Papers Anomaly Detection
“Anomaly Detection” 태그가 달린 논문 5,960편 · 필터 해제
Adapting prior-data fitted networks for tabular anomaly detection
While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the limited availability of strong deep representations. Recently, prior-d…
Anomaly DetectionAnomaly Detection and Localization for the Pantograph-Catenary System
Monitoring the Pantograph-Catenary System (PCS) provides insight into the health conditions of the pantograph and the railway infrastructure. Recent industrial solutions trace the pantograph's contact wire height and sta…
Anomaly DetectionFreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection
Unauthorized hardware replacement can preserve a wireless device's logical identity while altering its physical implementation, posing a challenge to hardware integrity verification. Spatio-frequency polarization fingerp…
Representation LearningAnomaly DetectionPSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and limited semantic prompts. We propose PSMP-CLIP, integrating patch-prom…
Anomaly DetectionLayerwise Tunable Lifting Scheme for the Convolutional Neural Network
This work introduces a family of tunable lifting schemes for biorthogonal wavelet filter banks. We propose three lifting strategies: low-pass tuning (LS-LayLatt-LP), high-pass tuning (LS-LayLatt-HP), and a sequential lif…
Image ClassificationAnomaly DetectionReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates …
Computational EfficiencyAnomaly DetectionGLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection
We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain transferability through graph-language alignment on the unit hypersphere. GLASS builds a unified representation s…
Density EstimationAnomaly DetectionTraining-Free Logical and Structural Anomaly Detection via Calibrated Fusion
Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as local texture corruptions, and logical anomalies, which violate global rules on object count, composition, or…
Anomaly DetectionLUMIN: Lightweight Universal Manufacturing Inspection Network for Anomaly Detection
Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference efficiency. Traditional sampling algorithms (Farthest Point Sampling, K-Means, etc.) rely on numerous backbon…
Anomaly DetectionAnomaly Detection on Small Industrial Components via Vision-Based Tactile Sensing
Automated inspection of small industrial components, including sub-centimetre-scale parts where defects are geometry-driven and poorly resolved by standard optical cameras, calls for sensing modalities that can directly …
Anomaly DetectionLocalizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection
Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observations drive the departure. We develop a framework for this localization…
Anomaly DetectionTraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attribution
LLM agents are increasingly applied to anomaly detection and root-cause analysis in time-series observations collected from real-world systems; however, their performance on these tasks has not been systematically evalua…
Anomaly DetectionGeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment
Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is to fuse views with sufficient geometric awareness while remaining sca…
Anomaly DetectionRobustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data is trustworthy. In practice, training data may be corrupted through c…
Anomaly DetectionWhat's the Catch? Evaluating Temporal Consistency in Vision-Language Models
Vision-language models (VLMs) achieve strong performance on video and image-sequence benchmarks, yet it remains unclear whether they capture temporal structure. To study this question, we formulate temporal grounding as …
Anomaly DetectionRAD: Rule-Augmented Relational Anomaly Detection
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity identity, schema st…
Graph Representation LearningAnomaly DetectionWhat Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference without a separate check that geometric rarit…
Anomaly DetectionWhat Memory Composition Does Not Tell Us About Anomaly Detection
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference without a separate check that geometric rarit…
Anomaly DetectionQuality Inspection of Printed Circuit Board Pin Insertion via Semantic Segmentation and Board-Level Feature Extraction
Quality control during printed circuit board (PCB) assembly is a critical step in ensuring reliable electronic products. Detecting misaligned pins during or after pin insertion remains a particularly challenging inspecti…
Instance SegmentationSemantic SegmentationAnomaly DetectionGuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing
Additive Manufacturing (AM) plays a vital role in the ongoing industrial revolution. However, quality control remains crucial and challenging due to printing defects or potential cyber-physical intrusions. Image or video…
Anomaly Detection