HiMatch-AD proposes DINOv3-driven hierarchical matching with uncertainty-based fusion for training-free medical anomaly detection and reports outperformance on the BMAD benchmark.
Student-teacher feature pyra- mid matching for anomaly detection
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IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
A single model trained on one normal sample per dataset from nine heterogeneous medical sources achieves state-of-the-art anomaly detection in one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized settings.
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
Benchmarking shows VAD methods transfer to autonomous driving scenes, with Tiny-Dinomaly providing the strongest accuracy-efficiency balance for edge hardware.
citing papers explorer
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HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection
HiMatch-AD proposes DINOv3-driven hierarchical matching with uncertainty-based fusion for training-free medical anomaly detection and reports outperformance on the BMAD benchmark.
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IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation
IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
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Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection
A single model trained on one normal sample per dataset from nine heterogeneous medical sources achieves state-of-the-art anomaly detection in one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized settings.
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Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
AAND is a two-stage anomaly detection method that advances a pre-trained teacher via residual anomaly amplification and applies hard knowledge distillation in reverse distillation to achieve SOTA results on MVTecAD, VisA, and MVTec3D-RGB.
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MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
MambaADv2 evolves Mamba state space models with hybrid blocks, frequency convolutions, and adaptive scanning for improved unsupervised anomaly detection.
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AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving
Benchmarking shows VAD methods transfer to autonomous driving scenes, with Tiny-Dinomaly providing the strongest accuracy-efficiency balance for edge hardware.