SOTAVerified

Anomaly Classification

Anomaly Classification is the task of identifying and categorizing different types of anomalies in visual data, rather than simply detecting whether an input is normal or anomalous. Unlike anomaly detection, which is typically a binary classification (normal vs. anomaly), anomaly classification requires distinguishing between multiple anomaly classes—each representing a distinct type of anomaly or irregularity. This task is critical in real-world applications such as industrial inspection, where different anomalies may require different responses or interventions.

Papers

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Showing 1–50 of 72 papers

TitleStatusHype
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection—0
A Cytology Dataset for Early Detection of Oral Squamous Cell CarcinomaCode0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—0
Few-Shot Anomaly-Driven Generation for Anomaly Classification and SegmentationCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification—0
Video Anomaly Detection with Structured KeywordsCode0
Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data—0
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect CommunicationCode0
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?—0
One-Class Domain Adaptation via Meta-Learning—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
SoftPatch+: Fully Unsupervised Anomaly Classification and SegmentationCode2
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
Circuit design in biology and machine learning. II. Anomaly detection—0
Multi-Class Abnormality Classification Task in Video Capsule EndoscopyCode0
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation—0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
MedIAnomaly: A comparative study of anomaly detection in medical imagesCode2
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical ImagesCode3
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review—0
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled ImagesCode3
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks—0
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRICode0
PatchProto Networks for Few-shot Visual Anomaly Classification—0
A Prototype-Based Neural Network for Image Anomaly Detection and LocalizationCode0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Conditioning Latent-Space Clusters for Real-World Anomaly Classification—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams—0
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and SegmentationCode1
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADCode2
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification—0
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus—0
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationCode2
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants—0
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine LearningCode1
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays—0
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All datasetsGoodsADMVTecADMVTec-ACVisA-AC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PatchCore-100%AUPR86.1—Unverified
2MiniMaxAD-frAUROC86.1—Unverified
3PatchCore-1%AUPR83.3—Unverified
4SimpleNetAUPR78.7—Unverified
5CFLOW-ADAUPR75.3—Unverified
6NSAAUPR71.8—Unverified
7DRAEMAUPR71—Unverified
8SPADEAUPR68.7—Unverified
9RD4ADAUPR68.2—Unverified
10f-AnoGANAUPR66.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy (% )81.4—Unverified
2EchoAccuracy (% )72.9—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy (% )84—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy(%)69.6—Unverified