Papers Anomaly Segmentation
“Anomaly Segmentation” 태그가 달린 논문 116편 · 필터 해제
MIAS-SAM: Medical Image Anomaly Segmentation without thresholding
This paper presents MIAS-SAM, a novel approach for the segmentation of anomalous regions in medical images. MIAS-SAM uses a patch-based memory bank to store relevant image features, which are extracted from normal data u…
Anomaly SegmentationDecoderSegmentationMetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of…
Anomaly DetectionAnomaly SegmentationMeta-LearningSegmentation+1Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt
Unsupervised reconstruction networks using self-attention transformers have achieved state-of-the-art performance for multi-class (unified) anomaly detection with a single model. However, these self-attention reconstruct…
Anomaly DetectionAnomaly SegmentationAdvancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models
We explore Generalizable Tumor Segmentation, aiming to train a single model for zero-shot tumor segmentation across diverse anatomical regions. Existing methods face limitations related to segmentation quality, scalabili…
Anomaly SegmentationSegmentationTumor SegmentationSpotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segmentation in 2D, research progress in 3D …
3D Anomaly DetectionAnomaly DetectionAnomaly SegmentationAutonomous Driving+2Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse i…
Anomaly SegmentationLanguage ModelingLanguage ModellingLarge Language Model+1MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial to know the distinct…
Anomaly DetectionAnomaly SegmentationFew-Shot LearningZero-Shot LearningA Dataset for Semantic Segmentation in the Presence of Unknowns
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalies). This is especially important for sce…
Anomaly DetectionAnomaly SegmentationAutonomous DrivingDomain Generalization+3Multi-modality Anomaly Segmentation on the Road
Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles that may jeopardize the safety of autonomo…
Anomaly SegmentationAutonomous DrivingSegmentationSemantic SegmentationScreener: Self-supervised Pathology Segmentation Model for 3D Medical Images
Accurate segmentation of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To add…
Anomaly SegmentationSegmentationSelf-Supervised LearningTeacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection
Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addre…
Anomaly DetectionAnomaly SegmentationDecoderDenoising+2Towards Accurate Unified Anomaly Segmentation
Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the eff…
Anomaly DetectionAnomaly SegmentationSegmentationUnsupervised Anomaly DetectionKAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show poten…
Anomaly DetectionAnomaly SegmentationGeneral KnowledgeLarge Language Model+4Neural Network Meta Classifier: Improving the Reliability of Anomaly Segmentation
Deep neural networks (DNNs) are a contemporary solution for semantic segmentation and are usually trained to operate on a predefined closed set of classes. In open-set environments, it is possible to encounter semantical…
Anomaly SegmentationregressionSemantic SegmentationFlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation
Anomaly segmentation is a valuable computer vision task for safety-critical applications that need to be aware of unexpected events. Current state-of-the-art (SOTA) scene-level anomaly segmentation approaches rely on div…
Anomaly SegmentationAutonomous DrivingContrastive LearningSegmentationPromptable Anomaly Segmentation with SAM Through Self-Perception Tuning
Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the doma…
Anomaly Segmentationparameter-efficient fine-tuningVL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection
Semantic segmentation networks have achieved significant success under the assumption of independent and identically distributed data. However, these networks often struggle to detect anomalies from unknown semantic clas…
Anomaly DetectionAnomaly SegmentationSemantic SegmentationFADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
Automatic image anomaly detection is important for quality inspection in the manufacturing industry. The usual unsupervised anomaly detection approach is to train a model for each object class using a dataset of normal s…
Anomaly DetectionAnomaly SegmentationLanguage ModelingLanguage Modelling+2AutoRG-Brain: Grounded Report Generation for Brain MRI
Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leadi…
Anomaly LocalizationAnomaly SegmentationDiffusion for Out-of-Distribution Detection on Road Scenes and Beyond
In recent years, research on out-of-distribution (OoD) detection for semantic segmentation has mainly focused on road scenes -- a domain with a constrained amount of semantic diversity. In this work, we challenge this co…
Anomaly DetectionAnomaly SegmentationDiversityOut-of-Distribution Detection+2