TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

arXiv:2609.39033 · cs.CV, cs.AI · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• AUC — 61× in this paper · explained in 2 episodes
• Calibration — 22× in this paper · explained in 7 episodes
• Adaptation — 11× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Aggregation — 5× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Entanglement — 4× in this paper · explained in 3 episodes
• Normalizing Flow — 4× in this paper · explained in 3 episodes
• Text-Axis Evidence Decomposition — 4× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
• Sub-Image Anomaly Detection with Deep Pyramid Correspondences
• AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
• MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
• GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
• AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
• VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
• Student-Teacher Feature Pyramid Matching for Anomaly Detection
• FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
• OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
• DeepViT: Towards Deeper Vision Transformer
• AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

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