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license: apache-2.0 |
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# Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark |
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🌐 [Hugging Face Dataset](https://huggingface.co/datasets/ChengYuQi99/M2AD) |
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> 📚 [**Paper**](https://arxiv.org/abs/2505.10996) • 🏠 [**Homepage**](https://hustcyq.github.io/M2AD/) |
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> by [Yunkang Cao*](https://caoyunkang.github.io/), [Yuqi Cheng*](https://hustcyq.github.io/), [Xiaohao Xu](), [Yiheng Zhang](), [Yihan Sun](), [Yuxiang Tan](), [Yuxin Zhang](), [Weiming Shen](https://scholar.google.com/citations?user=FuSHsx4AAAAJ&hl=en), |
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## 🚀 Updates |
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We're committed to open science! Here's our progress: |
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* **2025/05/19**: 📄 Paper released on [ArXiv](https://arxiv.org/abs/2505.10996). |
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* **2025/05/16**: 🌐 Dataset homepage launched. |
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* **2025/05/24**: 🧪 Code release for benchmark evaluation! [code](https://github.com/hustCYQ/M2AD) |
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## 📊 Introduction |
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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. |
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Introducing **M2AD** (Multi-View Multi-Illumination Anomaly Detection), a large-scale benchmark designed to rigorously test VAD robustness under these conditions: |
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- **119,880 high-resolution images** across 10 categories, 999 specimens, 12 views, and 10 illuminations (120 configurations). |
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- **Two evaluation protocols**: |
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- 🔄 **M2AD-Synergy**: Tests multi-configuration information fusion. |
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- 🧪 **M2AD-Invariant**: Measures single-image robustness to view-illumination variations. |
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- Key finding: SOTA VAD methods struggle significantly on M2AD, highlighting the critical need for robust solutions. |
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