arXiv AI
Jul 28
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set.
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Hugging Face Trending Papers
Aug 10
Beyond Hazard Resemblance: Contrastive Event Adjudication for Training-Free Video Anomaly Detection
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data.
Hugging Face Trending Papers
Jul 27
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.
arXiv Computer Vision
Sep 4
PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection
The paper introduces PL‑SCEA, a method that reconfigures the attention mechanism of frozen Vision Foundation Models to better detect and localize anomalies in industrial images with few training examples. PL‑SCEA preserves the semantic context of pretrained query‑key attention while adding token‑adaptive self‑correlations over contextualized value features, then applies positive‑correlation filtering and power‑law reweighting to highlight task‑relevant relationships. The resulting features are fed into a lightweight variational autoencoder to produce reconstruction‑based anomaly scores, achieving competitive image‑level detection and strong pixel‑level localization on MVTec AD and VisA datasets.
arXiv Computer Vision
Sep 7
Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion
The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.
arXiv Machine Learning
4d ago
TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks
arXiv:2609.36968v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods...
arXiv AI
Jun 2
A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision
arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.
arXiv AI
Sep 3
Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework
The paper introduces a new task called Quality Anomaly Perception for UGC Image Enhancement (UEAP) and presents the first benchmark dataset, UEAP-4k, featuring fine‑grained annotations of anomaly categories, locations, and severity levels in real‑world user‑generated content. It proposes the Difference‑Fusion Anomaly Perception Method (DFAP‑UGC), which fuses explicit differences between enhanced images and their references using dense spatial querying, regional verification, and quality‑aware ranking to robustly identify localized anomalies. A Locality‑Aware Dynamic Task Prioritization (LADTP) training strategy is also introduced to enable efficient end‑to‑end learning without multi‑stage overhead, and experiments demonstrate that DFAP‑UGC outperforms adapted classical baselines.
arXiv Computer Vision
Sep 3
DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation
The paper introduces DPA, a diffusion-based framework that decouples product-agnostic anomaly representations to enable zero-shot anomaly generation. By reusing real anomalies from existing source products and filtering them for plausibility, DPA learns product-irrelevant anomaly embeddings that can be transferred across products. An adaptive mask-guided pipeline and a training-free labeling module further refine the realism and localization of generated anomalies, leading to improved performance on MVTec-AD, VisA, and a new anomaly-transfer benchmark.
arXiv Machine Learning
Jul 27
Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection
arXiv:2607. 22212v1 Announce Type: cross Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced.
arXiv Computer Vision
Aug 27
See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection
The paper identifies a problem in multi‑view anomaly detection called cross‑view information leakage, where fusing multiple inspection views can cause normal features to mask anomalies during reconstruction. To address this, the authors propose GLAD, a framework that uses a Global‑Local Attention Driven approach, combining vision foundation model features with two fusion modules: Multi‑view Merging Attention for local, weighted fusion and Object‑Guided Attention for global context aggregation. Experiments on Real‑IAD and MANTA‑Tiny demonstrate that GLAD outperforms existing methods across various metrics, underscoring the importance of restricting information flow to preserve the reconstruction gap.
arXiv AI
Aug 11
Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection
arXiv:2608. 00442v2 Announce Type: replace-cross Abstract: Medical anomaly detection identifies abnormal images and localizes lesions under scarce supervision while generalizing across organs and modalities.