🔍 Anomaly Detection¶

🎞️ ECCV2026 · 8 paper notes

📌 Same area in other venues: 📷 CVPR2026 (7) · 🔬 ICLR2026 (10)

🔥 Top topics: Anomaly Detection ×5

Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks: Pistachio uses scene-constrained storylines to drive continuous video generation for a synthetic detection-and-understanding benchmark, exposing difficulties with complex normal behavior and long-term understanding while showing that CLIP-TSA trained with synthetic data and 10% of UCF-Crime training data outperforms training on the complete UCF-Crime training set alone.
Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection: AF3AD is a standalone geometric pseudo-anomaly generator that combines local coordinates, spatial kernels, directional gating, and stochastic presets; with a modified offset-prediction detector, it reaches object-level AUROC of 91.5% on AnomalyShapeNet and 85.2% on Real3D-AD.
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution: DeltaDeno drives two synchronized diffusion trajectories with normal and anomaly prompts, accumulates post-denoising latent differences into a defect localization map, and uses that map to constrain local generation, increasing downstream MVTec AD image-level AUROC from AnomalyAny's 77.6 to 84.7 without generator fine-tuning or real anomalous samples.
IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion: IMMoE uses local feature dropout to reduce overfitting to masking patterns and view-expert fusion to guide normal-feature reconstruction, achieving 84.7 I-AUROC / 33.1 P-AP on RIMAD with 50% of the product area masked, without fully establishing the recoverability of hidden defects under real occlusion.
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection: ArcAD calibrates the latent space of a reconstruction-based industrial anomaly detector using balanced normal prototypes, prototype-filtered synthetic anomalies, and a few real defects, raising Dinomaly's image-level AUROC from 88.8% to 92.5% on multi-class cold-start Real-IAD while retaining its original reconstruction-based inference score.
BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection: BAAF cleans contaminated training images through disjoint bags, out-of-bag anomaly scoring, and majority voting before retraining one unchanged one-class detector, raising average image AUROC across eight detectors from 0.762 to 0.933 on MVTec AD with 10% training contamination without adding test-time models.
A Comprehensive Analysis about Unsupervised Outlier Detection for Images: VUOD obtains reliable inlier/outlier pseudo-labels through fine-grained clustering and distance-ranking agreement, learns a lightweight feature mapping, and applies OCSVM to the improved representation, raising aggregated CIFAR-10 AUROC from FlexUOD's 0.942 to 0.972 while extending the same framework to industrial and medical images.
Beyond Common Sense: Grounding Logical Anomaly Detection in Inspection Criteria: SCAN turns industrial logical anomaly detection into explicit rule-by-rule visual inspection, combining FLAW training data, structured supervised fine-tuning, and rule-level reinforcement learning to raise an 8B model's strict scoring accuracy on MVTec-LOCO from 56.73% to 64.58%, while leaving substantial missed-detection concerns.