Papers Anomaly Classification
“Anomaly Classification” 태그가 달린 논문 103편 · 필터 해제
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibil…
Anomaly ClassificationAnomaly DetectionOne-Class Domain Adaptation via Meta-Learning
The deployment of IoT (Internet of Things) sensor-based machine learning models in industrial systems for anomaly classification tasks poses significant challenges due to distribution shifts, as the training data acquire…
Anomaly ClassificationDomain AdaptationMeta-LearningCEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Electroencephalograph (EEG) is a crucial tool for studying brain activity. Recently, self-supervised learning methods leveraging large unlabeled datasets have emerged as a potential solution to the scarcity of widely ava…
Anomaly ClassificationEEGSeizure DetectionSelf-Supervised LearningDual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augmen…
Anomaly ClassificationAnomaly DetectionAnomaly LocalizationDiversity+1SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their performance is limited in practical applicati…
Anomaly ClassificationAnomaly DetectionClassificationUnsupervised Anomaly DetectionCLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP
Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data in industrial scenarios, lots of few-sh…
Anomaly ClassificationAnomaly DetectionImage to textCircuit design in biology and machine learning. II. Anomaly detection
Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological system…
Anomaly ClassificationAnomaly DetectionDecision MakingDimensionality ReductionMulti-Class Abnormality Classification Task in Video Capsule Endoscopy
In this work for Capsule Vision Challenge 2024, we addressed the challenge of multiclass anomaly classification in video capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to adva…
Anomaly ClassificationDiagnosticAnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces tw…
Anomaly ClassificationAnomaly DetectionBinarizationNovel Class Discovery+2DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment th…
Anomaly ClassificationAnomaly DetectionAnomaly LocalizationDiversity+1Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization
Overfitting commonly occurs when applying deep neural networks (DNNs) on small-scale datasets, where DNNs do not generalize well from existing data to unseen data. The main reason resulting in overfitting is that small-s…
Anomaly ClassificationBayesian OptimizationAnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model
Anomaly detection is a critical task in industrial manufacturing, aiming to identify defective parts of products. Most industrial anomaly detection methods assume the availability of sufficient normal data for training. …
Anomaly ClassificationAnomaly DetectionMulti-class Anomaly DetectionCLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
Few-shot anomaly detection methods can effectively address data collecting difficulty in industrial scenarios. Compared to 2D few-shot anomaly detection (2D-FSAD), 3D few-shot anomaly detection (3D-FSAD) is still an unex…
Anomaly ClassificationAnomaly DetectionDecoderUnraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis of these anomalies can significantly enh…
Anomaly ClassificationAnomaly DetectionTime SeriesTime Series Alignment+2MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
Previous unsupervised anomaly detection (UAD) methods often struggle to handle the extensive diversity in training sets, particularly when they contain stylistically diverse and feature-rich samples, which we categorize …
Anomaly ClassificationAnomaly DetectionAnomaly SegmentationDiversity+1Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
Image Anomaly Detection has been a challenging task in Computer Vision field. The advent of Vision-Language models, particularly the rise of CLIP-based frameworks, has opened new avenues for zero-shot anomaly detection. …
Anomaly ClassificationAnomaly DetectionTest-time Adaptationzero-shot anomaly detectionMedIAnomaly: A comparative study of anomaly detection in medical images
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays …
Anomaly ClassificationAnomaly DetectionAnomaly SegmentationSelf-Supervised Learning+1Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, the substantial domain divergence between …
Anomaly ClassificationAnomaly DetectionAnomaly SegmentationLarge Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospe…
Anomaly ClassificationAnomaly DetectionAnomaly ForecastingSpatio-Temporal Forecasting+2MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly det…
Anomaly ClassificationAnomaly Detection