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Anomaly Classification

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보존된 평가 기록: 16

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.

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A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection

Anomaly Classification

논문

A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect

Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models

Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification

Video Anomaly Detection with Structured Keywords

Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data

Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

One-Class Domain Adaptation via Meta-Learning

CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention

SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP

Circuit design in biology and machine learning. II. Anomaly detection

Multi-Class Abnormality Classification Task in Video Capsule Endoscopy

AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios

DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation

Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization

AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model

CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation

Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry

MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection

Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection

MedIAnomaly: A comparative study of anomaly detection in medical images

Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

PatchProto Networks for Few-shot Visual Anomaly Classification

A Prototype-Based Neural Network for Image Anomaly Detection and Localization

SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets

Conditioning Latent-Space Clusters for Real-World Anomaly Classification

Classification of Anomalies in Telecommunication Network KPI Time Series