling to more challenging multi-class settings, where diverse categories and complex visual patterns must be jointly handled. Existing approaches often assume that anomalies lie far from normal samples in feature or spatial space. However, this assumption frequently fails due to two key issues: cross-class semantic confusion, where normal structures of one category are misclassified as anomalies in another, and pixel similarity failure, where anomalous regions visually blend into normal backgrounds. To address these challenges, we propose RPGAD (Region-Prompt Guided Anomaly Detection), an information-theoretic framework that models anomalies as semantic predictive instability, reflected in the joint responses of dual paths. RPGAD integrates two components: 1) DPENet (Dual-Path regional Energy evaluation Network), which compares region-level responses across normal-only and mixed paths through an entropy-guided energy formulation to generate robust region prompts; and 2) RDNet (Reverse Distillation Network), which selectively reconstructs prompted regions and employs a Prototype-Contrastive Optimal Transport (PCOT) loss to enhance inter-class separability and local feature aggregation. Experiments on five anomaly detection benchmarks - MVTecAD, VisA, BTAD, MPDD, and Real-IAD - demonstrate the effectiveness of RPGAD. At $256 \times 256$ resolution, RPGAD achieves strong overall performance, with mAD of 87.8%, 80.3%, 85.2%, 86.2%, and 77.7% on five benchmarks, and pixel-level AP and F1-max gains of up to 12.2 and 10.4 points over strong baselines. These results confirm that RPGAD provides accurate and robust multi-class anomaly detection and localization in complex visual scenarios.
In industrial visual inspection, unsupervised anomaly detection has significant application value due to the elimination of anomaly labeling requirements. However, existing methods often rely on independent modeling by category, leading to high storage and maintenance costs; unified multi-category modeling is susceptible to the diversity of normal patterns, resulting in approximate identity mappings and weakening anomaly representation capabilities. To address these issues, we propose a hierarchically conditioned denoising and guidance framework (HCDG), which combines adaptive hierarchical feature fusion with error-aware conditional denoising. HCDG integrates shallow texture and deep semantic features and uses noise prediction errors to guide adaptive denoising in the feature bottleneck. A feature-guided decoder reconstructs normal features, and reconstruction and noise prediction errors are jointly used for image-level and pixel-level anomaly scoring. HCDG achieves competitive overall performance on MVTec AD, reaching 99.7% I-AUROC, 99.8% I-AP, and 99.4% I-F1-max at the image level. At the pixel level, HCDG attains 98.4% P-AUROC, 70.2% P-AP, 69.9% P-F1-max, and 95.0% P-AUPRO. These results suggest that the proposed denoising and guidance strategy not only preserves image-level discrimination but also yields more stable pixel-level localization under unified multi-class training.
Yan Luo, Hongyang Zhao, Jiayi Sun et al.· Measurement science and tech...· 0 citations
LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.
Tu Nguyen, Dang Nguyen, Thuc Duy Le et al.· 0 citations
Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent ZSAD methods build on pretrained vision models, particularly CLIP, and construct normal and anomaly references from textual prompts or learnable visual representations. These methods perform anomaly discrimination primarily in spatial feature spaces, where subtle changes in texture, boundaries, and local structures can be confused with normal appearance variations. Although inconspicuous spatially, such defects can disrupt local texture regularity or boundary continuity, inducing response deviations across frequency bands. However, existing ZSAD methods do not explicitly model these frequency-dependent characteristics. Our image-domain analysis reveals that local defects exhibit spatial-frequency deviations from normal references across low-, middle-, and high-frequency bands, indicating that anomaly evidence is not universally dominated by high-frequency responses. Motivated by this observation, we propose FreqAnchorAD, a frequency-aware framework that organizes frequency-enhanced responses for anchor-relative anomaly discrimination. Specifically, the Local Frequency Compensation Module (LFCM) enhances intermediate patch tokens with local spatial-frequency cues. The Frequency-Deviation Anchor Projector (FDAP), our core discrimination module, organizes enhanced responses along a source-derived channel coordinate and measures anomaly evidence through relative similarity to normal and anomaly anchors. Finally, Asymmetric Anchor Supervision (AAS) stabilizes normal-anchor alignment while preserving diverse anomaly patterns. Experiments on thirteen industrial and medical benchmarks show that FreqAnchorAD achieves state-of-the-art mean performance in image-level anomaly recognition and pixel-level defect localization.
Jianfeng Qiu, Peiyuan Li, Juan Xie et al.· 0 citations
Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as local texture corruptions, and logical anomalies, which violate global rules on object count, composition, or arrangement. Existing detectors typically favor one family at the expense of the other. In particular, training-free methods effectively exploit frozen representations but lack an explicit notion of object count, while methods that reason about counts usually rely on category-specific component modeling. We show that counting ability can be introduced into training-free anomaly detection without additional training or part-level supervision. Our key idea is a normal-set calibration that aligns heterogeneous anomaly cues using statistics from normal images, enabling their direct fusion within a unified training-free framework. Built upon this calibration, our detector combines complementary frozen cues to address both logical and structural anomalies. On MVTec-LOCO, our method achieves image-level AUROCs of 89.0 and 95.9 on logical and structural anomalies, respectively, yielding a 92.5 average---the best among training-free detectors in our comparison. It remains competitive with methods requiring network training or part annotations, while its structural variant matches PatchCore on MVTec-AD (99.1 image-AUROC), suggesting that the proposed calibration generalizes beyond logical anomaly detection.
Anomaly detection is an essential part in industrial production which intends to identify anomaly samples and localize anomaly regions. Although current unsupervised anomaly detection methods have achieved excellent performance, they rely on clean and unlabeled normal samples. However, it is rarely satisfied in industrial scenarios and training with contaminated data is an inevitable challenge. To solve this problem, we propose an effective anomaly detection framework, named ContaminationAD. In our method, a pseudo-label generation strategy is introduced to evaluate the contamination levels of image patches and determines whether the sample is contaminated. Following this, a coarse localization of contaminated regions can be obtained and pseudo-labels are generated to guide text feature assignment for each sample. To concentrate on regions of interest while not neglecting fine-grained information, we design the Dual-Expert Network by incorporating routing mechanism to process visual feature adaptively. Based on this design, we propose semantic-driven segmentation weighting to facilitate anomaly localization. By applying routing weights and coarse localization when calculating the similarity between textual and local visual features, the training samples are dynamically weighted which enables the network to achieve a balance between the learning of normal and contaminated regions. Comprehensive experiments on the MVTecAD, VisA and BTAD datasets demonstrate that ContaminationAD outperforms the state-of-the-art anomaly detection methods. Notably, ContaminationAD exhibits strong robustness in the presence of data contamination, even when the contamination level is severe.
A novel large multimodal model applying vision experts for industrial anomaly detection (abbreviated as Myriad), which treats conventional IAD models as VEs and converts their anomaly maps into lightweight prompts that steer a frozen Q-Former toward suspicious regions, while a compact low-rank adapter shapes features for IAD.
Yuanze Li, Haolin Wang, Shihao Yuan et al.· Science China Information Sc...· 0 citations
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