Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection

arXiv:2510.17611 · cs.CV · Submitted 2025-10-20 · Read on arXiv

cs.CV

Submitted: 2025-10-20

Updated: 2026-09-26

Code: https://github.com/guojiajeremy/Dinomaly

Terminology

• Transformer — 28× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Linear Attention — 12× in this paper
• Ablation — 8× in this paper · explained in 1 episode
• Generalization — 8× in this paper · explained in 13 episodes
• Softmax Attention — 8× in this paper
• Loose Constraint — 7× in this paper
• Unified Framework — 7× in this paper · explained in 5 episodes
• Context-Aware Recentering — 6× in this paper
• Distillation — 6× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Meta-learning — 5× in this paper · explained in 2 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Loose Loss — 4× in this paper
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Point Cloud Data — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Gradient Flow — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Area Under the Per-Region-Overlap — 1× in this paper
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Average Precision — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vector Quantization (VQ) — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
• DINOv3
• Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain
• Distilling the Knowledge in a Neural Network
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
• Learning Multi-view Anomaly Detection with Efficient Adaptive Selection
• CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
• BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

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