ChengYuQi99/M2AD

Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark

🌐 Hugging Face Dataset

📚 Paper • 🏠 Homepage
by Yunkang Cao*, Yuqi Cheng*, Xiaohao Xu, Yiheng Zhang, Yihan Sun, Yuxiang Tan, Yuxin Zhang, Weiming Shen,

🚀 Updates

We're committed to open science! Here's our progress:

• 2025/05/19: 📄 Paper released on ArXiv.
• 2025/05/16: 🌐 Dataset homepage launched.
• 2025/05/24: 🧪 Code release for benchmark evaluation! code

📊 Introduction

Visual Anomaly Detection (VAD) systems often fail in the real world due to sensitivity to viewpoint-illumination interplay—complex interactions that distort defect visibility. Existing benchmarks overlook this challenge.

Introducing M2AD (Multi-View Multi-Illumination Anomaly Detection), a large-scale benchmark designed to rigorously test VAD robustness under these conditions:

• 119,880 high-resolution images across 10 categories, 999 specimens, 12 views, and 10 illuminations (120 configurations).
• Two evaluation protocols:
  • 🔄 M2AD-Synergy: Tests multi-configuration information fusion.
  • 🧪 M2AD-Invariant: Measures single-image robustness to view-illumination variations.
• Key finding: SOTA VAD methods struggle significantly on M2AD, highlighting the critical need for robust solutions.