ChengYuQi99/M2AD
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark 🌐 Hugging Face Dataset 📚 Paper • 🏠 Homepageby 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… See the full description on the dataset page: https://huggingface.co/datasets/ChengYuQi99/M2AD.
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
📚 **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.