Papers Multi-class Anomaly Detection
“Multi-class Anomaly Detection” 태그가 달린 논문 52편 · 필터 해제
XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection
The remarkable success of reconstruction-based methods in Unsupervised Anomaly Detection (UAD) lies in their ability to identify and localize anomalies by modeling discrepancies between input images and their reconstruct…
Unsupervised Anomaly DetectionMulti-class Anomaly DetectionBoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Reconstruction-based anomaly detection is attractive for industrial inspection, but scaling it from category-specific training to a one-for-all setting is challenging. A single model must reconstruct diverse normal appea…
Multi-class Anomaly DetectionUni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection
Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. When shifting to a unified paradigm that handles diverse classes simul…
Multi-class Anomaly DetectionDual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to increasingly complex and heterogeneous norma…
Unsupervised Anomaly DetectionMulti-class Anomaly DetectionLarge-Scale Universal Defect Generation: Foundation Models and Datasets
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data. This issue is aggravated by substantial …
Multi-class Anomaly DetectionFew-Shot LearningAnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical workflows. Existing single-class or multi-cl…
Multi-class Anomaly DetectionCollaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection
Industrial anomaly detection is a challenging open-set task that aims to identify unknown anomalous patterns deviating from normal data distribution. To avoid the significant memory consumption and limited generalizabili…
Multi-class Anomaly DetectionABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-language models offer strong generalization…
Multi-class Anomaly DetectionFew-Shot LearningVLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion
Detecting visual anomalies in diverse, multi-class real-world images is a significant challenge. We introduce \ours, a novel unsupervised multi-class visual anomaly detection framework. It integrates a Latent Diffusion M…
Multi-class Anomaly DetectionUniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression
Existing anomaly detection (AD) methods often treat the modality and class as independent factors. Although this paradigm has enriched the development of AD research branches and produced many specialized models, it has …
Multi-class Anomaly DetectionGeneralist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networks
Anomaly detection (AD) plays an important role in various real-world applications. Recent advancements in AD, however, are often biased towards industrial inspection, struggle to generalize to broader tasks like semantic…
Multi-class Anomaly DetectionKnowledge DistillationSynthetic Image Detection via Spectral Gaps of QC-RBIM Nishimori Bethe-Hessian Operators
The rapid advance of deep generative models such as GANs and diffusion networks now produces images that are virtually indistinguishable from genuine photographs, undermining media forensics and biometric security. Super…
Multi-class Anomaly DetectionMulti-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions
Recent advances in image anomaly detection have extended unsupervised learning-based models from single-class settings to multi-class frameworks, aiming to improve efficiency in training time and model storage. When a si…
Multi-class Anomaly DetectionCenter-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of dev…
Anomaly DetectionMulti-class Anomaly DetectionPyramid-based Mamba Multi-class Unsupervised Anomaly Detection
Recent advances in convolutional neural networks (CNNs) and transformer-based methods have improved anomaly detection and localization, but challenges persist in precisely localizing small anomalies. While CNNs face limi…
Anomaly DetectionAnomaly LocalizationMambaMulti-class Anomaly Detection+1Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
In multi-class unsupervised anomaly detection(MUAD), reconstruction-based methods learn to map input images to normal patterns to identify anomalous pixels. However, this strategy easily falls into the well-known "learni…
Anomaly DetectionDecoderMulti-class Anomaly DetectionUnsupervised Anomaly DetectionExploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning …
Anomaly DetectionMulti-class Anomaly DetectionUniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples. However, most existing AD methods suffer from limited generalizability, as they are primarily designed for domain-spec…
Anomaly DetectionImage ClassificationMedical Image SegmentationMulti-class Anomaly Detection+1CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
Existing unsupervised distillation-based methods rely on the differences between encoded and decoded features to locate abnormal regions in test images. However, the decoder trained only on normal samples still reconstru…
Anomaly DetectionAttributeDecoderDiversity+2Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning
For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward …
Anomaly DetectionContrastive LearningMulti-class Anomaly Detection