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 Detection

Anomaly Detection and Localization for the Pantograph-Catenary System

2026-10-01 · Francesco Vitale, Hangli Ge, Francesco Flammini arxiv

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 Detection

FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection

2026-09-15 · Xiaoxuan Huang, Jinlong Xu, YiZhe Wang, Meng Zhang 외 arxiv

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 Learning Anomaly Detection

PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection

2026-09-15 · Xuezhi Xiang, Guanghao Wu, Heqi Xiang, Jiayao Liu 외 arxiv

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 Detection

Layerwise Tunable Lifting Scheme for the Convolutional Neural Network

2026-09-09 · Abdumannon Yovkochov, An Le, Sungbal Seo, You-Suk Bae 외 arxiv

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 Classification Anomaly Detection

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

2026-09-07 · Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang, Min-Hung Chen 외 arxiv

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 Efficiency Anomaly Detection

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