Papers Anomaly Localization
“Anomaly Localization” 태그가 달린 논문 101편 · 필터 해제
AnoDODE: Anomaly Detection with Diffusion ODE
Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. In the realm of clinical screening and diagnosis, detecting abnormalities in medical imag…
Anomaly DetectionAnomaly LocalizationBraTS2021Density Estimation+1A Prototype-Based Neural Network for Image Anomaly Detection and Localization
Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in v…
Anomaly ClassificationAnomaly DetectionAnomaly LocalizationWeakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
With a focus on abnormal events contained within untrimmed videos, there is increasing interest among researchers in video anomaly detection. Among different video anomaly detection scenarios, weakly-supervised video ano…
Anomaly DetectionAnomaly LocalizationSupervised Anomaly DetectionVideo Anomaly Detection+1Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
Unsupervised anomaly localization, which plays a critical role in industrial manufacturing, aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or rec…
Anomaly LocalizationAutoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control
Manufacturing industries require efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled prod…
Anomaly DetectionAnomaly LocalizationDefect DetectionManufacturing Quality ControlBootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
Contrastive Language-Image Pre-training (CLIP) models have shown promising performance on zero-shot visual recognition tasks by learning visual representations under natural language supervision. Recent studies attempt t…
Anomaly DetectionAnomaly LocalizationTest-time Adaptationzero-shot anomaly detectionMSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection
Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD applications intend to further locate the …
Anomaly DetectionAnomaly LocalizationUnsupervised Anomaly DetectionWeakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder
This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection framework that computes metrics directl…
Anomaly DetectionAnomaly LocalizationGraph Self-Supervised Learning for Endoscopic Image Matching
Accurate feature matching and correspondence in endoscopic images play a crucial role in various clinical applications, including patient follow-up and rapid anomaly localization through panoramic image generation. Howev…
Anomaly LocalizationImage GenerationSelf-Supervised Learning2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we prese…
Anomaly DetectionAnomaly LocalizationAnomaly SegmentationNovelty Detection+2Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy. However, this improvement often comes …
Anomaly DetectionAnomaly LocalizationSupervised Anomaly DetectionUnsupervised Anomaly DetectionSegment Any Anomaly without Training via Hybrid Prompt Regularization
We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentatio…
Anomaly DetectionAnomaly LocalizationAnomaly SegmentationSegmentation+1Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of …
Anomaly DetectionAnomaly LocalizationHigh-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis
We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective…
Anomaly LocalizationVocal Bursts Intensity PredictionUnsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach
Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this be…
Anomaly DetectionAnomaly LocalizationDenoisingGenerative Adversarial Network+2ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
In the realm of machine learning, the study of anomaly detection and localization within image data has gained substantial traction, particularly for practical applications such as industrial defect detection. While the …
Anomaly DetectionAnomaly LocalizationData AugmentationDefect Detection+2Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Anomaly detectors are widely used in industrial manufacturing to detect and localize unknown defects in query images. These detectors are trained on anomaly-free samples and have successfully distinguished anomalies from…
Anomaly DetectionAnomaly LocalizationGPUCollaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overge…
Anomaly DetectionAnomaly LocalizationAdapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localizati…
Anomaly DetectionAnomaly LocalizationAnomaly SegmentationOne-Class ClassificationOmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization
Unsupervised anomaly localization and detection is crucial for industrial manufacturing processes due to the lack of anomalous samples. Recent unsupervised advances on industrial anomaly detection achieve high perfor…
Anomaly DetectionAnomaly LocalizationMulti-class Anomaly Detection