In modern high-throughput industrial production lines, product configurations and visual characteristics frequently change, making it impractical to collect and annotate data for every new scenario. This dynamic setting makes Zero-Shot Anomaly Detection (ZSAD) particularly suitable, as it enables defect detection without requiring training on target-specific samples.
arXiv:2604. 06435v2 Announce Type: replace-cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare.
arXiv:2605. 24251v2 Announce Type: replace Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints.
arXiv:2608.23723v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual f...
AT3D-AD introduces a unified framework for detecting, localizing, and classifying 3D point‑cloud anomalies. It uses a Physics‑Driven Parametric Anomaly Synthesis module to generate synthetic defects for explicit supervision, a Hierarchical Global‑Local Anomaly Alignment module to refine representations, and a Semantic‑Geometric Anomaly Classification module to achieve precise, type‑discriminative localization. The method sets new state‑of‑the‑art results on four benchmarks, achieving high AUROC and Macro‑F1 scores.
The paper introduces LUMIN, a lightweight network for manufacturing anomaly detection, and PSP, a four‑stage adaptive memory bank sampling pipeline that eliminates backbone forward passes. PSP achieves near‑random construction speed, 341× faster than FPS, while parallel similarity computation and stratified pixel sampling cut inference time by 20× with minimal accuracy loss. Experiments on five benchmarks show that LUMIN and PSP reach state‑of‑the‑art sampling accuracy with dramatically reduced latency and memory usage.
Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies.
Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory methods still use the memory bank as a nearest-neighbor lookup table: a test patch is treated as normal if it has one nearby normal anchor.
arXiv:2606. 29181v1 Announce Type: cross Abstract: Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data.
arXiv:2609.14722v1 Announce Type: new Abstract: Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data...
The paper introduces a lightweight full-frame detector for partially manipulated AI-generated videos, suitable for edge deployment without face-detection preprocessing. It distills a DINOv2-Base teacher into a frozen MobileNetV3-Small student using temperature-annealed soft-label transfer, attention-diversity regularization, frame-level supervision, and a residual feature adapter. The model addresses false positives on legitimate scene cuts and threshold-level miscalibration, achieving an AUC of 0.766 on a 55,393-sample spliced test set while running at 3.65 ms per 16‑frame clip with a 150.4 MB checkpoint.
The paper introduces NC‑TFAD, a task‑free continual anomaly detection framework that leverages neural‑collapse geometry to learn from non‑stationary data streams without task boundaries. It freezes a pretrained backbone, aligns streaming features to a simplex Equiangular Tight Frame prototype space, and uses synthetic anomaly anchors, inter‑ and intra‑class regularization, and a Focal Neural Collapse Contrastive loss to stabilize representations and enhance normal‑anomaly separability. A normal‑patch‑prototype‑guided localization branch generates calibrated anomaly heatmaps, and extensive experiments on MVTec AD and VisA demonstrate that NC‑TFAD outperforms existing task‑free continual learning and unified anomaly detection baselines in both image‑level detection and pixel‑level localization.
The paper introduces a lightweight two‑stage system for real‑time video anomaly detection. First, YOLO v11n‑pose detects people and extracts seventeen skeletal keypoints in a single forward pass. Second, each cropped person region is encoded with CLIP ViT‑B/32 and compared via cosine similarity to predefined textual descriptions of anomalous behaviors, removing the need for optical flow, separate pose estimators, or density‑based scoring. Experiments on CUHK Avenue, ShanghaiTech Campus, and a custom indoor dataset achieve about 51 FPS on an NVIDIA Titan XP, a 3.36× speedup over a multi‑feature baseline, while preserving high frame‑level AUROC scores (89.26%, 70.26%, and 84.13%).