Is In-Domain Training Enough for Fine-Grained Industrial Anomaly Understanding?

arXiv:2610.12310 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Accuracy — 38× in this paper · explained in 1 episode
• Calibration — 29× in this paper · explained in 7 episodes
• Aggregation — 17× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Grounding — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• GRPO — 2× in this paper · explained in 6 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Abstention — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Approaches — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Reasoning — 1× in this paper · explained in 2 episodes
• Memory Augmentation — 1× in this paper · explained in 2 episodes

Sources

• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
• Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• GPT-4o System Card
• AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
• JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
• LLaVA-OneVision: Easy Visual Task Transfer
• Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
• AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation
• EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models
• DINOv3
• OpenAI GPT-5 System Card
• MiMo-VL Technical Report
• OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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